What is a Data Engineer?

A data engineer is responsible for building, managing, and maintaining the systems that collect, store, and transform big data. Without these systems, anyone in a company may find themselves challenged with retrieving data to gain meaningful insights. That’s why a data engineer is a very essential part of a company. They work in the background to ensure data flows smoothly from sources (in raw formats) to destinations (in usable formats for analysis).  

Key Responsibilities

Each company can assign slightly distinct responsibilities to data engineers. This depends on the projects they participate in. But generally, their duties revolve around the following tasks:

• Developing Data Architecture: Data engineers create blueprints for data management systems that align with a company’s particular requirements. This often involves choosing the right technologies or tools to process data.

• Creating Data Pipelines: They build reliable pipelines (normally, ETL/ELT) to automate data collection and processing tasks. These pipelines are automated processes that help gather data from different sources, clean it, and make it ready for analysis.

• Optimizing Data Storage: They’re in charge of developing effective storage solutions. This helps store data in a way that increases data extraction speeds while reducing costs. 

• Maintaining Data Quality and Security: They ensure data is always accurate, consistent, and complete. They have to, therefore, implement monitoring systems and run constant audits to identify and rectify issues. In addition, they must adopt security solutions (e.g., encryption) to protect information from breaches and unauthorized users. 

• Tracking System Performance: They constantly monitor how data systems operate. This helps them make necessary improvements to ensure these systems always work effectively.

• Working with Other Teams: Data engineers have to work hand in hand with data analysts, scientists, and other stakeholders. This enables engineers to understand data requirements and create systems that address the requirements at hand.

Required Skills & Experience

Similarly, each company may have different requirements for a data engineer’s skills and experience in their job descriptions. However, an engineer is often required to have common skills. They often cover technical knowledge, soft skills, and hands-on experience.

• Expertise in Programming Languages: Data engineers need to master programming languages (e.g., Python, SQL, Java, or Scala). These languages help them manipulate and manage data efficiently.

• Familiarity With Big Data Technologies: They must be experienced in using big data technologies (e.g., Hadoop, Spark, or Kafka). These technologies assist them in processing vast amounts of data effectively.

• Cloud Solutions: Presently, most organizations are migrating towards the cloud to save costs and make access easier. So, experience with cloud platforms (AWS, Google Cloud, Azure, etc.) is a bonus. 

• Database Management Skills: Data engineers need to be skilled in monitoring database systems, whether SQL or NoSQL databases. Understanding of database management helps them organize, store, and retrieve data effectively.

• Knowledge of Data Warehouses: They must be familiar with data warehousing technologies (such as Snowflake or Amazon Redshift). These techs are essential to develop large-scale, scalable data storage solutions. 

• Soft Skills: Data engineers need strong abilities in problem-solving and communication. Why? First, they need robust analytical and critical thinking skills to resolve data-related problems effectively. Moreover, they work closely with other teams and relevant stakeholders. So, they must communicate technical findings well for everyone to understand.

• Educational Background: Some companies require candidates to possess at least a bachelor of science degree in computer science, engineering, applied math, or any data-related field. Other advanced roles may require advanced or additional certifications. However, there are still recruiters who just focus on the skills or experience of candidates.

Example: Designveloper’s Data Engineer Job Description

To help you better understand how a data engineer job description may look like in reality, let’s take a look at Designveloper’s example:

Here, we’re recruiting excellent data engineers for our Lumin project. In the job description, we detailed the role, the Lumin project, and core responsibilities if a candidate has an opportunity to work with us. We also highlighted the essential skills and experience we need from the candidate. 

So, are you interested in this Data Engineer position in Designveloper? Send us your CV and portfolio now!

FAQs

Below are some common questions candidates may ask when learning about the data engineering position. If you want to pursue this career, don’t overlook these questions.

Question 1: Is Data Engineering an In-Demand Job?

The answer is yes. According to the WEF (World Economic Forum) Report released in January 2025, data engineering is one of the most in-demand roles, especially in the IT Services industry. In particular, this profession records a global net growth of 32% from 2025 to 2030. There are many reasons behind the increasing demand for this position:

Booming of Big Data

Companies now can generate data from any source. Especially with the rise of the IoT (Internet of Things) technology like sensors and wearable devices, they can collect large volumes of data in real-time. To store, manage, and process such big data effectively, there’s a growing demand for data engineers. 

Need for Data-Driven Decisions in Real Time

Companies across all fields are increasingly depending on data to make informed decisions. Be it to optimize operations, boost customer experiences, or forecast demands, data always plays a core role in strategic decisions. However, without data engineers, data can’t be processed, accessed, and ready for analysis. 

Moreover, many companies now require real-time data processing to address specific tasks timely (e.g., suggesting personalized treatment plans). Data engineers play a key role in developing systems that can handle data in real-time. 

Shift Towards Cloud Technologies 

There’s a growing adoption of cloud platforms like AWS, Azure, or Google Cloud. This has propelled an increasing need for skilled data engineers who can build, deploy, and maintain data systems in cloud environments.

As companies continue to depend on data to improve their strategies, the demand for data engineers remains high. This provides good job opportunities to those with the right expertise and skills. 

Question 2: Does a Data Engineer Pay Well?

Data engineering is one of the most lucrative roles. In the United States, a data engineer can earn an average base salary of $125,345 per year. According to our previous article, this position even pays higher than data analysts and data scientists. 

Of course, this salary figure can vary depending on different factors. 

• Experience Level: Like other roles, the earning of a data engineer increases with experience. Those with less than one year of experience can earn an average of $97,540 a year, while those with more than seven years of experience often make six-figure salaries. 

• Industry and Location: The industry and geographical location also affect the data engineer’s salary. Sectors that are data-intensive, such as tech services, finance, or healthcare, tend to pay higher compensation. Likewise, tech centers like Silicon Valley, Seattle, or New York generally offer higher pay.

• Role’s Complexity Level: Depending on the technical complexity and important nature of their work, a data engineer’s salary may vary differently across companies. For example, Meta can offer a base salary of $145,000 – $204,000 annually for data engineers in product analytics. 

• Skills: Excelling at some technologies and tools (e.g., Hadoop, big data analytics, or cloud platforms such as AWS) can command higher salaries. Further, specific certifications or training in these techs can improve earning potential.

• Educational Background: A bachelor’s degree in computer science, computer engineering or a related field is sufficient to get you in a data engineering position, as already stated above. However, you can negotiate for higher salaries if you have graduate degrees or additional certifications.

Besides base salaries, data engineers often receive bonuses, equity options, and other benefits (e.g., health insurance).

Question 3: Can I Become a Data Engineer Without Coding?

Without coding knowledge, it’s tricky for you to become a data engineer. It’s because this position often requires a strong foundation in programming languages (e.g., Python or SQL). These coding languages help you write scripts to extract, convert, and load data effectively from different sources to a centralized platform.  

Even other data engineering tasks like data cleaning and database management require your expertise in coding. For instance, you need to write custom scripts to tackle particular data issues or formats, hence maintaining data quality. Plus, due to vast volumes of data, a data engineer must depend on scripting languages to develop automated solutions that can process data efficiently without human intervention.

Due to the nature of data engineering work, you can hardly become a data engineer without coding. However, what if you still want to pursue this career with limited coding skills? In this case, we advise you to begin in similar roles that don’t require much coding knowledge, typically data analysts. Then, you can gain exposure to data engineering concepts and gradually learn coding through online resources (e.g. courses or boot camps). 

Question 4: Where Should I Start to Become a Data Engineer?

If you want to become a data engineer from scratch, below are some steps you can take to embrace this journey:

1. Understand the Role of a Data Engineer

Start by learning about the real-life responsibilities of a data engineer and possibly their projects. This helps you understand real-life demands and expectations for this position, which is helpful for setting up realistic goals.

2. Build Essential Knowledge and Skills

This step lays the foundation for your journey toward becoming a data engineer. You need to build your knowledge of programming languages, cloud platforms, big data technologies (e.g., Hadoop or Spark), and data warehousing or data lakes. 

To build such knowledge and practical skills, you can enroll in online courses, boot camps, or formal educational programs. Such learning platforms as Udemy, edX, or LinkedIn provide a variety of data engineering courses. You can consider specialized certifications from tech giants like AWS Certified Data Engineer or Google’s Professional Data Engineer. 

3. Participate in Hands-On Projects

Next, work on practical projects to apply your knowledge and gain hands-on experience. 

This can involve developing personal projects that revolve around data manipulation and analysis, or contributing to open-source projects to learn from experienced fellows. These projects help enhance your portfolio, prove your skills to potential employers, and build your reputation in communities. 

Further, you can look for volunteer or internship opportunities in companies that often use data. This will give you practical experience and excellent opportunities to network with specialists in the field.

4. Never Stop Learning

The data engineering sector is continuously evolving. Therefore, stay updated with the latest technologies and trends by following industry leaders on social media, attending workshops or conferences, and reading blogs. Also, you can seek a mentor who may offer precious advice and guide you through your career journey.

Conclusion

Becoming a data engineer requires technical skills, practical experience, and a strong understanding of the data ecosystem. Further, candidates need to be dedicated and continuously learn to be successful on this career path. Through this article, you may better understand what a company’s data engineer job description may look like, coupled with key duties, skills, and salaries. For more interesting topics about data engineering, don’t forget to follow us on Facebook, X, and LinkedIn!

The post Data Engineer Job Description: Key Duties, Skills & Wages appeared first on Designveloper.

What is a Data Analyst?

A data analyst is a specialist who gathers, processes, and analyzes data. Typically, they need to collaborate with relevant stakeholders to translate specific needs and figure out what data is needed to respond to these requests. In doing so, they must examine historical data for patterns, and gather actionable insights from that data which will be conveyed to the stakeholders in visual forms (e.g., graphs or charts).

Key Roles

• Data Collection: Data analysts gather relevant data from multiple sources (spreadsheets, databases, and external datasets) to resolve a certain task.
• Cleaning: They clean data by handling incomplete, inaccurate, and inconsistent values.
• Analysis: They use various analytical methods (e.g., descriptive analysis or content analysis) as well as statistical tools (e.g., Power BI) to identify repeating patterns, trends, and correlations in the data.
• Data Visualization: They create visual reports and interactive dashboards to showcase insights and findings from the data. 
• Data Visualization: They present meaningful insights and findings from data by creating visual reports and interactive dashboards.
• Decision Support: By communicating data, they offer evidence-based recommendations to help enhance overall decision-making and business operations.

Recommended reading: 10 Best Data Analytics Tools and Techniques in 2025

Key Skills

• Proficiency in Analytical Techniques and Tools: Data analysts need to be adept at necessary techniques and tools (like Excel or Tableau) to perform data analytics effectively. 
• Statistical Knowledge: They need a solid understanding of statistics (e.g., different types of data, variability, or measures of central tendency) to analyze and explain data precisely.
• Understanding of Programming Languages: They need to know programming languages like Python or SQL. These languages help automate repetitive tasks (e.g., writing scripts to retrieve data from different sources) and perform advanced analysis. 
• Critical Thinking: They need to explain data critically and offer meaningful insights.
• Communication: They need to communicate insights and complex findings clearly so that non-technical stakeholders can understand and make informed decisions.

