Data management is essential for businesses because it keeps data accurate, easy to access, and secure. Good data management helps businesses make better decisions based on reliable information. It also makes operations faster by reducing errors and duplicate work.
With well-organized data, businesses can understand customer needs and improve their products and services. Proper data management also helps companies meet legal rules and avoid fines.
What is data management?
In 2025, data management is a complex process. It includes collecting, storing, organizing, and using data. The process should ensure that data is accurate, easy to access, and properly protected. Data helps companies make decisions as well as meet legal and security requirements.
History of data management in business
The way businesses see data management has changed a lot over time.
It all started with the record-keeping of proto-civilizations, but let’s get to nearer eras. Data management as we know it today started in the 1960s and 1970s. At that time, companies relied on early computers and punch cards to handle basic tasks like tracking inventory or finances. However, in the 70s, relational databases were introduced, and they instantly made storing and retrieving data easier, setting the stage for modern systems.
In the 1980s, personal computers became common in offices, and companies started using data as a resource – they built systems to store and analyze data from different departments.
The 1990s saw a big shift as businesses started to collect large amounts of data from websites. At that time, ERP systems brought all business data together in one place, so it became easier to manage.
In the 2000s, the growth of the internet, social media, and mobile phones created huge amounts of data. Companies started to see this “big data
Big data is a massive amount of information that is too large and complex for traditional data-processing application software to handle. Think of it as a constantly flowing firehose of data, and you need special tools to manage and understand it.
Big data definition in simple words
Big data encompasses structured, unstructured, and semi-structured data that grows exponentially over time. It can be analyzed to uncover valuable insights and inform strategic decision-making.
The term often describes data sets characterized by the "three Vs": Volume (large amounts of data), Velocity (rapidly generated data), and Variety (diverse data types).
How does big data work?
Big data is processed through a series of stages.
Data generation → Data is produced from sources, including social media, sensors, transactions, and more.
Data capture → This involves collecting data and storing it in raw format.
Data storage → Data is stored in specialized data warehouses or data lakes designed to handle massive volumes.
Data processing → Raw data is cleaned, transformed, and structured to make it suitable for analysis.
Data analysis → Advanced analytics tools and techniques, like machine learning and artificial intelligence, are applied to extract valuable insights and patterns.
Data visualization → Results are presented in visual formats like graphs, charts, and dashboards for easy interpretation.
What are the key technologies used in big data processing?
Big data processing relies on a combination of software and hardware technologies. Here are some of the most prominent ones.
Data storage
Hadoop Distributed File System (HDFS). Stores massive amounts of data across multiple nodes in a distributed cluster.
NoSQL databases. Designed for handling unstructured and semi-structured data, offering flexibility and scalability.
Data processing
Apache Hadoop. A framework for processing large datasets across clusters of computers using parallel processing.
Apache Spark. A fast and general-purpose cluster computing framework for big data processing.
MapReduce. A programming model for processing large data sets with parallel and distributed algorithms.
Data analysis
SQL and NoSQL databases. For structured and unstructured data querying and analysis.
Data mining tools. For discovering patterns and relationships within large data sets.
Machine learning and AI. For building predictive models and making data-driven decisions.
Business intelligence tools. For data visualization and reporting.
What is the practical use of big data?
Big data has revolutionized the way businesses operate and make decisions. In business, it helps with customer analytics, marketing optimization, fraud detection, supply chain management, and risk management. But that’s not all!
Big data in healthcare
Analyzing data helps identify potential disease outbreaks and develop prevention strategies. It became an important tool for virologists and immunologists, who use data to predict not only when and what kind of disease can outbreak, but also the exact stamm of a virus or an infection.
Big data helps create personalized medicine by tailoring treatments based on individual patient data. It also accelerates the drug development process by analyzing vast amounts of biomedical data.
Big data for the government
Big data can help create smart cities by optimizing urban planning, traffic management, and resource allocation. It can help the police to analyze crime patterns and improve policing strategies and response times. For disaster-prone regions, big data can help predict and respond to natural disasters.
