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Business Intelligence
Business Intelligence vs Data Analytics

Vipul Sharma, 3rd Jan

In the last few years, the terms Business Intelligence vs Data Analytics are thrown so much around! What exactly do they mean? Is there any difference between the two? Let’s encounter some relevant information for them.

The terms BI & DA are used often and interchangeably, but there’s a subtle distinction between the two. Here’s the comprehensive definition.

What is Business Intelligence?

Business Intelligence or BI can be described as “A combination of methods, procedures, architectures, and technologies that turn unstructured data into knowledge that can be used for strategic, tactical, and functional decision-making.” This was stated by the prominent research and advisory firm- Forrester. Let’s break down the term to make it easy.

In a larger context, Business intelligence has two definitions. It begins by outlining the methods, developments, and equipment that businesses employ to gather (and communicate) business insights. Second, it defines the insights produced as a result of this process. This distinction is minor, but it is essential.

When discussing business information, you should always be clear about referring to the process or the result. Business Intelligence includes the following tactics and tools-

  • Dashboard Development
  • Real-Time Monitoring
  • Data Analytics
  • Benchmarking
  • Performance Management
  • Data & Text Mining, etc.

This is how the processes and tasks of business intelligence are varied; while data analytics is a single tool within BI (and an important one, for that matter), it’s ultimately just one piece of a much bigger enigma.

Purpose of Business Intelligence

Most people will tell you that business intelligence enhances strategy and decision-making inside an organization. This is accurate. Profit, though, is what business intelligence ultimately comes down to. Money talks, regardless of what some people think; since we live in a capitalist society, there is no escape from it. But this pursuit of wealth can take many different forms.

Additionally, each case is different. A social media firm could be more interested in utilizing BI to figure out how to increase ad clicks than, for example, a toy company, which might use it to enhance its sales strategy.

Increasing profit through enhanced operations is the ultimate goal of business intelligence, regardless of the sector, organization, or purpose. This means that indicators like sales revenue, profit margins, staff attendance, and data from the supply chain are used to determine how an organization operates.

Now, let us clarify what Data Analytics is all about!

What is Data Analytics?

Collecting, filtering, analyzing, manipulating, storing, modeling, and querying data is known as data analytics (along with several other related tasks). Its objective is to generate insights that guide decision-making in various fields, including business, the sciences, government, and education. (Forrester- Prominent Research & Advisory Firm)

Sounds recognizable?

There is a lot of overlap between this and business intelligence, so it’s not surprising that it seems comparable. But in its most basic form, data analytics concentrates on the minute details of the analytics procedure. It is not solely a business intelligence tool but is frequently utilized in a business context.

Whether it is called data analytics, data science, or machine learning, the field has experienced an enormous shift over the past two decades due to increased data collection, advancements in data collection methods and techniques, and a significant increase in the computing power available to process data.

Data Analytics is considered a technical discipline and can be bifurcated into four broader categories- Descriptive Analytics, Diagnostic Analysis, Predictive Analysis & Prescriptive Analytics.

Interesting Fact: The market size for business intelligence and analytics software applications is forewarned to expand worldwide over the next few years, from 15.2 billion U.S. dollars in 2020 to more than 18 billion in 2025. The BI and analytics software application market is a subsegment of the enterprise application software market. (Source- Statista.com)

Difference Between Business Intelligence vs Data Analytics

Business Intelligence and Data analytics appear to be reasonably similar. Several key distinctions between business intelligence and data analytics are as follows-

Business IntelligenceData Analytics
Using the Insights vs Creating the InsightsThe main goal of business intelligence is to assist decision-making by providing helpful information and insights from data analytics.The main goal of data analytics is to transform and clean raw data into insights that may utilize for many different things, including BI.
To Look Back & ForwardTo guide future planning, business intelligence mainly focuses on looking back to see what has happened.Data analytics also recognizes historical trends, it frequently makes predictions using these facts.
Structured Data & Unstructured DataStructured data, or data kept in databases with tabular information or other systems, is used by business intelligence. Reports and dashboards are created using these data.Although it often starts with unstructured, real-time data, data analytics employs structured data. Before saving the data for further examination, data analysts must first organize and clean it.
Technical Users vs Non-Technical UsersLeadership groups and non-technical employees, such as chief executives, finance directors, or chief information officers, are the primary users of business intelligence.Analysts, data scientists, and computer programmers with a more technical focus typically specialize in data analytics.

Interesting Fact: In 2021, Morningstar Advisor Workstation was the world’s most popular data analytics software. At that time, Morningstar Advisor Workstation was used by 26.87% of financial planning and advisory organizations worldwide. (Source- Statista.com)

1. Data Literacy

Businesses have recognized the value-added advantages of data-driven choices and data-powered, actionable insight. So that business users can make wise decisions in their day-to-day work life without the aid of IT or Data Science teams, they now wish to equip all levels of their organization with practical Data Literacy training.

2. Data Quality Management

When it comes to guaranteeing the accuracy of analytics insights, data quality is crucial. A robust BI platform’s primary differentiator is still data quality management (DQM), and DQM-connected business processes ensure that an organization conforms to international data quality (DQ) and data governance (DG) requirements.

3. Collaborative BI

Due to its ability to speed up report sharing, decision-making, and data collecting, collaborative BI is quickly gaining popularity. Through web 2.0 platforms, collaborative BI encourages group problem-solving and open business debates.

4. Embedded Analytics

By 2023, the global embedded analytics market is anticipated to reach US$60 billion, according to Allied Market Research Report. Under being “resident” within native programs, embedded analytics allows quick data analysis without transferring data between software environments.

5. Widespread Cloud & SaaS Adaptation

In 2021, many companies will have switched to the public cloud or hybrid cloud and begun signing up for Software as a Service (SaaS) agreements for outsourced business intelligence (BI) services.

According to Dresner’s 2020 Cloud Computing and Business Intelligence Market Study, 54% of corporations believe cloud BI to be “extremely significant” for their companies, while 95% of software providers have voted in favor of the idea.

6. Automation in Analytics & BI

The number of significant firms using AI to automate data analysis increased significantly from 55% in 2019 to 64% in 2020. For large-scale analytics initiatives, automation is essential since manual work results in inefficiencies and a waste of time. Humans will be more beneficial in these situations as data-driven decision-makers. (Source- Dataversity.net)


In summary, data analytics plays a significant role in business intelligence. Without it, it cannot operate. On the other hand, although being widely utilized in business, data analytics works well without business data. Corporations have accepted it as a beneficial tool. Although business intelligence is now one of the most popular uses of data analytics, it is also relevant in many other industries. Organizations that embrace AI-driven BI tools will be best poised to capitalize on their increasing in-house datasets and expand remote workforces to grow in this dynamic business environment.


Vipul Sharma

Vipul Sharma is an Experienced Enterprise Architect with a demonstrated history of working with business requirements and providing architecture solutions in the Healthcare, Retail & and FinTech with Business Intelligence Consulting and Building BI Dashboards.

He is passionate about solving complex business problems in a scalable manner through data-driven insights and cross-functional collaboration. He has worked with Microsoft and VMWare to solve problems related to Customer Journey analysis, Inventory tracking and Cost Scheduling.

Vipul has proven expertise in Analytical Skills, Data Engineering, Data Warehousing, Business Intelligence, Analytics and Amazon Web Services (AWS). Strong engineering and management professional and completed his Masters's Degree in Finance from the University of Rochester.

He is the founder of Prefigure Technologies in Seattle Washington to Offer Business Intelligence Services, BI Consulting, BI Implementation support, and BI Training.