Salary

The salary of a data analyst varies depending on his/her years of experience, working locations, and more. But data analysts in the United States often receive an average annual income of $80,898 with a cash bonus of $2,000. Further, Energy, Mining & Utilities and Financial Services are two top-paying industries for this position, with a median total pay of $111,000. 

What is a Data Scientist?

A data scientist is a professional who employs advanced analytics, statistical methods, and machine learning to extract patterns from structured and unstructured data. Not only are they not limited to obtaining actionable insights into what matters now. But they also leverage past data to build and train ML models that predict future demands or automate decision-making.

For example, suppose you have an eCommerce store, and you hire a data scientist to build a recommendation engine that suggests products to the customers on your website based on what they have purchased before.

Key Roles

• Data Exploration: Like data analysts, data scientists clean and process large datasets. They then combine statistical analysis and machine learning algorithms (e.g., decision trees, clustering, or neural networks) to spot patterns and insights from data. 
• Model Development: They develop predictive models and algorithms to handle specific data tasks. For example, data scientists at Boston Children’s Hospital created DRAMMS group-wise registration algorithms to combine all brain scans of different patients into a single atlas for medical imaging purposes. These models and algorithms help automate decision-making processes and enhance accuracy.
• Data Communication: They interpret and communicate findings from models to improve business strategies.

Recommended reading: 10 Real-World Case Studies of Implementing AI in Healthcare

Key Skills

• Programming Languages: Data scientists need to be proficient in programming languages, typically Python, SQL, and R.
• Big Data Technologies: They need to be familiar with using big data technologies (e.g., Spark, Hadoop, NoSQL databases like MongoDB, and cloud platforms like AWS). These tools help them process even exabytes of data to conduct sophisticated analyses and create complex models. 
• Machine Learning: They need deep expertise in machine learning techniques (e.g., Scikit-learn or TensorFlow) and advanced mathematics. Such knowledge helps them build reliable algorithms and models. 
• Data Mining, Conditioning & Optimization: Data mining helps scientists uncover hidden relationships or trends in vast datasets. Conditioning refers to cleaning and converting data for analysis. Meanwhile, optimization involves using techniques like data compression or partitioning to optimize data processing tasks. These skills are essential to harness necessary data for demand forecasting, fraud detection, and other complex tasks.
• API Development: Building APIs to help data scientists connect their models smoothly with other software systems.
• Critical Thinking & Problem-Solving: Data scientists need these soft skills to tackle business issues using data-driven methods.

Salary

With an average annual salary of $123,141 in the US, data scientists have higher earnings than data analysts. Today, the highest-paying industries for this position are Financial Services and Information Technology, with a total annual pay of $148,000. However, other factors like experience and geographical location can affect your total pay. 

What is a Data Engineer?

A data engineer is mainly in charge of developing programs that collect data reliably and effectively for usage and analysis. Such programs often include architecture and data pipelines. The architecture is often about databases or large-scale processing systems that can support vast datasets. Meanwhile, data pipelines are automated processes of delivering data from different sources to destinations for analysis.

Key Roles

• Data Pipeline Construction: Data engineers build, schedule, and monitor data pipelines to ensure they operate efficiently. In other words, engineers must use tools like Apache Airflow, AWS Step Functions, or Luigi to help data be processed in the accurate order and flow smoothly across systems without errors. 
• Architecture Maintenance: They develop data architecture, or platforms, that include specific technologies like databases (e.g., MySQL, PostgreSQL), cloud storage services (e.g., AWS S3), or big data processing frameworks (e.g., Apache Spark). They need to regularly monitor, maintain, and troubleshoot the architecture to help data scientists and analysts do their jobs effectively.
• Data Warehouse Management: They apply techniques (e.g., query optimization or indexing) to ensure the optimal performance of data warehouses, data lakes, or data marts. In other words, they need to ensure that data warehouses can process large data volumes effectively and user queries run quickly without delays. 
• Data Integration, Generation & Management: Data engineers are responsible for designing custom solutions to integrate data from various sources. Plus, they often generate real-time or even synthetic data for testing and analysis purposes. Moreover, they’re in charge of creating guidelines for everyone in an organization to store, access, and use data securely. 

Key Skills

• Data Manipulation: Data engineers should have the skills to manipulate structured and unstructured data. Specifically, they must handle different data types and formats and integrate such data smoothly into data warehouses. In addition, they need to design data systems for other users to store and access such data securely.
• Experience with Tools: They should know how to use databases (MySQL), data warehouses (Google BigQuery or Amazon Redshift), and data frameworks (Hadoop or Apache Spark). Further, they must be good at using testing frameworks and CI/CD tools.
• Programming and Algorithms: They need to be skilled in languages like Python, Java, or Scala to write scripts or applications that handle massive datasets effectively. They also need to develop algorithms for tasks like data sorting and implement strong data pipelines to process data at scale.
• SQL: Data engineers need deep expertise in SQL to build and monitor ETL (Extract, Transform, Load) pipelines. Also, this language helps them manipulate data effectively and optimize database performance. 
• Problem-Solving and Creative Thinking: Data engineers need these soft skills to address complex problems effectively.

Salary

Among the three positions, the annual income of data engineers is highest with $125,345 in the United States. Naturally, the figures also differ based on factors such as an engineer’s experience or geographical location. Further, Arts, Entertainment & Recreation is the top paying industry for this position, with a medium total pay of $147,000. 

Data Analyst vs Data Scientist vs Data Engineer: A Comparison Table

To help you better envision the differences between data analysts, data scientists, and data engineers, we created the following comparison table:

Which Job Should You Choose?

Data has been booming in recent years, resulting in the significant growth of data-related jobs. According to the US Bureau of Labor Statistics, the job openings for data scientists are predicted to increase by 36% from 2023 to 2033. This translates to roughly 20,800 openings annually over the given period. 

For those looking for jobs associated with data, this is a truly good sign. However, among the three jobs we mentioned, which one should you choose? Data analysts, data scientists, or data engineers? This depends on many factors:

• Interests: Are you more interested in working closely with business teams or addressing technical challenges?
• Background: If you have no technical background or fundamental knowledge about data, starting as a data analyst is often the most accessible option. It’s because this position requires less technical expertise and a shorter learning curve than data scientists and engineers. 
• Career Aspirations: Where do you want yourself to stand in the future? Do you prefer developing the backbone of data infrastructure, devising data-driven strategies, or building predictive models? 

Only when you understand the main differences and requirements of each job, can you make a wise decision that fits your strengths and career goals.

Job Opening: Be Our Colleague!

Through this article, we expect you to have a better understanding of how a data analyst, data scientist, and data engineer differ. The choice for your future job depends greatly on your interests, background, and even career ambitions. 

You already have two or more years of practical experience working with big data infrastructure and are looking for a data engineering position. Why don’t you join us? We at Designveloper are opening a lucrative position for our Lumin team. Lumin is a document editor that allows users to edit, send, and sign PDFs easily and effectively. Since its first launch, Lumin has boasted more than a million users globally. 
If you want to become a part of our Lumin team, drop us your CV or portfolio! Read more about our data engineering position here!

The post Data Analyst vs Data Scientist vs Data Engineer: A Detailed Comparison appeared first on Designveloper.

Innovations in cloud computing over the last several years have enabled almost limitless data storage, very quick data processing, and robust analytics capabilities. Because of this, businesses have access to additional data sets, which they can use to better understand their consumers, products, and industry overall. Your business’s success depends on your ability to identify and effectively use the most appropriate cloud technology.

What Are the Advantages of Big Data?

Modern businesses depend on big data to provide formerly unfathomable insights. If more individuals join the digital economy, we can get a clearer view of the world. Improved decision-making results from a more thorough comprehension.

The amount of big data is likewise continuously increasing. According to scientific estimates, humans produce 2.5 quintillion bytes of data daily. This Data encompasses all possible recordings, including transcripts of conversations, uploaded videos, usage metrics, downloads, swipes, and any other capturable activity. Big data also includes unstructured data, including images, text files, voice recordings, and more.

Various types of data show what people want and need. Finding this evidence will give businesses an advantage over their competitors by allowing them to make better judgments. Big data entails mining data for insights into untapped markets, inefficient processes, possible dangers to operations, and exciting new growth opportunities.

Banks can avoid the spread of fraud by routinely monitoring millions of accounts using big data analytics. Medical professionals may spot regional or population-specific patterns by sifting through mountains of anonymized patient data. Internet of Things companies may rank process improvements based on real-time environmental data streams of thousands of field-based sensors.

Big data offers industries tools for solving some of their biggest challenges. For example, logistics companies can analyze GPS data to optimize delivery routes, cutting costs and reducing delivery times. Retailers can examine shopping behaviors to predict trends and adjust their inventory accordingly. Governments can assess massive citizen datasets to allocate public resources better or prepare for emergencies like natural disasters or pandemics.

Big data can help us learn more and reduce our guesswork. To get a quick start, maximize your time and money. In that case, AWS is helpful.

Amazon Web Services’ Big Data Analytics: What Are My Options?

Amazon Web Services (AWS) big data analytics products mostly fall into four types:

• Analytics
• Data movement
• Data lake
• Predictive analytics and machine learning.

Services such as Amazon Elastic MapReduce (EMR) are available on AWS for processing, visualizing, and analyzing enormous amounts of data. One of IT-Magic’s many pleased clients is a rapidly expanding SaaS firm that successfully met all of its data processing requirements after deploying Amazon Elastic Medical Record (EMR) with the help of AWS infrastructure support.

Amazon Web Services (AWS) offers serverless solutions like Amazon Kinesis Data Streams, which can store and analyze data streams of any size to facilitate real-time data transfer. Data lake solutions also rank well on the AWS cloud. Services like AWS Glue and AWS Lake Formation ease the creation of scalable, secure data lakes for enormous amounts of unstructured information. Modern society can only function with this data infrastructure.

Regarding artificial intelligence and machine learning systems, Amazon SageMaker is among the most recognizable names. SageMaker is crucial for extensive data because of its fully controlled infrastructure and capabilities. The teams responsible for MLOps may shift their emphasis from ensuring that all IT software and hardware are current to ensuring that the models they are working on are of high quality.

These are just a few of the numerous AWS solutions available. AWS gives companies everything they need to succeed in big data. Finding the correct tools and learning how to use them effectively for certain use cases could be difficult. Fortunately, that’s an area where IT Magic really shines.

Real-World Applications of AWS Big Data Analytics

AWS is already enabling businesses across multiple sectors to solve unique challenges:

• Healthcare: Hospitals use AWS to analyze patient data, improve diagnoses, and personalize treatment plans.
• Retail: E-commerce platforms employ AWS analytics to recommend products, optimize inventory, and manage supply chains.
• Entertainment: Streaming services analyze viewing habits to suggest content and manage licensing costs effectively.