Essentially, big data has the potential to transform any industry by providing insights that drive innovation, efficiency, and decision-making. That includes
finance (fraud detection, risk assessment, algorithmic trading),
manufacturing (predictive maintenance, quality control, supply chain optimization),
energy (smart grids, energy efficiency, demand forecasting), and even
agriculture (precision agriculture, crop yield prediction, and resource optimization).
What kinds of specialists work with big data?
The world of big data requires a diverse range of professionals to manage and extract value from complex datasets. Among the core roles are Data Engineers, Data Scientists, and Data Analysts. While these roles often intersect and collaborate, they have distinct responsibilities within big data.
Data engineers focus on building and maintaining the infrastructure that supports data processing and analysis. Their responsibilities include:
Designing and constructing data pipelines.
Developing and maintaining data warehouses and data lakes.
Ensuring data quality and consistency.
Optimizing data processing for performance and efficiency.
They usually need strong programming skills (Python, Java, Scala) and be able to work with database management, cloud computing (AWS, GCP, Azure), data warehousing, and big data tools (Hadoop, Spark).
A data analyst’s focus is on extracting insights from data to inform business decisions. Here’s exactly what they’re responsible for:
Collecting, cleaning, and preparing data for analysis.
Performing statistical analysis and data mining.
Creating visualizations and reports to communicate findings.
Collaborating with stakeholders to understand business needs.
Data analysts should be pros in SQL, data visualization tools (Tableau, Power BI), and statistical software (R, Python).
Data scientists apply advanced statistical and machine-learning techniques to solve complex business problems. They do so by:
Building predictive models and algorithms.
Developing machine learning pipelines.
Experimenting with new data sources and techniques.
Communicating findings to technical and non-technical audiences.
Data scientists need strong programming skills (Python, R), knowledge of statistics, machine learning, and data mining, and a deep understanding of business problems.
In essence, Data Engineers build the foundation for data analysis by creating and maintaining the data infrastructure. Data Analysts focus on exploring and understanding data to uncover insights, while Data Scientists build predictive models and algorithms to solve complex business problems. These roles often work collaboratively to extract maximum value from data.
Along with this trio, there are also other supporting roles. A Data Architect will design the overall architecture for big data solutions. A Database Administrator will manage and maintain databases. A Data Warehouse Architect will design and implement data warehouses. A Business Analyst will translate business needs into data requirements. These roles often overlap and require a combination of technical and business skills. As the field evolves, new roles and specializations are also emerging.
What is the future of big data?
The future of big data is marked by exponential growth and increasing sophistication. These are just some of the trends we should expect in 2024 and beyond.
Quantum computing promises to revolutionize big data processing by handling complex calculations at unprecedented speeds.
Processing data closer to its source will reduce latency and improve real-time insights.
AI and ML will become even more integrated into big data platforms, enabling more complex analysis and automation.
As data becomes more valuable, regulations like GDPR and CCPA will continue to shape how data is collected, stored, and used.
Responsible data practices, including bias detection and mitigation, will be crucial.
Turning data into revenue streams will become increasingly important.
The demand for skilled data scientists and analysts will continue to outpace supply.
Meanwhile, big data is not without its challenges. Ensuring its accuracy and consistency will remain a challenge and an opportunity for competitive advantage.
” as a valuable asset. New tools like Hadoop allowed businesses to handle very large datasets for the first time.
By the 2010s, cloud computing
Cloud computing is the delivery of computing services, including servers, storage, databases, networking, software, analytics, and more, over the internet (the cloud) to offer faster innovation, flexible resources, and economies of scale. Cloud computing enables users to access and utilize various IT resources and services on demand without needing to own or manage physical hardware or infrastructure.
Five key characteristics of cloud computing
On-demand self-service. Users can provision and manage computing resources as needed, often through a self-service portal, without requiring human intervention from the service provider.
Broad network access. Cloud services are accessible over the internet from a wide range of devices, including laptops, smartphones, tablets, and desktop computers.
