Big Data Analytics in Business: Applications and Key Use Cases
Business Challenges Addressed with Data Analytics
Big data analytics is applied differently across industries, but there are several common areas of application:
- Process Optimization. Understanding business intelligence in big data is crucial for making data-driven decisions. This helps organizations identify bottlenecks in supply chains, production processes, workforce management, and more.
- Trend and Demand Forecasting. Market changes can be predicted by analyzing various sources: sales data, search queries, and economic indicators. Predictive analytics goes beyond simply responding to demand fluctuations; it allows businesses to proactively forecast them.
- Product and Service Development. Big data analytics can be used to generate ideas for new products, test hypotheses, and analyze the market and competitors. This reduces time-to-market and lowers the risk of failure when launching new products.
- Marketing Effectiveness Enhancement. Traditional promotional tools no longer deliver the same results as before. Data takes center stage, enabling the creation of detailed customer profiles, precise audience segmentation, and personalized offers.
- Risk Management and Security. Companies can anticipate potential threats ranging from financial risks to equipment failures. Forecasting and timely prevention of problems helps minimize losses.
Key Metrics for Big Data Application in Business.
Source: The Global State of CX 2024, Data and AI Leadership Executive Survey (NewVantage Partners),
Over 100 Data, Analytics and AI Predictions Through 2030 (Gartner)
These are just a few applications of big data, but implementing even one of these can yield significant business benefits.
How Big Data is Used In Business: Real-World Examples from Major Players
Let's look at examples of how market giants from telecommunications and financial sectors use machine learning and artificial intelligence.
Telecommunications
Quality of Service (QoS) Management. Operators use big data analytics to improve their customer service. For example, Telefonica, a Spanish telecom operator, analyzes network traffic and calls to identify areas with problematic connection quality. This enables quick response to issues, increasing customer satisfaction and reducing complaints.
Marketing Campaign Optimization. By analyzing customer behavior—from consumed content to geolocation—companies can precisely segment their audience to offer the most relevant services. For instance, MegaFon Tajikistan uses the EW DataFlow analytics platform to create detailed, in-depth subscriber profiles, driving increased conversion and ARPU.
Infrastructure Management and Failure Prediction. Telecommunications networks generate enormous amounts of data that can be analyzed for optimization. AT&T uses analytics for various calculations, such as estimating the number of potential consumers in a network segment or identifying base stations that can provide coverage in case other stations fail. Big data analytics in business is a powerful tool that helps predict and eliminate problems, making the system more resilient.
The Scope of Big Data Analytics in Telecom
Source: Kbvresearch, Telcom Analysis Market, 2020
Finance
Fraud Detection. Using big data helps identify suspicious transactions and prevent financial crimes: carding, creation of fake accounts, and phishing. For example, SWIFT international organization has launched pilot projects using AI powered by big data to detect payment fraud. Artificial intelligence can track suspicious activities such as unexpected password changes in personal accounts or attempts to transfer large sums of money.
Credit Scoring. Financial institutions now apply scoring models that consider not only standard parameters like income and credit history but also analytical data: social activity, geolocation, and interests. For instance, fintech company FICO analyzes utility and mobile phone payment history. This enables assessment of customers without credit history, such as young people or recent immigrants.
Customer Churn Prediction. Using big data analytics, banks can study customer behavior, evaluate their satisfaction, and identify those likely to switch to competitors. This allows taking preventive measures to maintain the customer base.
All of these examples of big data application in business demonstrate how companies can leverage data for growth. They can use open-source tools for these use cases, but this requires highly skilled data specialists and can take up to 6 months. Due to the high entry barrier and risk of not recovering investments, companies aren't always ready to implement such initiatives.
To overcome the challenges associated with using open source, organizations acquire ready-made solutions for big data and predictive analytics such as EW DataFlow. Eastwind's low-code platform optimizes distributed computing and reduces errors through automation capabilities.
Furthermore, the Eastwind team supports clients throughout the entire big data infrastructure implementation: we calculate necessary resources, design business solution approaches, and develop models. For example, we helped one CIS bank identify 70% of churning customers in advance.
>> Learn more about the EW DataFlow capabilities
Impact of Analytical Models on Banking Credit Products.
Source: McKinsey & Company, Designing next-generation credit-decisioning models, 2021
Technologies and Tools for Big Data Analysis
To understand the meaning of big data in business, we must examine the technologies that enable it. To work with data about millions of customers and their transactions, reliable solutions are needed for aggregating, storing, processing, and analyzing large volumes of information. Let's explore them in detail.
Data Storage and Processing Platforms
To process massive datasets relatively quickly, it's necessary to distribute the workload across available resources. Apache Hadoop and Apache Spark are two popular open-source systems for large-scale distributed computing.
Hadoop is a framework that uses the HDFS (Hadoop Distributed File System) for data storage and the MapReduce model for processing. It is one of the most popular technologies in Data Science, suitable for working with both structured and unstructured data.