These success stories highlight how AWS tools can scale to meet even the most demanding requirements.

How to Get Started with AWS Big Data Solutions

If you’re ready to embrace big data analytics, here are steps to consider:

1. Define Your Objectives: Clarify what you hope to achieve with big data, whether it’s better customer insights, fraud detection, or predictive maintenance.
2. Evaluate AWS Services: Identify which AWS tools align with your goals.
3. Start Small: Use AWS’s free tier and trial periods to experiment with data pipelines and analytics tools.
4. Partner with Experts: Companies like IT-Magic can accelerate your progress and optimise your setup.

You can turn your data into a powerful strategic asset by leveraging AWS and following these steps.

Realize Big Data’s Full Potential with AWS and IT-Magic

Contact IT-Magic if you need help establishing a foundation for considerable data-driven future growth. As an AWS Advanced Tier Services partner specializing in Data and analytics, they have assisted enterprises from all industries in enhancing their data analytics capabilities.

IT-Magic’s expertise goes beyond merely setting up infrastructure. They work closely with businesses to align AWS’s powerful tools with their goals. Whether you need to build a robust data lake, enable real-time analytics, or deploy machine learning models, IT-Magic has the know-how to deliver tailored solutions.

IT-Magic wishes to help customers with data analytics by creating a space that is open to new ideas and risks. They can assist you with creating an AWS cloud infrastructure well-suited for big data applications. Also, if you’re looking to make better strategic decisions with your company’s data, their experts are ready to help.

The post Get the Most Out of Your Big Data Project with AWS Analytics! appeared first on Designveloper.

Heightened Emphasis on Ethical AI and Data Privacy

As data science evolves, so do ethical AI and data privacy concerns. Regulations like GDPR have set new principles for the usage of user data, making companies more responsible for the ethical usage of data. Other organisations, such as Data Science UA, dealing with computer vision software development, have transitioned to transparent AI models so that their algorithms act responsibly towards the users, eradicate biases, and stay in the periphery of the ethical dimensions. This trend represents a paradigm shift toward responsibility with AI and helps the users set up and maintain trust with the service providers.

AutoML: The Rise of Automatic Machine Learning

Automation in machine learning democratises the domain of data science by automating such mundane tasks as choosing data preprocessing and model selection for building a model. Thus, it changes the analytics landscape by bringing the power of machine learning to a wide range of users, business analysts, and domain specialists and making quick, efficient decision cycles across many industries a reality. Therefore, in the context of machine learning innovations, automation could assure innovation and improve competitive advantage in an increasingly data-driven world.

Real-Time Data Analytics and Edge Computing

Real-time analytics processes the data in real-time; hence, it diverges from the traditional way of processing in batches. If integrated with edge computing that is processing data closer to where they are sourced, thus reducing latency to give faster insights is incredibly revolutionary to industries like IoT, finance, and health where real-time decision-making counts since it not only offers improved user experiences as well as improved operational efficiency but also provides companies with the vital competitive edge in this highly fast-paced market.

Enhanced Analytics for Data Democratization

Augmented analytics leverages AI to automate data preparation and generate insights and explanations. It enables users to interact with data in natural language to democratise information and make data analysis possible without requiring expert knowledge in non-technical employees. This will hugely upgrade decision-making processes across organisations by supplying intuitive tools like Tableau and IBM Watson Analytics, which empower all employees toward a very strong data-driven culture that permeates every department and drives innovation and efficiency throughout the enterprise.

Enhanced Natural Language Processing (NLP) and Language Models

Breakthroughs in NLP by GPT and BERT have opened doors to language understanding and generation in ways nobody could have imagined. Models, ranging from chatbots and sentiment analysis to the automatic translation of languages, allow for unparalleled accuracy. NLP models can be complex, produce responses similar to humans, improve customer service, and open the gates to content creation. Applications are so versatile that NLP will soon be an even more integral part of any data-driven organisation.

Quantum Computing in Data Science

Quantum computing can revolutionise data science, unleashing the capability to solve complex data problems at extremely high speeds. It is still in its early stages, but it has the potential to significantly change cryptography, optimisation, and large-scale simulation in the coming years. Companies like Google are investing in quantum research, seeing it as crucial. This technology will eventually allow data scientists to process large datasets faster than ever, unlocking insights that were previously unreachable.

Data Science for Environmental and Social Good

Data science primarily applies to business applications. However, it is also increasingly used in climate change research or developing predictive healthcare models for societal issues. Data-driven projects help organisations monitor environmental impact, improve resource usage, and shape policies that can lead to sustainable lifestyles. This trend highlights data science’s role in fostering socially responsible approaches to global challenges.

Innovations to Watch: AI-Driven Data Science Tools and Platforms

New tools powered by AI are starting to emerge that will support data scientists in data wrangling, visualisation, and predictive modelling. Richer functionality on platforms like DataRobot and RapidMiner simplifies sophisticated data tasks and unlocks faster and more accurate analyses. Equipping organisations with tools helps them stay competitive. These tools accelerate time-to-value and drive efficiency in data science processes. The future evolution of data science tools will facilitate easier workflows in data-driven areas. Additionally, these tools will empower businesses to better utilise insights. This will promote innovation and foster a culture of continuous improvement across all levels of the organisation.

Conclusion

Data science is an ever-evolving field that transforms industries and decision-making for businesses. More organisations are realising that data-driven insights can create operational efficiencies and encourage innovation. To compete in today’s data-driven world, businesses must exploit the latest trends in data science. This includes AutoML, ethical AI, and quantum computing. These advancements are necessary not only for automating workflows but also for resolving complex challenges with unprecedented speed and accuracy.

Firms should follow these developments in data science to use them strategically for long-term value creation. In a data-driven world, these advancements enable organisations to adapt to changing market dynamics. They also help create better experiences for customers. Additionally, businesses can make informed decisions that align with their long-term objectives.

The post New Trends and Innovations in Data Science appeared first on Designveloper.

Carefully Evaluate Migration Readiness

Before committing to a major Visual FoxPro migration, companies need to assess their readiness across three key areas honestly:

Legacy Software Environment

What is the legacy environment like today? Is it too complex or brittle? In other words, are there too many highly customized older applications that won’t be easy to modernize or replace? Migration will require just how much work is based on existing technical debt and capabilities.

Data Environment

What shape is your data in? Evaluating data quality, consistency, accessibility, and portability is crucial. Data migrations often prove more difficult than expected. Garbage in will mean garbage out after a migration if data issues are not addressed upfront.

Organizational Readiness

Do you have the skills, resources, bandwidth, and executive buy-in to make the migration a success? These big projects are straining IT teams and business units alike. Early identification of organizational change management and training needs is important.

However, if companies are not ready across these areas, migration success is in jeopardy, and schedule delays, cost overruns and legacy system snags are possible. It’s about being honest about readiness gaps and budgeting the time and resources to pre-emptively address them.

Recommended reading: Navigating the Cloud: How Web Hosting Drives Efficiency in Corporate Travel Platforms

Validate a Strong Business Case

Major software migrations require massive investments. The business case needs to be compelling enough to secure steady executive sponsorship and stakeholder alignment. Clearly define and validate the primary business drivers and expected outcomes before kicking off a migration journey that could span years.

Key business case validations include:

• Cost/benefit analysis – Will savings or revenue gains exceed project costs enough to justify the investment?
• Risk evaluation – What risks does the legacy environment pose if left in place without a migration?
• Current state analysis – Are legacy systems truly incapable of meeting current and projected business demands? Why?
• Future state visioning – Will desired capabilities be unlocked after migration, and is leadership aligned on what that looks like?

Without a solid business case, migrations can lose steam or get prioritized differently as leadership changes occur over long project timelines. Continually revalidating the business drivers and expected outcomes is essential governance throughout a migration initiative.

Architect the Future State with Care

Importance of Thoughtful Architecture

Architecting the future state technology environment is crucial for migration success, yet many companies fail to do this effectively. All too often, teams simply try to replicate legacy system capabilities within new solutions. This reactive approach usually leads to suboptimal results and missed opportunities.

Envisioning the Future State

Instead, you need to thoughtfully envision and architect your future state with the business capabilities required for the future firmly in mind. Conduct productive working sessions with both business and technology stakeholders across the organization.

Define what capabilities will actually differentiate you in the market and empower employees moving forward. Which solutions will unlock our growth flywheel? Get alignment on the must-have functionality while standardizing other non-differentiating systems.

Aligning Business Goals and Technology Principles

Your business goals and strategy should be the future state architecture. However, there are some fundamental modern technology principles that we should embed, such as cloud scalability, API-centered connectivity between systems, modular components, centralized data access, and built-in security.

Nobody can predict the future, but cloud solutions are incredibly flexible and provide unmatched scale to start small and scale up or scale down as the needs change. The move from a rigid legacy architecture to APIs and microservices brings much-welcomed agility. It lets you swap out components as needed, like building blocks.

Evolving Beyond Legacy Limitations

You only want to migrate legacy technical debt and limitations forward if you have to. This migration is the perfect opportunity to evolve. Architecting the future state is tough work that requires a shared vision across the business and IT.

The Long-Term Impact of Today’s Decisions

But done right, you can recast aging technology environments into key enablers of strategic goals rather than a constraint or afterthought. The solutions you architect today will determine what your organization can and can’t accomplish for years to come.

Plan Meticulously and Adjust Accordingly

Major software migrations can easily take 1-3+ years from planning through execution. Trying to plan out every project detail upfront meticulously is a fool’s errand. At best, companies can define a high-level roadmap with the flexibility to adjust as inevitable surprises occur.

Critical elements of a pragmatic migration roadmap include:

• Phased deployment – Break projects into manageable phases focused on specific business capabilities or functions. Avoid big-bang cutovers.
• Pilots and testing – Test migration processes, data conversions, integrations, etc., with pilot groups first before wide-scale rollouts.
• Business involvement – Keep business representatives involved throughout to monitor results and catch issues early.
• Contingency planning – Identify risk scenarios and have contingency plans in place for potential pivots or delays.
• Post-implementation reviews – Conduct lessons learned and satisfaction assessments after each major milestone to identify ongoing improvements needed.

Adequate testing and piloting will reveal flaws and knowledge gaps in even the most meticulous plans. Building structured feedback loops and contingencies into the roadmap will enable course corrections. Remain nimble to adjust project scope and timelines based on learnings without losing executive buy-in.

Invest in Migration Testing

Testing is the most underappreciated aspect of major software migrations. IT teams often underestimate the testing efforts required, both from a timeframe and resource perspective. This is problematic since testing directly reveals how well solution designs and data migration assumptions actually align with business requirements.

Testing phases that require heavy focus for enterprise migrations include:

User acceptance testing (UAT) – Validate new solutions perform as expected for key user workflows. Identify usability and functionality gaps early.

Integration testing – Rigorously test all critical application and data integrations. Verify reliable interoperability and data synchronization.

Performance testing – Validate that new solutions can handle peak production workloads and expected growth. Identify infrastructure gaps.