Resource pooling. Cloud providers pool and allocate resources dynamically to multiple customers. Resources are shared among users but are logically segmented and isolated.
Rapid elasticity. Cloud resources can be rapidly scaled up or down to accommodate changes in demand. This scalability ensures that users can access the resources they need without overprovisioning or underutilization.
Measured service. Cloud usage is often metered and billed based on actual usage, allowing users to pay for only the resources they consume. This "pay-as-you-go" model offers cost efficiency and flexibility.
Service models of cloud computing
There are three primary service models of cloud computing: IaaS, PaaS, and SaaS. Let’s break them down.
IaaS
Infrastructure as a Service provides virtualized computing resources over the internet. Users can access virtual machines, storage, and networking components, allowing them to deploy and manage their software applications and services.
Description: IaaS provides users with virtualized computing resources over the internet. These resources typically include virtual machines, storage, and networking components. Users can provision and manage these resources on demand, giving them control over the underlying infrastructure.
Use Cases: IaaS is suitable for users who need flexibility and control over their computing environment. It's commonly used for hosting virtual servers, running applications, and managing data storage.
Examples: Amazon Web Services (AWS) EC2, Microsoft Azure Virtual Machines, Google Cloud Compute Engine.
PaaS
Platform as a Service offers a higher-level development and deployment environment. It includes tools and services for building, testing, deploying, and managing applications. Developers can focus on writing code while the platform handles infrastructure management.
Description: PaaS offers a higher-level development and deployment environment that abstracts much of the underlying infrastructure complexity. It includes tools, services, and development frameworks that enable users to build, test, deploy, and manage applications without worrying about the infrastructure.
Use Cases: PaaS is ideal for developers who want to focus solely on coding and application logic without managing servers or infrastructure. It accelerates application development and deployment.
Examples: Heroku, Google App Engine, and Microsoft Azure App Service.
SaaS
Software as a Service delivers fully functional software applications over the internet. Users can access and use software applications hosted in the cloud without the need for installation or maintenance. Common examples include email services, customer relationship management (CRM) software, and office productivity suites.
Description: SaaS delivers fully functional software applications over the internet. Users can access and use these applications through a web browser without the need for installation or maintenance. SaaS providers handle everything from infrastructure management to software updates.
Use Cases: SaaS is widely used for various business applications, including email, collaboration tools, customer relationship management (CRM), human resources management, and more.
Examples: Salesforce, Microsoft 365 (formerly Office 365), Google Workspace, Dropbox.
These three cloud computing service models represent a spectrum of offerings, with IaaS providing the most control over infrastructure and SaaS offering the highest level of abstraction and simplicity for end-users. Organizations can choose the service model that best aligns with their specific needs, resources, and expertise.
How are cloud services hosted and delivered?
Public Cloud. Services are offered to the general public by cloud providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Resources are shared among multiple customers.
Private Cloud. Cloud infrastructure is exclusively used by a single organization. It can be hosted on-premises or by a third-party provider. Private clouds offer more control and customization options.
Hybrid Cloud. A combination of public and private clouds, allowing data and applications to be shared between them. Hybrid clouds provide flexibility, enabling organizations to leverage the scalability of public clouds while maintaining sensitive data on private infrastructure.
Multi-Cloud. Companies use services from multiple cloud providers to avoid vendor lock-in and exploit each provider's strengths. Multi-cloud strategies often involve managing resources and applications across various cloud environments.
Cloud computing providers
These are some of the most popular and widely recognized cloud computing providers.
Amazon Web Services (AWS)
AWS is one of the largest and most widely used cloud service providers globally. It offers a vast array of cloud services, including computing, storage, databases, machine learning, and analytics
Notable services: Amazon EC2 (Elastic Compute Cloud), Amazon S3 (Simple Storage Service), AWS Lambda, Amazon RDS (Relational Database Service).
Website: AWS
Microsoft Azure
Azure is Microsoft's cloud computing platform, providing a comprehensive suite of cloud services, including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS).