The Hadoop ecosystem consists of multiple tools, approximately 15 in total
For convenient work with Hadoop clusters, companies use ready-made solutions, including the low-code platform EW DataFlow. It enables aggregating data from multiple sources into a single Data Lake, configuring ETL/ELT processes, developing ML models, scheduling jobs, and optimizing computational resource usage. It's a user-friendly tool that allows focusing on business analytics rather than cluster configuration.
>> Learn what EW DataFlow can do for you
Apache Spark is another framework that is particularly effective for real-time analytics. It enables in-memory computing, making it suitable for complex tasks such as stream data processing.
Apache Spark consists of four components and a core. APIs are available for four programming languages
Machine Learning Libraries
The use of ML is the next step in business analytics and big data after building infrastructure with Hadoop or Spark. AI/ML enables companies to achieve their business goals by predicting customer behavior and automating complex processes.
Popular open-source libraries for building machine learning models, such as TensorFlow and Scikit-learn, can analyze vast amounts of data and provide valuable business insights.
ML Model Lifecycle
The Challenges of Implementing Big Data in Business
When implementing a data processing platform, there are challenges that should be considered in advance.
Ensuring Data Quality and Reliability. Data can be unstructured, incomplete, or outdated. Accurate analysis requires data cleaning, validation, and processing. This problem can be solved using ETL and ELT tools that automate the process of extracting, loading, and transforming data. These two methods have their own advantages and disadvantages, and they are suitable for different tasks.
"When processing big data, ELT is typically used, where data is first extracted from various sources, loaded into a centralized storage, and then transformed. This approach ensures the receipt of validated and current information. Without this, there's an increased risk of incorrect use of big data analytics, leading to flawed business decisions."
— Vladimir Borkovsky, EW DataFlow Product Manager
Security Compliance. Business data analytics often involves customer information, requiring strict regulatory compliance. To avoid fines and maintain customer trust, companies must implement Role-Based Access Control (RBAC) systems. This enables the principle of least privilege, where employees are given only the access rights required for their specific tasks.
Shortage of Specialists. For big data operations, businesses need data scientists, data engineers, and DevOps. However, finding candidates with appropriate qualifications either internally or in the job market isn't always possible, which slows implementation and increases training costs. To reduce dependency on highly qualified developers and analysts, companies can implement low-code platforms like EW DataFlow.
"Low-code solutions automate part of the processes related to analytical tasks. For example, they speed up ELT process configuration. With a low-code platform, a team doesn't need 10 senior specialists, and even they will handle work more efficiently"
— Vladimir Borkovsky, EW DataFlow Product Manager
Integration with Existing Solutions. It's important to connect all data sources to the data management system: CRM, Help Desk, and knowledge base. Without proper integration, data will remain fragmented, and the company won't achieve a single source of truth. To solve this, they create a Data Lake that can accumulate both structured and unstructured data from all systems.
The Future of Big Data Analytics in Business
AI/ML are increasingly applied in new areas that were not previously considered core to large companies. Here are a few examples:
| Trend | Technology | Business Advantages | Example |
| Hyper-Personalization | Real-time customer data analytics | Boosts conversion rates and average purchase values, and enhances customer acquisition efficiency | Ford is planning to patent an in-car advertising system that gathers real-time data on passengers to display relevant ads. |
| Automated Decision-Making | Machine learning and behavioral analysis | Reduces operational costs and errors, enabling data-driven, objective, and accurate decision-making | JPMorgan analyzes emails, social media messages, phone calls, and other unstructured customer data to offer flexible credit programs and insurance options at minimum risk. |
| Digital Twins | Virtual models of companies and processes, updated in real time | Allows for continuous process monitoring, quick response to disruptions, resource optimization, and scenario modeling | Siemens uses digital twins to develop engines, communication systems, and trains, allowing for advanced design and operational efficiency. |
FAQ, or Misconceptions about Big Data Analytics
Big data analytics in business unlocks immense potential for enterprise companies across various domains, from customer service to internal process optimization. However, some organizations aren’t ready to implement big data infrastructure due to myths about this process. Which of the following about big data analytics in business is true? Let’s find out common misconceptions:
- More Data Means Better Insights Quality matters as much as quantity. Poor data quality can lead to inaccurate conclusions and wrong decisions, making data cleaning and validation crucial.
- Only Top Data Scientists Can Handle Data. While skilled specialists are vital for advanced analytics, you can reduce dependency on them by automating routine tasks with low-code platforms, allowing junior data scientists to easily configure ETL/ELT.
- Business Data Analytics is a One-Time Investment. It's actually an ongoing process requiring continuous investment in tools and team. Data volume and complexity keep growing, requiring adaptation to maximize value and maintain competitiveness.
Business analytics in big data continues to evolve, shaping how organizations operate. A strategic approach is essential: understanding what problems big data analytics should solve, how to integrate it with business processes, and how to train employees. The Eastwind team is ready to help clients with this — we support EW DataFlow platform implementation projects from sizing calculations to business case execution. We can also assist in forming an in-house team and launch initial ML models while your employees familiarize themselves with the new system.