End-to-end process testing – Test major business processes from end-to-end. Catch workflow gaps across systems.

Regression testing – Re-test previously working functions to catch any new defects after solution changes.

Data validation – Scrutinize production data conversions. Data errors post-migration severely impact business operations.

Getting testing right is crucial. Assign adequate resources to validation well in advance of production cutovers. Phase testing in conjunction with piloting and work to resolve defects systematically.

Invest in People and Skills Development

Ultimately, people determine the success or failure of technology migrations. Unfortunately, companies often underinvest in change management and skills development required to adopt new solutions. This slows user adoption and the ability to unlock full business value from new technologies.

Major training initiatives that require investment include:

• Technical training – Hands-on training to reskill IT teams on new solutions being adopted.
• Process training – Training for business users on updated processes and workflows enabled by new systems.
• Leadership alignment – Sessions to align leaders on desired outcomes and get their vocal advocacy to drive adoption.
• End-user training – Easy-to-consume training for all end users on new solutions and digital capabilities available to them.
• Power-user networks – Develop networks of power users across business units who become local experts and coaches.
• Measurement and reinforcement – Regularly measure solution adoption and continue to reinforce usage through training refreshers as needed.

Well-executed change management ensures organizations have the skills and alignments required to migrate successfully to new ways of working. This upfront investment pays long-term dividends.

Maintain Laser Focus on Data Quality

“Garbage in, garbage out” remains one of the most famous adages in technology for a reason. Data underpins business operations and decision-making today. Just a few critical data errors post-migration can wreak havoc on downstream systems and processes.

That is why maintaining an intense focus on data quality, conversion, and validation takes outsized importance on enterprise migrations. Some leading practices include:

1. Data profiling: Thoroughly profile legacy datasets to understand what is clean and what is dirty. Identify data domains requiring fixes beforehand.
2. Data cleansing: Provide resources to fix identified dirty data in legacy systems prior to conversion. This avoids baking in problems.
3. Validation rules: Define business rules and validations to test against converted datasets. Automate where possible.
4. Reconciliation: Compare production data volumes, metrics, and KPIs pre- and post-conversion to identify inconsistencies.
5. Anomaly detection: Use statistical analysis and even AI-based anomaly detection to uncover odd data values that skew results or break downstream processes.
6. Incident response: Have skilled data resources ready to investigate and resolve data issues discovered post-migration with minimal business disruption.

While often considered boring or secondary work, getting data right is what keeps companies running. Data experts working closely with business teams need to take the lead on data quality initiatives underlying migrations.

Conclusion: Remain Flexible and Expect Surprises

Major enterprise software migrations are multi-year transformational journeys full of expected and unexpected twists. I remain attuned to the fact that surprises and challenges will continue to arise no matter how meticulous the upfront planning is. Build contingency plans along with structured feedback loops to correct course smoothly when roadblocks occur.

If organizations follow leading practices around the critical elements above – validating readiness, centering architecture on differentiating business capabilities, phasing rollouts, testing rigorously, investing in people, ensuring data quality, and working closely with partners – they put themselves in the best position to migrate successfully and unlock their desired business outcomes over time.

With the rapid pace of technology innovation and evolution, companies must become adept at migrating between solutions to remain competitive. Using lessons from past migration journeys helps pave the way for future initiatives to reach their end destinations right on time.

The post Lessons Learned from Major Corporate Software Migrations appeared first on Designveloper.

What is your use case?

Datacenter Proxies

Datacenter proxies are intermediaries that route your internet traffic through powerful servers in commercial data centers. Such connections aren’t verified by residential internet service providers, but they aren’t limited by them either. Such an internet connection ensures that datacenter proxies are the fastest proxy type there is.

Cost of Datacenter Proxies

But that’s not it. Datacenter proxies are also the cheapest. One server can create thousands of virtual machines with a new IP address. Providers don’t need to support a physical device for each IP address, which drives down the price per IP to a bit more than a dollar.

Limitations of Datacenter Proxies

I could say a lot more good about datacenter proxies, but it won’t change the fact that they aren’t meant for every use case. Yes, datacenter proxies are fast and cheap, but sometimes you need one or two IP addresses to ensure maximum anonymity. In such cases you are better off with a residential proxy.

Mobile Proxies

And when you need to access mobile services apps or website versions, for example, you will need to purchase mobile proxies. There’s no way around it, in some cases, datacenter proxies, even if the cheapest option, won’t be the best performing.

Importance of Research

The only way to avoid a mistake is to research your use case before committing to a large pool of IP datacenter proxies. Many people blindly purchase large datacenter pools just to see the IPs banned on their websites quickly.

Do you need accurate location targeting?

A drawback of datacenter proxies is that they aren’t very good at targeting locations on a city level. A provider might have a large selection of regions, but we must understand that commercial data centers capable of running proxies are quite sparse.

Due to datacenter proxies using a commercial internet connection, you won’t be able to select a specific city. A country or a region is as far as datacenter proxies can go. If your target website requires more accurate targeting, as is often the case in bulk purchasing, you’ll need residential proxies.

These proxies are set up in residential areas with ordinary internet connections. They are slow, but you will have to live with it if your tasks deem it necessary.

Private or Shared access?

Datacenter proxies work great when shared with multiple users. Since the servers are powerful, you won’t see much performance drops. However, you cannot be sure that other users won’t ban the IPs on your target web servers. Therefore, consider choosing a private proxy pool.

Private datacenter proxies are better both for speed and detection avoidance. At a slightly larger cost, you will be able to use the IPs exclusively by yourself. Unless you are on a strict budget, I suggest choosing private proxies.

Are you fine with rotating IPs?

Most datacenter proxies are rotating by default. It makes it more difficult for websites to detect you are using a proxy and restrict your access if you aren’t playing by the rules. However, some use cases require a static IP address, which doesn’t change over time. Consider which proxy type is best for you before purchasing.

4 Factors for choosing a datacenter proxy provider

Considering the above factors will help, but the choice of provider might be even more important. The decision is complicated by the fact that one cannot evaluate the quality of the proxy provider without testing the proxies and supplementary services provided.

Firstly

The first thing to look for in a provider is the IP pool. Most providers brag about millions of IPs available. While it is true in most cases, you should ensure that the IP pools are big enough in the locations of interest to you. Many providers have IPs available only in certain regions, usually those where they are the cheapest. 

Secondly

Customer support is also an important factor. Datacenter proxies are the most reliable type, but downtimes, bugs, or other technical issues might still come up. It’s a good idea to chat with their team and check their responsiveness before actually purchasing proxies. Make sure to see if they have a blog with useful guides too.

Thirdly

Excellent reviews from other customers are a good indication the provider isn’t lying about the quality and quantity of their products. Check platforms like Trustpilot and online forums related to your use cases. You shouldn’t trust what others say blindly, but the general consensus is a good indication of quality.

Lastly

Lastly, test the proxy provider with a free trial. A good provider is a transparent provider, so it won’t shy away from showing what their proxies are capable of by providing free access. Some providers even give away proxy lists completely free of charge. If they can provide proxies for free, chances are their paid proxies will be even better.

If you need a recommendation, I had a lot of luck with IProyal datacenter proxies. They are relatively cheap, have large IP pools in good locations, and have a responsive and helpful team. Their dashboard and browser extension are very convenient and on par with what most other providers will have you paying extra.

Wrapping up

The biggest risk lies in choosing what isn’t optimal for your use case. It’s not that proxies won’t work at all, but it might not be an optimal use of your money. Considering the above questions is essential for ensuring the optimal performance of your datacenter proxies. A good provider will also help you to answer them.

The post Five Things to Consider Before Purchasing Datacenter Proxies appeared first on Designveloper.

What is Data AI Strategy?

A data AI strategy is a set of guidelines for how a company applies artificial intelligence to enhance its operations and make better decisions. It trains artificial intelligence systems using data gathered from several sources. Then, these systems can perform activities requiring human intellect, such as pattern recognition, prediction making, and process automation. 

However, the company must know its objectives. It must know what it wants to do using artificial intelligence. Does it want to raise revenue, enhance customer service, or streamline processes? When decided, it compiles the required data and is ready for artificial intelligence examination. 

The company then decides on appropriate artificial intelligence tools and technology for its requirements. It also develops a team that can properly handle and apply these tools. The approach calls for ongoing performance monitoring of artificial intelligence systems and necessary improvement actions. This guarantees the constant performance of the AI systems and insightful analysis. 

Key Steps to Successfully Implement a Data AI Strategy 

1. Define Clear Objectives & Roadmap

Clearly defining objectives is the first step in implementing a data artificial intelligence strategy. You must know the goals you wish to achieve with artificial intelligence. This can include enhancing operations’ efficiency, sales, or customer service. Well-defined goals will direct your artificial intelligence initiatives and maintain your concentration. 

First, determine the issues you want AI to address. Consult several departments to learn their needs. This will help develop particular objectives. For instance, if you want to improve customer service, your aim might be to cut response times by half. 

Once you have well-defined goals, jot them down. Verify that every member of your company knows them. This guarantees everyone is aiming for the same objectives. Well-defined goals are the basis of a good data artificial intelligence plan. 

2. Data Value Creation & Gathering 

Gathering and preparing the data comes second. Data is the fuel running artificial intelligence; your AI attempts will fail without solid data. Start by determining the information you will need to meet your goals. This could be operational, sales, or customer data. 

Once you have recognized the data, you must compile it. You can accomplish this through surveys, transaction data, and internet analytics tools. Verify that the data is comprehensive and accurate. Inaccurate or incomplete data can lead to incorrect conclusions and decisions. 

After gathering the data, you need to prepare it for analysis. This entails data cleaning, duplicate removal, and filling in missing values. Accurate artificial intelligence projections depend on good data preparation. However, guaranteeing the effectiveness of your data artificial intelligence plan requires time and money. 

3. Choose the Right AI Tools 

The third step is to choose the right AI tools. The market features several artificial intelligence tools. However, selecting the proper ones might greatly affect your artificial intelligence plan’s success. Research several artificial intelligence tools and their characteristics first. Seek tools that fit your goals and data requirements. 

Think about elements including support, pricing, and simplicity of use. You want tools within your means that are simple to operate. Search also for instruments with decent customer service. Should you run across any problems during execution, this can be quite beneficial. 

Once you have selected the tools, study how to apply them wisely. You should also teach your staff about their applications. This will guarantee their maximum potential.  

4. Build a Skilled Team

The fourth step is developing a qualified team. A good data artificial intelligence plan requires staff with the correct competencies, including domain experts, data scientists, and artificial intelligence analysts. Start by determining your necessary talents. Then, either hire or teach others possessing these abilities. 

Seek for those with a background in artificial intelligence, machine learning, and data analysis. For instance, someone with retail experience can offer insightful analysis if you work in the retail sector. 

Further, invest in team training and development. Artificial intelligence is fast developing, so maintaining your workforce’s current knowledge of the newest technologies and trends is crucial. 

