Data Analytics vs. Business Intelligence: How to Make the Right Choice for Your Business

You might see the terms “data analytics” and “business intelligence” often in your day-to-day work, but have you ever wondered what the difference really is? While some may use each term interchangeably, they refer to different concepts and have distinct uses in business strategy. Both are critically important tools that clarify the data picture, providing insight to make informed decisions and stay competitive.

This article will explain data analytics and business intelligence, how they’re applied, and how to choose between them in your business environment.

What are data analytics and business intelligence?

The processes are not mutually exclusive; they form a relationship of sorts. Organizations use both to gain information on the state of business operations, investigate potential opportunities, and prevent negative outcomes.

What is data analytics?

Data analytics is the foundation of modern business intelligence, providing the data and insights needed to make informed decisions. It encompasses the discovery, interpretation, and communication of meaningful patterns in data. It refers to a wide range of methods and procedures used to evaluate datasets and interpret the information from them. The focus is on analyzing data to gain insights and drive business strategy and employs a range of techniques to transform raw data into clear information that reveals trends and patterns.

The purpose varies, from basic business reports that provide descriptive summaries of past data to more complex studies that identify future trends. The insights from the analyses are used to strategically optimize operations, improve customer satisfaction, and increase profitability.

What is business intelligence?

Business intelligence (BI) is the process of collecting, storing, and analyzing the company’s historical data. It is a subset of data analytics that focuses more on operational insights to address immediate business needs.

BI integrates a wide range of tools, applications, and methodologies to collect data from internal and external sources. It prepares this data for analysis, developing and running queries against it. It then generates reports, dashboards, and data visualizations so the results are accessible and actionable.

Business intelligence vs. data analytics: 5 key differences

Both data analytics and BI are important elements in evaluating data to inform business decisions. Some of the biggest differences include the types of data they handle, the scope, where the data originates, how much data is involved, and the business goal. We’ll break each one down here:

Data types

Data analytics deals with a wide array of data types, including structured and unstructured data. This encompasses everything from numerical data, text, and videos in simple databases to complex formats like social media or IoT (Internet of Things) devices in data lakes or data warehouses. The variety of formats allows for processing diverse data for sophisticated and comprehensive data analysis, including predictive modeling and sentiment analysis.

Business intelligence concentrates on structured data, like sales records, financial information, and other operational data that directly support business goals. This straightforward approach works well for performance tracking and reporting on operational efficiency.

Scope

Data analytics is extensive in scope and is used to extract deep insights that inform broad business strategies. Using statistical techniques and machine learning, it can simulate potential outcomes and extrapolate future patterns. This forward-looking view enables firms to predict market demands, and make appropriate plans.

Business intelligence is narrower in scope, focusing on providing concise snapshots of what’s going on at the moment based on existing data. It’s valuable for gaining immediate clarity on key performance indicators (KPIs) and other metrics, helping organizations make quick decisions during day-to-day operations.

Data sources

Data analytics relies on multiple internal and/or external data sources, including demographics, economic indicators, and even weather data, integrated and analyzed to provide an inclusive view of all the factors that might affect business operations.

Business intelligence, on the other hand, draws primarily upon internal data sources from operations, customer interactions, business transactions, and human resources to focus on the internal company dynamics. It may include some external data for benchmarking or competitive analysis, as it is meant to focus on operational performance.

Data volume

Business intelligence uses internal data, which is finite in nature and proportionally smaller in volume. The data is aggregated to create straightforward reports that are easier to access and understand.

Data analytics may not involve ‘infinite’ amounts of data, but since the scope includes external sources, the volume can be orders of magnitude larger—potentially incorporating petabytes of data. The volume and scope of data used for data analytics are more complex and may require data scientists to evaluate and analyze.

Business goal

Data analytics is intended to be predictive, offering organizations the answer to “What is likely to happen?” The purpose is to be proactive rather than reactive and used to identify trends and patterns so organizations can prepare for possible opportunities or avoid potential hurdles derived from the data.

Conversely, business intelligence can answer the questions “What has happened in the past?” and “What is happening now?” to optimize business operations. The information extracted from this data informs internal tactical decisions that affect how the business operates.