Notable services: Azure Virtual Machines, Azure App Service, Azure SQL Database, Azure AI and Machine Learning.
Website: Microsoft Azure
Google Cloud Platform (GCP)
GCP offers cloud services for computing, data storage, machine learning, and data analytics. Google's expertise in data and AI is a standout feature of GCP.
Notable services: Google Compute Engine, Google Kubernetes Engine (GKE), BigQuery, Google Cloud AI Platform.
Website: Google Cloud
IBM Cloud
IBM Cloud provides cloud computing and AI services with a focus on hybrid and multi-cloud solutions. It offers a variety of cloud deployment options, including public, private, and on-premises.
Notable services: IBM Virtual Servers, Watson AI services, IBM Cloud Object Storage, Red Hat OpenShift on IBM Cloud.
Website: IBM Cloud
Oracle Cloud
Oracle Cloud offers cloud infrastructure and services, including databases, applications, and cloud-native technologies. It is designed to support enterprise workloads and applications.
Notable services: Oracle Cloud Infrastructure (OCI), Oracle Autonomous Database, Oracle Cloud Applications.
Website: Oracle Cloud
Alibaba Cloud
Alibaba Cloud is a leading cloud service provider in Asia and offers a wide range of cloud computing services, data storage, and AI capabilities.
Notable services: Elastic Compute Service (ECS), Alibaba Cloud Object Storage Service (OSS), Alibaba Cloud Machine Learning Platform.
Website: Alibaba Cloud
Salesforce (Heroku)
Salesforce provides a cloud-based platform known for its CRM solutions. Heroku, a subsidiary of Salesforce, is a cloud platform for building, deploying, and managing applications.
Notable services: Salesforce CRM, Heroku Platform as a Service (PaaS).
Website: Salesforce, Heroku
made data storage easier and cheaper. We started to use advanced tools to analyze data and predict customer behavior. Around this time, governments introduced strict rules about how data could be used, which forced companies to focus more on security and governance.
Today, data is a core part of every business. We rely on real-time data to manage operations, understand customers, and prevent fraud. Over the years, data management has grown from a simple operational tool to a critical part of business success.
Key components of data management
When we talk about data, we use some notions and it’s better to get the same vision of the key terms. Here are seven components of data management:
I. Data collection is about gathering information from different sources, like customer surveys, website visits, sales transactions, or sensors in machines. For example, an online store collects data about what customers buy, how often they visit, and which products they look at.
II. Data storage. It means keeping information safe and organized. The storage can happen on physical devices like hard drives or in digital spaces like cloud platforms. A company can store customer details, sales records, and employee data in a secure database. Without proper storage, important information could get lost, damaged, or is hard to find.
III. Data organization means arranging data so it is easy to use. For instance, sort customer information into categories like name, email, and purchase history in a spreadsheet.
IV. Data security is about protecting data from theft, loss, or unauthorized access. To guarantee protection, companies use passwords, encryption, and firewalls. For example, a bank limits access to sensitive customer information to authorized employees only.
V. Data governance is about the accuracy and security of data. It involves rules for how data is collected, stored, shared, and used. Good data governance ensures data is trustworthy, protects against security risks, and keeps the organization compliant with regulations.
VI. Data quality means accuracy, completeness, and reliability of data. It focuses on fixing errors, filling in missing information, and making sure that data is consistent across different systems. Poor data quality can lead to mistakes, like wrong shipments or bad reporting, which can hurt the business. In short, data quality ensures that the information business acquires is correct and useful.
VII. Data integration combines data from various databases, applications, sales platforms, customer service systems, and marketing tools into one system. Without integration, the data would stay separate and hard to analyze or use.
Benefits and challenges of data management
Data management can help you in many ways. Its key benefit is that it gives you reliable information you can base your decisions on. The result? Your overall business efficiency is improved, and customer satisfaction is supported. Another key benefit is meeting legal rules, which avoids fines and protects the company’s reputation. Businesses that use data well often gain an advantage over competitors by spotting trends faster.