5. Monitor and Improve 

The fifth and last step is monitoring and enhancing your AI strategy. Using a data artificial intelligence approach is not a one-time endeavor. It calls for constant observation and development. To monitor your growth, first arrange key performance indicators (KPIs). 

Review these KPIs often to find whether you are reaching your goals. If you are not, find the causes and act to correct them. This could require retraining your staff, changing your data, or improving your artificial intelligence algorithms. 

Also, track recent developments in AI. The field is changing continually, and new tools and approaches are being developed constantly. So, staying current with these advancements will enable you to strengthen your artificial intelligence approach. 

Conclusion 

Implementing a data AI strategy can be challenging, but following these five key steps can make it easier. Define clear objectives, gather and prepare data, choose the right AI tools, build a skilled team, and continuously monitor and improve your strategy to ensure its success. 

The post 5 Key Steps to Successfully Implement a Data AI Strategy  appeared first on Designveloper.

The terms “data analyst” and “business analyst” are often used interchangeably, causing confusion. So this article aims to clear up this confusion. 

Whether you’re seeking to understand these roles or considering a career in these fields, this guide is for you. 

In this detailed comparison, we’ll delve into what a data analyst and a business analyst do, explore the similarities and differences between the two jobs, and discuss their importance in tech companies.

So, are you ready to unravel the mystery of data analyst vs business analyst roles? Read on!

Data Analysts and Business Analysts in Tech Companies

Diving into the world of tech companies, two roles often stand out: Data analysts and business analysts can be classified under this category. The following comparison of data analyst vs business analyst is to provide clarity on these important roles. Both positions have to do with decision-making processes but their responsibilities are towards different aspects of the company.

What is a Data Analyst?

A Data Analyst is a person who gathers, processes, and analyzes data using statistical tools on a large set of data. They learn how information can help to inquire and find solutions to various issues. Thanks to the big data, the position of a Data Analyst has become more significant.

Collecting, Cleaning, and Analyzing Data

Data collection and preparation and data analysis are also among the main duties of a data analyst. This process is fundamental when it comes to the comparison of the “data analyst vs business analyst” positions.

Data analysts initially collect data from various sources. These could be databases, online services or APIs. For instance, a data analyst employed in a technology firm may extract the data of users’ engagement with an application from the firm’s backend database.

After data collection is data cleaning or data pre-processing. Information from real-life scenarios is often messy and has a high level of variation. Cleaning and preprocessing are carried out using programming languages like Python using Pandas or SQL. It manages with the missing values, removes the features that are not relevant, and also manages the inconsistency in the data for analysis.

The last process is data analysis. Data analysts use statistical methods and data graphical methods to work on data and search for patterns. For example, they may use the scatter graph to demonstrate the relationship between the time spent on the app and the user churn rate.

It is also a cyclic process of data collection, data cleaning, and data analysis as well. When new data is collected, data analysts perform the process again, and present the updated results to the tech companies to help them make their decisions.

Using Statistical Methods and Data Visualization Tools

Statistical methods are another important component of a data analyst’s work. These techniques help work on big and complex data and derive meaningful information. For instance, they may use regression analysis to identify the relationship between one or more variables in a data set.

Another important aspect relating to a data analyst is data visualization tools. Such tools like Tableau or Power BI help the analyst to present his results in a more presentable and comprehensible way. For example, a data analyst may use a scatter graph to present the correlation of two variables.

When speaking about the difference between the two roles in the context of “data analyst vs business analyst”, it is crucial to note that while both of them operate with data, the tools and methodologies can be rather dissimilar. A data analyst works more with raw data and processes it with the help of statistical tools and data visualization tools. On the other hand, the business analyst might be more concerned with using this data for strategic business planning.

Just to recall, when choosing between a data analyst and a business analyst, the nature of data and the needs for the analysis will be the determining factors. Both positions are helpful and can provide you with the different view of your business processes.

Contributing to Technical Projects and Product Development

Technical projects and product development involve the use of data analysts who are very essential in this process. They apply data to make decisions and ensure that the products are developed and constructed with data backing.

For the purpose of this analysis, let us consider a tech firm that is intending to develop a new mobile application. A data analyst would engage with the user behavior data, the market data, and the competitors’ data. They may discover that the audience prefers simplicity and organization of the site. This could help in the development of the app and come up with a better app for the end user.

Moreover, data analysts are typically in a working collaboration with the product development team. They are valuable as they include data for determining further development of the product. For instance, if the data shows that users are spending much time on a given aspect, the team may decide to enhance that aspect in the next version.

When it comes to data analyst vs business analyst, the former is more focused on the product’s technical aspect. The data analysts use programming languages like Python or R to clean the data and to fit models on the data sets.

Just keep in mind that every technical project is a puzzle and data are the pieces of this puzzle. The data analyst’s role is to orchestrate these pieces in a way that is beneficial to the overall project. These are crucial in converting the collected data into strategic plans; hence their relevance to any tech firm.

What is a Business Analyst?

A Business Analyst is a person who is in the middle between the development team and business representatives. They are supposed to have a clear vision of business requirements and transform them into technical specifications, as well as to make sure that the solutions adopted meet business goals.

Focusing on Business Needs

The position of business analysts is very vital in making sure that the functioning of a tech company is aligned to the business objectives. They serve as the interface between the IT and business aspects of the organization to ensure that data and technology are used to meet the firm’s goals.

Business orientation is another critical area of the business analyst’s duties and responsibilities. They interact with the stakeholders with a view of understanding their needs, issues and goals. This helps them to search for improvement spots and suggest adjustments that would add value to the business.

For example, a business analyst employed in a tech firm may be asked to improve efficiency of a specific activity. It would start with the identification of the current process, identification of the critical points, and the gathering of the requirements for the subsequent process. They would then consult the IT department to find a solution, for instance, creation of new software program or change in the process map.

Conducting Stakeholder Interviews, Gathering Requirements, and Analyzing Business Processes

A further duty of a business analyst in the context of the tech industry is to bridge the gap between the IT and the business departments of an organization. These are the identification of stakeholders, interviews with these stakeholders, gathering of the requirements and evaluation of business processes.

Stakeholders’ interviews are crucial in the process of defining the requirements and expectations of different actors in the project. These interviews are useful in as much as they assist in identifying the objectives of the project, the likely challenges which may be faced and the anticipated outcomes. For instance, a business analyst employed by a technology firm can have a one-on-one with a product manager to understand what he or she requires in a new software product.

The second significant process is the collection of requirements. This involves defining the special needs of the stakeholders. A business analyst acquire these requirements by methods such as brainstorming, questionnaires, and focus group meetings. For example, when developing a new data analysis tool, the business analyst may gather the requirements concerning the tool functionality, its look and feel, and compatibility with other applications.

Business process analysis is the investigation of the current process, the evaluation of the weaknesses and the specification of the improved process. Such analysis can lead to enhanced processes and a match of the IT systems to the business needs. Let’s assume that a tech firm needs to enhance the process of data reporting in its functioning. The business analyst would observe the current process, determine where the issues are and then work on how to resolve them.

Supporting Business Interests and Ensuring Solutions Align with Strategic Goals

A business analyst in the technological sector is also expected to play the role of an interface between the IT and business divisions. They are the ones who can ensure that the solutions provided correspond to the strategic goals of the enterprise. This entails the specification of the organization’s needs and the alignment of these needs with the available technical specifications and ensuring that the solutions implemented meet the requirements.

For example, if we consider a case of a tech firm that wishes to improve its customer relations. A business analyst would first of all identify the business vision of the organization, which here is to enhance customer satisfaction. They would then discuss with the IT department to seek for a remedy, for example, a more efficient CRM.

The research done in the recent past indicates that firms with well-coordinated business and IT strategies outperform those that do not have this kind of synergy by 21%. This statistic highlights the significance of the work of a business analyst in relation to business needs and the guarantee of the solution’s compliance with the strategic objectives.

Business analysts are also involved in the management of projects and the execution of change management initiatives. They help in identifying the scope of the project, risks and opportunities involved and that the project does not exceed the set budget and time frame. For example, in the process of creating a new mobile application, the business analyst works with the project manager, developers, and designers to make sure that the application meets the business objectives and the users’ needs.

The Importance of Data and Business Analysts in Tech Companies

The world is becoming increasingly data-driven, with the big data market size projected to reach USD 103 billion by 2027. This surge in data generation has led to an increased demand for professionals who can decipher this data and extract valuable insights. 

These numbers prove this statement: According to the World Economic Forum, Data Analysts will become the job in the highest demand. Meanwhile, the job growth rate for Business Analysts is at 9% from 2018 to 2028 according to estimates .

That’s why the importance of analysts cannot be overstated in this data-centric era. Despite the differences between data analyst vs business analyst roles, both are essential in empowering businesses to utilize data for strategic decision-making and success.

The impact of these roles is consistent across all industries, particularly in technology. Let’s explore how these roles contribute to every facet of operations within tech companies, using DSV as an example:

• Data-Driven Development: Our analysts work alongside developers to ensure every feature we build has the backing of data insights, leading to products that truly resonate with users.
• Performance Optimization: We continuously analyze user behavior and app performance to identify areas for improvement, ensuring our products are fast, reliable, and user-friendly.
• Market Research & Competitor Analysis: Our analysts stay ahead of industry trends and analyze competitor strategies, informing our product roadmap and ensuring we remain competitive.
• Client-Centric Solutions: We leverage data to understand our clients’ specific needs and tailor our solutions accordingly, delivering results that achieve their business goals.

With the invaluable support of data analysts and business analysts, we successfully deliver innovative, user-centric, and market-leading tech solutions for our clients. These professionals bring immense value to DSV. They are the unsung heroes behind the scenes, turning data into actionable insights and driving business success.

The Importance of Data and Business Analysts at Designveloper

At Designveloper, we understand the value of Data Analysts and Business Analysts in the technology industry. These two roles are somewhat different but are in harmony to guarantee that our projects reach a successful conclusion.

Data Analysts at Designveloper are decision-makers within the company since they are responsible for making decisions on behalf of the company. They collect and process a vast amount of information to help us understand such things as trends, patterns, and other characteristics. It means that we can make decisions based on the data we have, increase our efficiency and effectiveness of operations, and even predict possible trends.

On the other hand, our Business Analysts are in the middle between the technical team and the stakeholders. They interact with the clients to understand their requirements, correlate these with technical requirements and make sure that our solutions are still business relevant.

An example of such mutual cooperation was during the development of a complex for an e-commerce store. The requirements were collected from the client and our Business Analysts assisted in transforming them into technical requirements. On the same note, our Data Analysts observed the users’ behavior on the website and determined the specific products that are of interest and the challenges that the user is likely to encounter. This synergy was realized in the development of a friendly user website that boosted the client’s sales significantly.

Data Analyst vs Business Analyst: 3 Similarities

Both data and business analysts play an integral part in the decision-making process of any business. Therefore, the two roles share some of the common grounds:

Analytical Skills and Problem-Solving Abilities

Both data analysts and business analysts rely heavily on their analytical skills. They use these skills to interpret complex data and business processes, identify patterns, and draw meaningful insights. Therefore, their work often involves problem-solving abilities, where they need to address challenges and find effective solutions.