Business intelligence and data analytics use cases

Real-world applications for BI and data analytics run across all sectors and industries. Now that you understand how they work on paper, it’s time to examine how each works in practice. Here are some everyday use cases for each:

Business intelligence use cases

• Inventory management: BI systems use historical sales data along with current inventory levels to predict future demand. This gives businesses the information to maintain optimal stock levels, reducing potential overstocking or understocking. Combining this data with supply chain management data can help optimize ordering processes, reduce overall carrying costs, and improve efficiency.
• Sales analysis: Organizations can analyze sales patterns, customer purchasing behaviors, and user sentiment over time. This helps identify product performance so businesses can adjust their promotion and sales strategies in real time and tailor offers to meet changing market conditions and customer preferences.
• Operational performance: BI tools can help companies track and analyze KPIs across different operational processes, helping managers identify trouble spots, understand how resources are used, and implement changes that are more likely to improve productivity and save costs.

Data analytics use cases

• Finance: Financial forecasting is among the most effective uses for data analytics, enabling organizations to use advanced tools to build forecasting and modeling matrices. Analysis can predict trends based on historical data, assess market conditions, and gain insights into investments, budget allocations, and risk management.
• Streaming services: In this competitive industry, data analytics is critical for organizations to gain information that helps them distinguish themselves from their contemporaries. The vast amounts of information that can be analyzed help inform viewer behaviors, which can drive personalization strategies. This, in turn, can enhance user engagement and guide programming and content creation decisions.
• Fraud detection: Keeping data safe is an everyday organizational task. Specialized data analytics tools are central to detecting and preventing fraud across all industries, including banking, insurance, e-commerce, and healthcare. The tools can analyze patterns and anomalies in large datasets to identify fraudulent transactions, flag suspicious activities, and alert security teams in real time. This safeguards company assets, reinforces customer and employee confidence, and helps them stay compliant with regulatory requirements.

Data analytics vs. business intelligence: Why choose one over the other?

The choice between data analytics or business intelligence isn’t black or white, nor is it all about one technology over the other. The decision comes down to figuring out which solution most appropriately aligns with your business’s specific requirements. No matter what choice you make, it will have a profound impact on how your data informs your business strategies and operations.

Complimentary, not competitive

Data analytics and business intelligence are often discussed as if they are mutually exclusive. It’s more productive to see them as complementary methodologies—each with its own strengths and purposes.

For organizations in dynamic industries, being proactive and future-focused provides a considerable competitive advantage. Data analytics is well-suited for performing intricate analyses to forecast future trends and behaviors. This can help in developing innovative strategies to gain market share, anticipate changes in customer sentiment, and gain insights into industry-specific ebbs and flows.

Comparatively, business intelligence is important for companies that need quick, reliable access to data pertaining to their current day-to-day operations, providing immediate insights that inform tactical decision-making.

The more practical approach for many organizations is to integrate both solutions into their operational and strategic frameworks. This bilateral application offers comprehensive coverage—addressing operational efficiency through BI while driving innovation and exploring potential opportunities through advanced data analytics.

Here are some questions you might ask to find out which best fits your organization:

• What are the primary business goals? Does your organization need to enhance current operational efficiencies, or is the goal to explore new business opportunities that could reshape your market position?
• What is your organization's current data maturity? Does it possess the capability to collect and analyze large volumes of data effectively, or would it benefit more from refining existing data systems to improve accuracy and efficiency?
• What resources are available to support data initiatives? Does your organization have the necessary technology, personnel, and budget to implement advanced data analytics solutions, or would a focus on strengthening business intelligence capabilities be more practical at this stage?
• How quickly does your organization need insights from its data? Does it require real-time analytics to make immediate decisions, or can business operations accommodate insights derived from historical data analyses?
• How complex is the data? Does the data require complex modeling and predictive analytics to provide value, or are straightforward descriptive analytics sufficient to meet your goals?
• How much data integration is required? Does your organization need to integrate disparate data sources across various departments or even external partners, or are its data sources consolidated and straightforward?
• How important is data democratization to your organization? Do you need to empower multiple stakeholders across different levels to analyze and interpret data, or are these activities centralized within specific teams?
• What are the regulatory and compliance requirements for the data? How do these requirements impact your choice between more agile data analytics platforms versus robust, secure business intelligence systems?

Ultimately, the decision to utilize data analytics or BI depends on the specific context of your business, including its immediate needs and strategic objectives. By carefully evaluating these factors, you can make an informed decision that maximizes the utility of your data to support your organization’s growth and success.

Data analytics or business intelligence? CData supports both

Whether your organization plans to use data analytics, business intelligence, or both, CData Connect Cloud can help bring it all together. Connect Cloud streamlines analysis and BI reporting processes by enabling data access to any source within your teams' preferred tools.