But not everything is bright.
Analyzing and managing large amounts of data is not easy at all, especially as businesses grow.
Keeping data secure from hackers or accidental loss is a constant concern.
Combining data from different systems or formats is often complex and requires advanced tools.
Ensuring data stays accurate and up to date can take time and effort.
Finally, complying with strict data laws and regulations requires careful planning and resources.
Despite these challenges, effective data management is essential. You can achieve it with the right team of data management specialists.
Who provides data management for business?
Data management consists of multiple components, and the further we go into the future, the more complex this process becomes. This means, in particular, that your company needs more and more people to deal with data. As of 2025, there are several established roles focusing on different aspects of data management. Here are just some of them.
Database Administrator
They set up and maintain databases. DBAs back up data, fix performance issues, and make sure databases are secure and accessible. These specialists focus on the technical side of storing and retrieving data and work mainly with database systems rather than broader data processes.
Data Analyst
They analyze data to find patterns, trends, and insights. Data analysts work with data to extract meaning, while roles like DBAs focus more on managing and storing the data itself.
Data Engineer
This role appeared in early 2000s, with the rise of big data. Data engineers build systems that collect, store, and process large amounts of data. They design data pipelines to move data from one system to another and prepare it for analysis. Data engineers create the infrastructure for data use, whereas analysts and scientists work with data after it is prepared.
Data Scientist
These people try making predictions and solving complex problems. They do so with statistical modeling and machine learning
Machine learning (ML) is a subset of artificial intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed. It involves the development of algorithms that can analyze and learn from data, making decisions or predictions based on this data.
Common misconceptions about machine learning
ML is the same as AI. In reality, ML is a subset of AI. While AI is the broader concept of machines being able to carry out tasks in a way that we would consider “smart,” ML is a specific application of AI where machines can learn from data.
ML can learn and adapt on its own. In reality, ML models do learn from data, but they don't adapt or evolve autonomously. They operate and make predictions within the boundaries of their programming and the data they are trained on. Human intervention is often required to update or tweak models.
ML eliminates the need for human workers. In reality, while ML can automate certain tasks, it works best when complementing human skills and decision-making. It's a tool to enhance productivity and efficiency, not a replacement for the human workforce.
ML is only about building algorithms. In reality, algorithm design is a part of ML, but it also involves data preparation, feature selection, model training and testing, and deployment. It's a multi-faceted process that goes beyond just algorithms.
ML is infallible and unbiased. In reality, ML models can inherit biases present in the training data, leading to biased or flawed outcomes. Ensuring data quality and diversity is critical to minimize bias.
ML works with any kind of data. In reality, ML requires quality data. Garbage in, garbage out – if the input data is poor, the model's predictions will be unreliable. Data preprocessing is a vital step in ML.
ML models are always transparent and explainable. In reality, some complex models, like deep learning networks, can be "black boxes," making it hard to understand exactly how they arrive at a decision.
ML can make its own decisions. In reality, ML models can provide predictions or classifications based on data, but they don't "decide" in the human sense. They follow programmed instructions and cannot exercise judgment or understanding.
ML is only for tech companies. In reality, ML has applications across various industries – healthcare, finance, retail, manufacturing, and more. It's not limited to tech companies.
ML is a recent development. In reality, while ML has gained prominence recently due to technological advancements, its foundations were laid decades ago. The field has been evolving over a significant period.
Building blocks of machine learning
We can state that machine learning consists of certain blocks, like algorithms and data. What is their role exactly?
Algorithms are the rules or instructions followed by ML models to learn from data. They can be as simple as linear regression or as complex as deep learning neural networks. Some of the popular algorithms include:
Linear regression – used for predicting a continuous value.
Logistic regression – used for binary classification tasks (e.g., spam detection).
Decision trees – A model that makes decisions based on branching rules.
Random forest – An ensemble of decision trees typically used for classification problems.
Support vector machines – Effective in high dimensional spaces, used for classification and regression tasks.