Collaboration with Various Teams

Data analysts and business analysts are not isolated in their roles; they collaborate with various teams within the organization. 

For instance, they may work with engineering teams to understand and utilize technological tools, or with marketing teams to understand customer behavior and market trends. This collaboration allows them to gain a holistic view of the organization and contribute more effectively to its goals.

Contribution to Data-Driven Decision Making

Perhaps the most significant similarity between data analyst vs business analyst roles is their contribution to data-driven decision-making. 

Both jobs involve analyzing data or business processes to derive insights that inform strategic decisions. Whether you’re identifying a new market trend or finding efficiencies in a business process, both roles use data to back decisions and contribute to the organization’s overall strategy.

Data Analyst vs Business Analyst: 3 Differences

Despite several similar points, there are still some noticeable differences between data analyst vs business analyst roles. In the next comparison, let’s take a look at how they contribute unique goals, responsibilities, and skills.

Goals

Both these jobs have different goals in today’s business landscape. 

Data analysts focus on the data itself. They delve into the sea of numbers and statistics, unearthing hidden patterns and valuable insights. Their role is to answer the “what” and “why” – deciphering what the data is saying and why it’s saying it. 

Business analysts, on the other hand, focus on the business context and needs. They translate the complex data narratives into understandable and actionable plans that drive business objectives. With that ultimate goal, they help answer the “how” – using the insights derived from data to devise strategies and solutions.

Responsibilities

Data analysts are mainly tasked with gathering, processing, and conducting statistical evaluations of data. 

To gain a deeper understanding of their responsibilities, let’s examine the workflow of data analysts:

• Interpret statistical data and convert it into actionable insights. Then businesses will leverage these insights for crucial decision-making.
• Use specialized tools and software to extract data, answer data-related questions, and establish procedures to optimize data efficiency.
• Analyze and interpret data trends, then report these trends to enhance business value.
• Maintain and advance data databases or systems.
• Turn data into a format that is easier to comprehend.
• Use automated tools to mine data from primary and secondary sources.
• Evaluate the significance and quality of data through analysis.

On the other hand, business analysts are in charge of comprehending business needs and transforming these needs into technical specifications. 

Here are the duties they are responsible for:

• Bridge the gap between informed decision-making and business objectives. 
• Assess business operations and systems to identify areas of improvement in terms of productivity, efficiency, and profitability.
• Collaborate with stakeholders, identify requirements, and highlight any issues that may be impacting a business’s bottom line.
• Identify opportunities as market conditions evolve, build effective solutions to problems, and manage the implementation of projects they lead.
• Constantly analyze data to seek issues that could cause downstream effects.
• Research advanced techs to modernize processes and systems.
• Hold regular meetings with stakeholders to present and communicate their findings.
• Develop projects.

Skills

Data analysts typically require robust statistical skills and proficiency in data manipulation languages such as SQL or Python. They also need to be skilled at using data visualization tools like Tableau or PowerBI.

While business analysts also need to understand data analysis, they place a greater emphasis on communication and project management skills. They often need to be proficient in business intelligence tools and methodologies, and have a solid understanding of the specific industry they operate in.

Data Analyst vs Business Analyst: Which One to Choose

A data analyst works with large sets of data and tries to find out a pattern in it. They use statistical methods and programming languages such as SQL and Python to gather, scrub, reshape, and analyze data. For example, a data analyst working for a retail firm may use data from customers’ purchases to determine the patterns of their purchases in order to help the firm in its marketing.

On the other hand, a business analyst is more concerned with the requirements of a business and come up with recommendations based on data. They assess the current state, define the opportunities for change, and design the response to the business issues. For instance, a business analyst working for a software company may use the data on users’ feedback to suggest changes to the product.

Both positions demand analytical thinking and knowledge of SQL and other programming languages; however, the decision between the two depends on the individual’s preferences and objectives.

So to choose between these two roles, several factors come into play. They include:

• Data or Business Focus: Does unlocking hidden data insights thrill you, or does shaping business strategy energize you?
• Technical Prowess: Are you comfortable with complex datasets and statistical analysis, or do you excel at communication and collaboration?
• Long-Term Career Vision: Do you see yourself diving deeper into data science, or does collaborating with decision-makers drive your passion?

For instance, if you dream of a career steeped in data science, the data analyst path offers a launchpad for exploring advanced statistical analysis. However, if you’re more interested in shaping business strategy and partnering with key stakeholders, the business analyst role might be your perfect match.

Conclusion

By now, you’re probably buzzing with insights! After this comparison, you may have a detailed insight into the similarities and differences of data analyst vs business analyst roles. 

Whether you want to uncover hidden trends in data or thrive on translating insights into real-world action, both data analyst and business analyst offer exciting, high-demand careers in the tech world. 

No matter your choice, the tech industry welcomes your unique talents. So, explore further, discover your strengths, and unleash your inner data or business whiz!

The post Data Analyst vs Business Analyst: A Comparative Guide for Beginners appeared first on Designveloper.

What Is Cloud Data Protection (CDP)?

This technological term refers to the practices, technologies, and strategies used to safeguard data stored in cloud environments. Its primary goal is to ensure the confidentiality, integrity, and availability of data while leveraging the benefits of cloud computing.

Cloud-based DLP and CDP are interconnected components of a comprehensive cloud security strategy, working together to protect sensitive data from various threats and ensure regulatory compliance. Cloud-based Data Loss Prevention (DLP) refers to the use of cloud services to monitor, protect, and manage sensitive data across various cloud environments and endpoints. 

The benefits of cloud-based DLP extend from security to various business aspects. It enables seamless scalability, allowing businesses to adjust their data protection measures as they grow without significant additional costs.

Benefits of Cloud Data Protection 

CDP provides a robust framework to safeguard data stored and processed in the cloud and the following benefits:  

• Enhanced Data Security 

CDP solutions often include encryption both in transit and at rest, ensuring that data is protected from unauthorized access. Implementing strict access controls helps prevent unauthorized users from accessing sensitive information.  

• Data Availability and Reliability 

Regular backups and efficient recovery mechanisms ensure data is always available, even in the event of data loss or corruption. CDP solutions include disaster recovery plans to maintain business continuity during unexpected events.  

• Scalability 

Cloud platforms allow businesses to easily adjust their storage needs without investing in physical infrastructure. Efficient resource management enables businesses to optimize their storage usage and costs.  

• Cost Efficiency 

Businesses avoid significant upfront investments in hardware and infrastructure, reducing capital expenditure. Pay-as-you-go models help manage operational expenses more effectively.  

• Compliance and Governance 

CDP solutions help organizations comply with industry regulations and standards, such as GDPR, HIPAA, and ISO. Detailed audit trails and logging features support compliance audits and internal governance processes.  

• Simplified Management 

Centralized management consoles offered by cloud platforms make it easier to monitor and manage data protection policies across the organization. Automation of backup, recovery, and security processes reduces the burden on IT staff and minimizes human error.  

• Flexibility and Accessibility 

CDP allows employees to access data securely from anywhere, enhancing remote work capabilities. CDP solutions often support multiple platforms and devices, ensuring seamless integration across various IT environments.  

• Improved Collaboration 

Cloud platforms enable shared access to data and resources among team members and partners. Real-time data synchronization ensures that all users have access to the most up-to-date information. 

Essential Aspects of Cloud Data Protection

Understanding cloud data protection is crucial for safeguarding sensitive information, ensuring regulatory compliance, and mitigating security risks. It also helps maintain business continuity by preventing data loss and ensuring quick recovery.  

Data Encryption 

Data encryption is crucial for cloud data protection. Encrypting data at rest on cloud servers prevents unauthorized access while encrypting data in transit protects it from interception as it moves between the user’s device and the cloud.  

Access Control 

Access control involves implementing strong authentication methods such as multi-factor authentication (MFA) and using role-based access control (RBAC) to ensure users have only the permissions necessary for their roles.  

Data Masking 

Data masking is important for protecting sensitive data in non-production environments during development or testing.  

Data Backup and Recovery 

Regular data backups and having a recovery plan are essential to prevent data loss due to accidental deletion, corruption, or ransomware attacks, ensuring business continuity through disaster recovery plans. 

Compliance and Regulatory Requirements 

Compliance with regulatory requirements such as GDPR or HIPAA is necessary to meet legal standards. This involves regularly auditing and documenting compliance efforts.  

Security Monitoring and Incident Response 

Security monitoring and incident response are critical; continuously monitoring cloud environments for suspicious activity and having an incident response plan in place to quickly address security breaches or data leaks.  

Data Loss Prevention (DLP) 

Data loss prevention (DLP) solutions help monitor and control data transfers, preventing unauthorized data exfiltration.  

Identity and Access Management (IAM) 

Effective identity and access management (IAM) ensures secure management of identities and their access to cloud resources, including proper user lifecycle management (provisioning, de-provisioning, and role changes).  

Security Patches and Updates 

Regularly updating and patching cloud services and applications to protect against vulnerabilities is essential.  

Vendor Management 

Vendor management involves assessing and monitoring the security practices of cloud service providers, ensuring they meet contractual and regulatory security requirements.  

User Education and Awareness 

User education and awareness are critical components, involving training users on best practices for data protection and security and promoting awareness of potential threats such as phishing attacks. 

Common Use Cases of Cloud Data Protection

Cloud data protection involves securing data stored in cloud environments from unauthorized access, loss, or corruption. Here are some common use cases: 

Data Backup and Disaster Recovery 

Ensures data is regularly backed up to a cloud storage solution. Provides a means to recover data in case of hardware failure, natural disaster, or cyberattacks. Offers geographically dispersed backup locations to enhance data resilience. 

Data Encryption 

Encrypts data both at rest and in transit to protect against unauthorized access. Uses strong encryption algorithms and key management practices. Ensures compliance with regulatory requirements for data protection.  

Access Control and Identity Management 

Implements robust authentication and authorization mechanisms. Utilizes multi-factor authentication (MFA) and role-based access control (RBAC). Monitors and manages user access to sensitive data.  

Data Loss Prevention (DLP) 

Detects and prevents data breaches by monitoring data transfers. Implements policies to restrict the movement of sensitive data and overall data management. Uses machine learning and pattern recognition to identify potential data leaks.  

Compliance and Regulatory Adherence 

Ensures data protection practices meet industry standards and regulations (e.g., GDPR, HIPAA). Maintains audit logs and reports for compliance verification. Implements data retention and deletion policies in accordance with legal requirements.  

Data Masking and Anonymization 

Protects sensitive data by masking or anonymizing it for use in non-production environments. Reduces the risk of exposure during testing, analysis, or development activities. Ensures that data remains useful while protecting individual privacy.  

Threat Detection and Response 

Monitors cloud environments for suspicious activities and potential threats. Utilizes intrusion detection and prevention systems (IDPS). Automates response mechanisms to mitigate risks in real-time.  

