Comparing Business Intelligence vs. Data Science and Analytics

Data and the resulting insights and intelligence provided have become key commodities. Organizations across every sector are looking to leverage the informational assets they have on hand, as well as other accessible data, and analyze these items to support an array of important improvements and advancements.

And now that so much data is being created every day, initiatives related to data science and business intelligence are providing unparalleled insights and visibility into patterns and trends that were previously unattainable.

According to Forbes, approximately 2.5 quintillion bytes of data are created every day, and about 90% of the world’s data was created within the past two years. This incredible data output holds considerable potential for researchers and information systems professionals.

However, these concepts are not just relevant to people in tech. In this increasingly data-reliant world, many types of professionals can benefit from understanding the similarities and differences between business intelligence, data science, and data analytics.

What Is Business Intelligence?

A key objective or focus of business intelligence in information systems is providing insights for the management of assets, available resources, or processes. In this way, business intelligence initiatives are usually part of planning and control activities, with the goal of establishing a single source of truth.

This “one version of truth”, can then be used as a comparative benchmark to gauge overall performance or the impact of improvements made. Many business intelligence processes and use cases involve the creation of cyclical, standardized reports that encompass structured data from reliable and quality internal sources.

This data is periodically pulled from internal operational systems or software platforms and combined into a single data set, which is then analyzed through the use of data discovery and/or analysis tools or software.  From here, the analyzed data set can be used to create data visualizations or to support self-service analysis to gain actionable insights.

What Is Data Analytics?

Data analytics is the process of studying data sets to gain new insights about the information they contain. Data can be a valuable asset for companies, but it’s not very useful in its raw form. The purpose of data analytics is to extract the value from raw data sets by reorganizing them so that they can be more easily interpreted by a wide audience.

Data analysts seek to identify trends and patterns in large data sets and create ‘visualizations’ of their findings to share with other members of their organization. These visualizations commonly come in forms such as charts, graphs, tables, or scatterplots.

The specific insights that data analysts look for will depend on the type of analysis being performed. There are four main types of data analytics designed to provide different levels of insight:

  • Descriptive analytics: This is the process of describing things that happened using data. Example: A company using sales data to determine which of their products or services are more popular.
  • Diagnostic analytics: This is the process of using data sets to identify the possible causes of trends or events. Example: Using seasonal performance data to determine if the weather is adversely impacting business.
  • Predictive analytics: This is the process of using data to make more accurate predictions about the future. Example: Predicting how an event will impact a company’s stock price based on past performance data from similar incidents.
  • Prescriptive analytics: This is the process of using data to make better decisions and develop more effective strategies. Example: Using consumer profile data to identify leads and construct more engaging pitches.

By using all four of these data analysis methods in conjunction, organizations can identify their most pressing issues, find out what’s causing them, and determine the optimal solutions. Today, data analytics are being utilized in a wide range of sectors including finance, healthcare, hospitality, entertainment, government, retail, and manufacturing.

What Is Data Science?

Whereas business intelligence is typically focused on human interpretation of analysis for management functions, data science is more geared toward gleaning insights with the help of technology or intelligent algorithms. In this way, data science requires specific tools and analytical frameworks, including elements like:

  • Descriptive analytics
  • Identifying the relationships between specific variables or events
  • Predictive analytics

In addition, where business intelligence usually encompasses the analysis of internal data sources, data science can include internal and external data, as well as unstructured sources like documents, images, or videos.

In this way, while business intelligence analysis typically results in a standardized report, data science analysis will not often conclude with a report of this kind. Instead, researchers and analysts create a machine learning model, or an algorithm with the capability to learn about a concept, make a prediction, or determine the next best action for a specific initiative.

Examples of data science use cases might include determining the next best offer for a customer based on their purchase history, demographics, and other data; or leveraging a machine learning model to recognize fraudulent activities.

Data Analytics vs. Data Science

Data analytics and data science share many similarities. However, data science is a much broader field that encompasses a wider range of methods and processes. While data analysis is often focused on answering specific questions, data science deals with improving organizational strategy as a whole.

Data scientists may use the insights gained through data analysis to build predictive models and develop algorithms designed to support organizational goals. They often work closely with employees from other departments such as sales and product development to help guide decision-making.

While data analysts follow the lead of decision-makers by researching specific issues that impact the organization, data scientists often drive company-wide decisions and help develop strategies from the ground up.

How Do Data Analytics and Business Intelligence Work Together?

In the digital age, making critical business decisions without the help of data is almost unheard of, and organizations across industries use data analysis to support their business intelligence strategies.

Data analysts take unstructured data and organize it into a comprehensible form so that the information can be more easily examined. Business intelligence experts can then use this structured data to help guide organizational decision-making.

Data Analyst vs. Business Intelligence Analyst: Which Is the Better Career Path?

Business intelligence and data analysis can both be highly lucrative and fulfilling career paths. However, it’s important to realize that there are several significant differences between these roles, and the best choice for you depends on your skills and interests.

Data analysts identify and solve problems within their organizations using analytics and statistical models, while business intelligence analysts provide organizational leaders with evidence-based insights that they can use to make better decisions.

Data analysts and business intelligence analysts follow a similar process. People in both roles must understand the needs of end-users, and must be able to present their findings in easily digestible formats.

However, data analysts often work with a wider variety of data than business intelligence analysts, including unstructured data, which is more difficult to store and manage. The data used by business intelligence analysts, on the other hand, is typically structured and processed from data warehouses.

Additionally, the skills and training required for data analysis and business intelligence careers can also vary significantly, although there is some degree of overlap between the two.

How To Become a Data Analyst

To become a data analyst, you’ll need at least a bachelor’s degree in computer science or another area that focuses on statistical and analytical skills, such as mathematics or information systems.

However, some employers may prefer candidates who have a higher level degree, such as a Master of Science in Management Information Systems (MIS). While it can take longer to complete, earning an MIS can qualify you for a wider range of careers and help potentially help you command a higher salary.

Data analysts should be familiar with the programming languages Python, R, and SQL, and be competent with data visualization platforms like Tableau and Power BI. In many cases, they must also understand how to create and maintain different types of databases, including SQL and non-SQL.

How To Become a Business Intelligence Analyst

To become a business intelligence analyst, you’ll typically need at least a bachelor’s degree in a relevant area. Compared to data analysts, business intelligence analysts can come from a wider range of educational backgrounds.

Common majors for business intelligence analysts include computer science, statistics, information systems, business administration, and economics. Regardless of their major, most business intelligence analysts are expected to have completed coursework in areas related to data analysis, statistics, and technology.

Business intelligence analysts must be familiar with the SQL programming language and should be very comfortable using data visualization programs (Tableau, Power BI). Additionally, they must possess strong communication skills and a comprehensive understanding of basic business and finance principles.


Forbes, “How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read”