Neural networks – A set of algorithms modeled after the human brain, used in deep learning for complex tasks like image and speech recognition.
K-means clustering – An unsupervised algorithm used to group data into clusters.
Gradient boosting machines – Builds models in a stage-wise fashion; it's a powerful technique for building predictive models.
An ML model is what you get when you train an algorithm with data. It's the output that can make predictions or decisions based on new input data. Different types of models include decision trees, support vector machines, and neural networks.
What’s the role of data in machine learning?
Data collection. The process of gathering information relevant to the problem you're trying to solve. This data can come from various sources and needs to be relevant and substantial enough to train models effectively.
Data processing. This involves cleaning and transforming the collected data into a format suitable for training ML models. It includes handling missing values, normalizing or scaling data, and encoding categorical variables.
Data usage. The processed data is then used for training, testing, and validating the ML models. Data is crucial in every step – from understanding the problem to fine-tuning the model for better accuracy.
Tools and technologies commonly used in ML
Python and R are the most popular due to their robust libraries and frameworks specifically designed for ML (like Scikit-learn, TensorFlow, and PyTorch for Python).
Data Analysis Tools: Pandas, NumPy, and Matplotlib in Python are essential for data manipulation and visualization.
Machine Learning Frameworks: TensorFlow, PyTorch, and Keras are widely used for building and training complex models, especially in deep learning.
Cloud Platforms: AWS, Google Cloud, and Azure offer ML services that provide scalable computing power and storage, along with various ML tools and APIs.
Big Data Technologies: Tools like Apache Hadoop and Spark are crucial when dealing with large datasets that are typical in ML applications.
Automated Machine Learning (AutoML): Platforms like Google's AutoML provide tools to automate the process of applying machine learning to real-world problems, making it more accessible.
Three types of ML
Machine Learning (ML) can be broadly categorized into three main types: Supervised learning, Unsupervised learning, and Reinforcement learning. Let's explore them with examples
Supervised learning
In supervised learning, the algorithm learns from labeled training data, helping to predict outcomes or classify data into groups. For example:
Email spam filtering. Classifying emails as “spam” or “not spam” based on distinguishing features in the data.
Credit scoring. Assessing credit worthiness of applicants by training on historical data where the credit score outcomes are known.
Medical diagnosis. Using patient data to predict the presence or absence of a disease.
Unsupervised learning
Unsupervised learning involves training on data without labeled outcomes. The algorithm tries to identify patterns and structures in the data. Real-world examples:
Market basket analysis. Identifying patterns in consumer purchasing by grouping products frequently bought together.
Social network analysis. Detecting communities or groups within a social network based on interactions or connections.
Anomaly detection in network traffic. Identifying unusual patterns that could signify network breaches or cyberattacks.
Reinforcement learning
Reinforcement learning is about taking suitable actions to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path in a specific context. These are some examples:
Autonomous vehicles. Cars learn to drive by themselves through trial and error, with sensors providing feedback.
Robotics in manufacturing. Robots learn to perform tasks like assembling with increasing efficiency and precision.
Game AI. Algorithms that learn to play and improve at games like chess or Go by playing numerous games against themselves or other opponents.
How do we use ML in real life?
Predictive analytics is used in sales forecasting, risk assessment, and customer segmentation.
Customer service. Chatbots and virtual assistants powered by ML can handle customer inquiries efficiently.
Fraud detection. ML algorithms can analyze transaction patterns to identify and prevent fraudulent activities.
Supply chain optimization. Predictive models can forecast inventory needs and optimize supply chains.
Personalization. In marketing, ML can be used for personalized recommendations and targeted advertising.
Human resources. Automating candidate screening and using predictive models to identify potential successful hires.
Predicting patient outcomes in healthcare
Researchers at Beth Israel Deaconess Medical Center used ML to predict the mortality risk of patients in intensive care units. By analyzing medical data like vital signs, lab results, and notes, the ML model could predict patient outcomes with high accuracy.