Secure Data Sharing and Collaboration 

It enables secure sharing of data with third parties or within the organization. It utilizes encryption and access controls to safeguard shared data. It ensures that only authorized users can access and collaborate on sensitive information.  

Data Integrity and Verification 

Ensures data integrity through checksums and hash functions. Verifies that data has not been tampered with or corrupted. Provides mechanisms to validate data consistency and authenticity. 

Cloud Security Posture Management (CSPM)

Continuously monitors and assesses cloud environments for security risks. Implements best practices and security configurations. Identifies and remediates vulnerabilities to maintain a secure cloud posture. 

How to Choose a Cloud Data Protection Solution?

This requires careful consideration of various factors to ensure that your data is secure, compliant, and easily recoverable. Here’s a step-by-step guide to help you make an informed decision: 

Identify Your Needs 

Assess the amount of data you need to protect, the types of data involved (such as structured, unstructured, or sensitive data), and regulatory requirements like GDPR or HIPAA. 

Evaluate Security Features 

Ensure the solution offers strong encryption both in transit and at rest, robust access management, role-based access controls, and multi-factor authentication (MFA) for enhanced security. 

Check Backup and Recovery Capabilities 

The solution must support automated and scheduled backups, offer efficient data recovery processes, and provide granular recovery options.

Assess Integration and Compatibility

Your CDP solution must be compatible with your existing cloud infrastructure (such as AWS, Azure, or Google Cloud), integrated with your critical applications and services, and offered APIs for custom integrations.

Consider Performance and Scalability

Assess the impact on system performance during backup and recovery operations and ensure the solution can scale with your data growth and business expansion.

Evaluate Management and Monitoring

Look for a user-friendly interface, real-time alerts, detailed reporting capabilities, and a centralized dashboard for monitoring data protection status.

Review Vendor Reputation and Support

Research their reliability, market presence, and customer reviews. Ensure the availability of responsive customer support and comprehensive documentation, and check the Service Level Agreements (SLAs) for uptime, support, and recovery times.

Consider Cost

Understand the pricing model (such as subscription-based or pay-as-you-go) and the total cost of ownership, including setup, maintenance, and potential upgrades, and take advantage of free trials to evaluate the solution before committing.

Conduct a Proof of Concept (PoC)

PoC tests the solution’s effectiveness in your environment, assesses its performance under realistic workload conditions, and evaluates the ease of deployment, configuration, and use.

Ensure Compliance and Certifications

Check for industry-standard certifications such as ISO 27001 or SOC 2, and ensure the solution provides comprehensive audit logs for compliance audits.

Conclusion

Implementing robust cloud data protection (CDP) measures is crucial for securing sensitive information, ensuring regulatory compliance, and maintaining business continuity. By staying informed about the latest trends and adopting a proactive approach to cloud data protection, organizations can safeguard their data, support remote work capabilities, and foster improved collaboration.

The post The Ultimate Guide To Cloud Data Protection: Best Practices For 2024 appeared first on Designveloper.

But what exactly are these tools, anyway? You’ll find the detailed answer in our article. 

In this article, we’ll start with the definition and benefits of AI data analytics. We’ll then delve into the latest trends in AI data analytics and what the future holds. And, of course, this article also introduces you to the top 10 AI data analytics tools in 2024.

So, if you’re intrigued by the power of AI in data analytics and want to know about the tools that are making a difference, stay tuned!

Understanding AI Data Analytics

In the first section, we’ll discover the fundamentals behind AI data analytics. 

What are AI & Data Analytics?

Data Analytics is the process of methodically using logical and statistical techniques to describe, depict, and assess data. 

It involves the collection, cleaning, and analysis of data to extract valuable insights that would otherwise be lost in the mass of information. These insights, accordingly, enable your business to make more informed decisions.

Meanwhile, Artificial Intelligence (AI) represents the emulation of human cognitive functions by machines, predominantly computer systems. 

These functions encompass learning (which gathers data and the guidelines for its application), reasoning (which employs these guidelines to draw informed conclusions), and self-correction. Accordingly, AI can learn and adapt from data, which sets it apart from traditional computational systems.

How Can AI & Data Analytics Work Together?

AI and data analytics can work together to create powerful synergies. AI, with its ability to learn from data, can automate and optimize the process of data analytics. 

Navigating through large amounts of data, this advanced tech discovers patterns or trends and then delivers insightful conclusions quickly and accurately. This capability far exceeds what humans can do, thereby promoting the widespread adoption of AI in data analytics.

This led to the launch of AI data analytics tools. In today’s data-driven world, the need for these tools is more important than ever before. 

They can handle large volumes of data, uncover hidden patterns, and provide valuable insights, all while reducing the time and resources required for traditional data analysis. This makes them indispensable for any organization looking to leverage their data effectively.

The Role and Benefits of AI in Data Analytics

To better understand why data analytics tools are increasingly integrated with AI capabilities, let’s delve into the immense benefits of AI in these tools:

Automating Mundane Tasks

AI has permeated the business sector, with 51% of companies employing it for business process automation. Especially in the realm of data analytics, AI is instrumental in automating tasks that are often mundane and repetitive.

One of the key areas where AI proves its worth is in the automation of data cleaning and preparation. These processes, when performed manually, are not only time-consuming but also susceptible to errors. By automating these tasks, AI significantly enhances the accuracy and efficiency of data processing. Also, it can free up time for data analysts to focus on more complex tasks. 

Advancing Pattern Recognition

AI algorithms help your business identify hidden patterns, trends, and anomalies in data that might be missed by humans. This advanced pattern recognition capability can lead to new insights and discoveries, enabling your business to make more informed decisions.

Fostering Predictive Analytics

Predictive analytics is a field that detects future outcomes by using data, statistical algorithms, and AI/ML techniques. 

By analyzing historical data, AI can predict future trends and behaviors, helping businesses to make proactive, data-driven decisions. This predictive capability can be particularly useful in sales forecasting, risk management, and operational efficiency.

Improving Accuracy and Efficiency

AI can significantly enhance the accuracy and efficiency of data analysis processes. By automating data cleaning and analysis tasks, AI reduces the risk of human error, leading to more accurate results. 

Moreover, AI can process and analyze data much faster than humans, leading to increased efficiency. This improved accuracy and efficiency can result in better decision-making and increased business performance.

Democratizing Data Analysis

AI is democratizing data analysis by making it accessible to non-technical users. With AI data analytics tools, users without a background in data science can extract valuable insights from data. These tools often come with intuitive interfaces and automated features, enabling users to analyze data and generate reports with ease. 

This democratization of data analysis is empowering more people to make data-driven decisions and is fostering a culture of data literacy across organizations.

Latest Trends and Future in AI Data Analytics

The advantages of using AI in data analysis are undeniable. This encourages more software providers to add AI features to their data analysis tools to enhance their clients’ work. 

Today, nearly half of the companies surveyed by ThoughtWorks define a data product as something that includes data analytics and AI, rather than merely considering AI as an add-on. This shift in perspective shows that many data analysis firms now see AI as a crucial component of their services. 

As this trend continues, we can see current developments in AI data analytics tools that will influence their future direction.

The Rise of Generative AI

Since its introduction in November 2022, no one might think ChatGPT can become the fastest-growing tech in the history of web apps. This has led to the later development of Generative AI, or GenAI. This AI model helps detect patterns in existing data and generate new content based on given prompts. 

Precedence Research reports that the global market GenAI in analytics was valued at USD 724.5 million in 2022 and is expected to grow with a CAGR of 28.51% from 2023 to 2032. 

The main driver behind this growth is its standout features (e.g., data accessibility). In the recent AWS survey, 80% of the companies also said that GenAI will revolutionize their organizations. So in the future, this demand for GenAI in data analytics will increase to ensure fast, accurate decision-making.

Despite the high level of enthusiasm for GenAI, it has yet to deliver significant value. Of course, the production deployment of GenAI will require more than just experiments; it’ll require additional investment and organizational change. Also in the AWS survey, 93% of the companies agreed that a data strategy is critical to deriving value from GenAI. Therefore, companies still have a long way to harness the full power of this tech for data analytics.

Focus on Data Security & Accuracy

McKinsey’s survey revealed that businesses identify inaccuracy and cybersecurity as the two most significant risks associated with AI. Indeed, AI solutions that generate data or output often lack thorough examination and validation. This oversight frequently results in subpar outcomes, including erroneous, inapplicable, and questionable results. Besides, if the training data is biased, AI models may deliver inaccurate outputs.

Many companies are realizing these problems and attempting to mitigate them. The proof is half of the companies today are reducing the risks of inaccuracy and cybersecurity arising from AI during data analytics. Looking ahead, this will remain a significant concern, and businesses will implement more measures to ensure the accuracy and security of AI data analytics tools.

Top 10 Leading AI Data Analytics Tools in 2024

As we navigate through the era of digital transformation, AI data analytics tools stand at the forefront, revolutionizing the way businesses operate. 

So what tools are worth your investment? This list will provide you with an in-depth look at the best ones in 2024.

Microsoft Power BI

Microsoft Power BI is a software suite that has business intelligence capabilities to turn your unrelated sources of data into consistent and interactive insights. It provides a consolidated view of essential business data, connects to numerous data sources, simplifies data preparation, and facilitates ad hoc analysis. Power BI generates attractive reports and publishes them for your organization to access on the web and mobile devices.

Key Features

• Copilot: Swiftly generate comprehensive reports based on your prompts. Visually present data summaries, create DAX (Data Analysis Expressions) experiences, and provide straightforward answers.
• Microsoft 365 Integration: Allow your organization to share insights across Office 365 or Microsoft Teams, enabling everyone to make data-driven decisions.
• Microsoft Fabric Integration: Revolutionize your organization’s data management with Microsoft Fabric. Allow Power BI users to integrate their workflows with version control systems like Git, fostering efficient team collaboration.

Pricing Plans

• Free Account: Allow free signup and offer the power of interactive, visually rich reports at no cost.
• Power BI Pro: For just USD 10.00 per user/month, access shared Power BI reports and publish your own for a broader impact.
• Power BI Premium Per User: Starting at USD 20.00 per user/month, license specific users with features designed for enterprise-scale needs.
• Power BI in Microsoft Fabric: Allow report sharing with those having no Pro License through Power BI Premium Capacity or access to Microsoft Fabric workloads. Pricing details vary.

Tableau

Tableau is a robust data visualization tool utilized in the Business Intelligence industry. It helps convert raw data into a format that’s easy to understand without any technical skills or coding knowledge.

Key Features

• Einstein Copilot: Democratize data analytics across your organization and make insights more comprehensible at every level. Enable admins to confidently leverage AI-powered experiences, all while maintaining stringent data security.
• Tableau Pulse: Equip every employee with intelligent, personalized, and contextual insights delivered in their workflow.
• Tableau Mobile: Enable you to monitor your metrics on the go and explore personalized data insights.
• Dynamic Axis Ranges: Allow axis ranges of your charts to change dynamically based on the values in your workbook. This then enables more dynamic visualizations when your data often changes.
• Multi-row Calculations in Tableau Prep: Carry out table computations during your data preparation phase. 

Pricing Plans

• Tableau Creator: This plan offers such products as Tableau Desktop, Tableau Pulse, Tableau Prep Builder, and Tableau Cloud (with one Creator license). Start with USD 75 per user/month if you’re billed annually.
• Tableau Explorer: This plan offers such products as Tableau Pulse and Tableau Cloud (with one Creator license). Start with USD 42 per user/month if you’re billed annually.
• Tableau Viewer: This plan allows you to access visualizations in a safe platform. Start with USD 15 per user/month if you’re billed annually.

IBM Watson Studio

As one of the best AI data analytics tools, IBM Watson Studio empowers you to build, execute, and administer AI models to deliver insightful decisions anywhere on IBM Cloud Pak for Data. It integrates open-source frameworks (e.g., TensorFlow or PyTorch) with IBM and its ecosystem tools to implement data analytics. 

Key Features

• Streamlined Experimentation with AutoAI: Experience the power of automated model pipeline construction. From data preparation to model type selection, and from generation to ranking of model pipelines, AutoAI accelerates your experimentation process.
• Advanced Data Refinery: Use an intuitive graphical flow editor to clean your data. Leverage interactive templates to code operations, functions, and logical operators seamlessly.
• Visual Tooling Integration: With IBM SPSS Modeler in Watson Studio, prepare your data swiftly and develop models visually.
• Decision Optimization Embedded: Merge predictive and prescriptive models to optimize decisions using predictions. Create and modify models in Python, OPL, or even with natural language.

Pricing Plans

IBM offers two Watson Studio versions, including:

• IBM Watson Studio on IBM Cloud Pak for Data: This version allows for various licensing options and platform deployment on private or public cloud. Its price isn’t public, so you need to contact IBM for further information.
• IBM Watson Studio on IBM Cloud Pak for Data: This one offers a set of fully managed services you may access on the IBM public cloud. It has two pricing plans: Lite – a free one for only one user & Premium – a paid one for unlimited users at USD 1.02 USD per CUH (Capacity Unit Hour).

MonkeyLearn

MonkeyLearn is a cloud-based text analytics tool that extracts valuable data from various text sources (e.g., emails or surveys) and transforms it into personalized visualizations. The platform provides insights and workflow automation for support, product, customer experience (CX), and development teams, and can be integrated into in-house applications. 

Key Features

• Pre-Built & Custom Machine Learning Models: Opt for pre-configured models or tailor your own for your needs. 
• Fast Data Visualizations & In-depth Insights: Uncover immediate insights as you analyze your data and customize visualizations based on your preferences.
• Text Analytics with Available Business Templates: Explore a wide range of templates, each designed for unique business contexts and equipped with ready-to-use text analysis models and dashboards. Simply integrate with your apps and BI tools using our native integrations, SQL connection, or API.
• Integration With External Sources: Seamlessly connect with multiple apps like Zendesk, Medallia, and Google Sheets to conduct data analytics and automate business workflows.

Pricing Plans

• Team: Start with USD 299 per month for 10K queries.
• Business: The price depends on your business needs. So, contact MonkeyLearn for further information.

Akkio

Akkio is a Generative Business Intelligence (BI) tool revolutionizing how we work with data. Now anyone can harness the power of AI and machine learning to chat with live data, automatically generate charts and dashboards, and precisely forecast key business outcomes with next-generation speed and accuracy.

Key Features

• AI Capabilities: Incorporate all data sources (e.g., MongoDB, Amazon Redshift, or PostgreSQL) and automatically update data. Provide predictive models to build instantly strategic, actionable insights from your data and deliver auto-updated reports to clients.
• Chat Explore: Generate insightful answers from the dataset in real-time with the support of the built-in GPT-4 chat.
• Generative Reports: Automatically build instant reports right after you connect your data with Akkio and describe the issue or app you’re working on. These reports offer solutions for marketing spend optimization, sales forecasting, and more. 
• Chat Data Prep: Use prompts to clean data, reformat dates, combine columns, and more without the need for coding.

Pricing Plans

Akkio’s pricing packages are designed for three different user groups:

• Users: This plan provides team members with two options: Basic at USD 49 per user/month & Professional at USD 99 per user/month. 
• Organization Add-Ons: This plan offers custom add-ons to advance your company’s capabilities through two options. They include Build-On Package at USD 999 per business/month & Dedicated Server at USD 1,999 per business/month.
• Data Package Add-Ons: This plan allows all businesses to access 1M rows of connected data & 100K predictions for free per month. The more rows and predictions your business requires, the more you pay. 

Altair RapidMiner

Altair RapidMiner is one of the leading AI data analytics tools in 2024. It provides a pathway to modernization for established data analytics teams and a pathway to automation for teams that are just beginning their data journey. 

Key Features

• Automated Data Mining & Conversion: Automate the process of extracting and transforming your data into more accurate datasets within seconds. Remove error-prone, repetitive tasks and enhance tasks that add more value to your business.
• Data & Machine Learning Workflows: Swiftly create, train, evaluate, and deploy predictive & prescriptive models for your business data.
• Real-time Data Processing & Visualization: Instantly detect abnormal signals, trends, and outliers. Then use powerful, visual dashboards to present insights to all stakeholders across your business. This feature, accordingly, allows for quick, fully informed decisions based on vast amounts of rapidly changing inputs.

Pricing Plans

• Professional: Start at USD 7,500 per user/month on the cloud.
• Enterprise: Start at USD 15,000 per user/month on the cloud.
• AI Hub: Start at USD 54,000 per user/month on the cloud.

Dataiku

Dataiku is a comprehensive platform designed to facilitate the development, deployment, and management of data and AI projects across an organization. It serves as a collaborative workspace that brings together both technical and non-technical team members, enabling them to work together effectively on data projects.

Key Features

• Data Prep: Provide a wide set of data transformation, AI-based data prep, and other functions to connect, prepare, and clean data for analytics at any scale.
• Visualization & Analytics Apps: Offer self-service analytics that involves no-code apps, central workspaces, and dashboards. These services enable your analytics teams to share insights & data outputs with ease.
• Visual AutoML: Support your teams to build co-code ML models and explain model behavior through built-in explainability functions.
• GenAI Capability: Empower you to create enterprise-level, secure GenAI apps for your data projects.
• Extensibility: Enhance the platform’s native capabilities with plugins and custom applications. This then allows you to tailor Dataiku to your specific requirements.

Pricing Plans

Dataiku offers a free plan and three paid ones. But the company doesn’t specify the prices. These plans include:

• Free Edition: This plan is free forever and is suitable for up to 3 users. It allows you to install the platform on your infrastructure and access files or open-source databases.
• Discover Plan: This plan is designed for small teams of up to 5 users. It includes 20+ database connectors plus limited automation.
• Business Plan: This plan is intended for mid-sized teams of up to 20 users. It offers unlimited computations with Kubernetes, full automation, and advanced security.
• Enterprise Plan: This plan offers scalable automation and governance. It includes all database connectors, full deployment capabilities, an isolation framework, and unlimited resource governance.

Sisense

Sisense is one of the best AI data analytics tools in the world. It provides a unique business intelligence and analytics platform that helps users connect different data sources and perform complex queries in seconds. 

Key Features

• GenAI Chatbot: Suggest where to begin your data exploration, enhance analytics, request data, and receive answers in a natural language. Build your custom, LLM-powered Chatbot by using React Components & APIs to mix and match built-in GenAI building blocks.
• Actionable Intelligence: Integrate intelligence into your business’s workflows. Its goal is to facilitate data-driven decision-making by enabling all users to manage, visualize, and interpret their data within a single platform.
• Scalability: Allow the scaling of analytics across the organization with easy sharing of insights through dashboards.
• Sisense Quest: Enable you to implement sophisticated analytic models and then incorporate actions that can be executed by viewers directly from the dashboard. 

Pricing Plans

Sisense offers a flexible pricing plan based on your business’s requirements. So, you need to contact the company for further information. 

Polymer

Polymer is among the best AI data analytics tools known for its simplicity and user-friendliness. As a Business Intelligence (BI) platform, it eliminates the need for complex configurations and extensive learning periods. With Polymer, users can effortlessly create visual representations, construct dashboards, and professionally present their data.

Key Features

• Dashboard Creator: Empower you to craft personalized dashboards swiftly without coding or design expertise. Also, this feature provides a user-friendly drag-and-drop dashboard builder with a variety of chart types and flexible filter options.
• Polymer AI: Automate the process of data analysis and presentation of relevant insights. 
• Embedded Analytics: Allow the integration of data analysis directly into your business apps and software, making analytics and reporting an integral part of the user experience. With Polymer’s Embedded Analytics, you can create visually appealing charts and graphs that integrate seamlessly into your own apps and services.

Pricing Plans

• Starter: Starting with USD 25/month (billed annually), this plan is for 1 editor and 1 account per data connector.
• Pro: Starting with USD 50/month (billed annually), this plan is for 1 editor and 5 accounts per data connector.
• Teams: Starting with USD 125/month (billed annually), this plan is for 3 editors and 15 accounts per data connector.
• Enterprise: This plan is for a custom number of editors and unlimited accounts per data connector. So you need to contact the company for further information.

KNIME

KNIME is a complete platform for data analytics and science. This tool bridges the gap between dashboards and advanced analytics through an intuitive interface that is suitable for anyone working with data. It empowers more business experts to be self-sufficient and more data experts to push the business to the forefront of modern data science, integrating the latest AI and Machine Learning techniques.

Key Features

• KNIME AI Assistant (K-AI): Autonomously generate analytics workflows based on your instructions. 
• LLM Customization: Allow you to integrate and customize LLMs using your own data. This enables you to build AI-powered apps tailored to your specific use cases.
• AI Coding Assistant: This feature automatically generates Python scripts and visualizations, simplifying the handling of complex tasks.
• User-Friendly Interface: Its interface is designed to be easy to use, requiring little to no coding. This makes it suitable for users with different levels of programming skills.

Pricing Plans

KNIME offers two options based on your place of data management and deployment.

• KNIME Community Hub: This plan is for those managing data on KNIME. It offers two pricing plans, including a free one for individuals and a paid one for teams at USD 99 per month.
• KNIME Business Hub: This plan is for those installing the platform on their business infrastructure. It offers three pricing plans, including Basic for small teams with business needs at USD 39,900 per year, Standard for teams with many solutions at USD 71,250 per year, and Enterprise for enterprise-wide deployment.

Conclusion

This article unpacked the world of AI data analytics, explored exciting future trends, and introduced you to the top 10 tools that are changing the game in 2024. Now it’s your turn to set sail on your data adventure!  With the right AI data analytics tools at your side, you can unearth hidden insights and make discoveries that propel your business forward. So, dive in and start exploring these amazing tools today!

The post 10 AI Data Analytics Tools Transforming the Industry in 2024 appeared first on Designveloper.