This application of ML aids doctors in making critical treatment decisions and allocating resources more effectively, potentially saving lives.
Fraud detection in finance and banking
JPMorgan Chase implemented an ML system to detect fraudulent transactions. The system analyzes patterns in large datasets of transactions to identify potentially fraudulent activities.
The ML model helps in reducing financial losses due to fraud and enhances the security of customer transactions.
Personalized shopping experiences in retail
Amazon uses ML algorithms for its recommendation system, which suggests products to customers based on their browsing and purchasing history.
This personalized shopping experience increases customer satisfaction and loyalty, and also boosts sales by suggesting relevant products that customers are more likely to purchase.
Predictive maintenance in manufacturing
Airbus implemented ML algorithms to predict failures in aircraft components. By analyzing data from various sensors on planes, they can predict when parts need maintenance before they fail.
This approach minimizes downtime, reduces maintenance costs, and improves safety.
Precision farming in agriculture
John Deere uses ML to provide farmers with insights about planting, crop care, and harvesting, using data from field sensors and satellite imagery.
This information helps farmers make better decisions, leading to increased crop yields and more efficient farming practices.
Autonomous driving in automotive
Tesla's Autopilot system uses ML to enable semi-autonomous driving. The system processes data from cameras, radar, and sensors to make real-time driving decisions.
While still in development, this technology has the potential to reduce accidents, ease traffic congestion, and revolutionize transportation.
. Data scientists often work with unstructured data, like text or images, and focus on advanced analysis and predictions. Scientists create algorithms, while analysts often stick to descriptive or diagnostic analysis.
Data Governance Specialist
The role appeared in the early 2010s, alongside the rise of data regulations like GDPR. They ensure that data use complies with laws and company policies. They create rules for handling data and monitor compliance to protect data privacy and security.
What are the key differences between the roles?
Focus. Some roles, like DBAs and data engineers, focus on infrastructure and systems. Others, like analysts and scientists, work directly with data to extract insights.
Skills. Engineers and scientists need coding and technical skills, while analysts may focus more on interpreting data and creating visuals.
Scope. Leadership roles oversee the entire data strategy, while technical roles like DBAs or engineers work on specific tasks.
What tools and technologies are used for data management?
Here are some of the most popular and widely used data management tools and technologies:
Microsoft SQL Server. A highly popular RDBMS. Supports both transactional and analytical workloads.
Oracle Database. Known for handling large-scale operations.
MySQL. An open-source RDBMS commonly used for web applications.
PostgreSQL. Known for its scalability and strong compliance with SQL standards.
Apache Hadoop. Essential for big data management and used by companies that handle large datasets.
Tableau. A popular data visualization tool.
Apache Spark. Often used with Hadoop for real-time data processing.
AWS. Offers cloud storage (S3), databases (RDS, Redshift), and big data solutions (EMR, Athena).
Google Cloud Platform. Offers tools like BigQuery and Cloud Storage.
Snowflake. Popular for data warehousing and analytics.
Datadog. A monitoring and analytics platform widely used by IT teams.
How much do data management specialists make?
Below are the average annual salaries for mid-level data analysts and engineers in different countries. They are very approximate and can vary on many factors. Hopefully, these numbers will provide a broad picture of direct labor costs for data management specialists worldwide.
Data Analyst
United States: $110,000
United Kingdom: $85,000
Israel: $50,000
Brazil: $25,000
Ukraine: $15,000
India: $12,000
Vietnam: $10,000
Data Engineer
United States: $125,000
United Kingdom: $90,000
Israel: $55,000
Brazil: $25,000
Ukraine: $20,000
India: $14,000
Vietnam: $12,000
The salaries of data management specialists are quite high in developed countries like the USA, Israel, and the United Kingdom. However, in regions with a lower cost of living, you can find specialists as qualified as in the US but at a fraction of the price. Hire data management specialists abroad with staff augmentation services and cut costs on your data management.
Book a call with MWDN to find out more about staff augmentation and the services we provide.
