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Business Intelligence vs Data Analytics: Understanding the Critical Differences

Business Intelligence vs Data Analytics
Published on Feb 15, 2022

As companies, big and small, join the race of Big Data, what’s getting lost in translation is the process and disparity between gathering business intelligence and employing data analytics to make impactful business decisions. Before you even consider choosing what type of data analytics software will work for you or what data you want, you need to understand the core problem you want to solve. 

With Data Analytics gaining popularity on a global scale, many companies are leveraging multiple technologies in this field to gain insights into their customers.  

Business Intelligence, a concept widely used by analytics organizations, plays a vital role in the visualization of customer data to understand and predict their behavior patterns. Hence, when it comes to the field of Analytics, the Business Intelligence vs Data Analytics choice is a relatively challenging one. 

Today, Business Intelligence and Data Analytics are often used interchangeably for the convenience of establishing effective communication. However, this causes confusion among people, especially beginners who are still trying to understand the underlying difference between the two. 

Business Intelligence and Data Analytics are significantly different. Both have different scopes of work and require a varied set of skills to help organizations flourish with data-driven decision-making.  

They Are Not the Same 

If you are still trying to figure out the difference between Data Analytics and Business Intelligence, then you are not alone. 

Some people distinguish between the two saying that – BI examines historical data to explain things that have happened and Data Analytics uses data science techniques to predict future outcomes. But there’s more to it. 

Used interchangeably, both terms have major differences. Business Intelligence is a more generalized term that encompasses Data Analytics. While Data Analytics is geared toward offering future predictions and trends, BI helps business professionals make decisions based on past data insights. 

Before we understand the differences between these two, it is important to first understand the basics of these concepts. 

What is Business Intelligence? 

In Business Intelligence (BI), business data is documented, analyzed, and worked upon to generate exceptional outcomes in the future. Revolving around business data, BI analyzes the performance of the business in the past times and reflects on the overall growth. 

Business Intelligence boosts errorless operations concerning data. It offers an insight into the records of the business and helps the officials to evaluate the journey of their enterprise in terms of economic progress and future outcomes. It helps to keep the flow of data unstoppable yet unbeatable. 

Business intelligence tools help scrutinize the business history and the cases that have either been disabled or enabled by the enterprise to succeed. BI helps to render an opportunity for the business officials to assess the company’s performance and introspect the failures that caused the growth. 

Understanding Data Analytics 

A pretty self-explanatory name, Data Analytics implies the analysis of the information acquired through data mining. Even though it sounds simple, it is so much more than just analysis. Data Analytics is deemed as an integral methodology for successful business development. 

Data Analytics can be categorized as: 

  • Descriptive Analytics – this incorporates the visualization of data that details historical performance, which enables senior management employees to make crucial business decisions. 
  • Predictive Analytics – predictive analysis provides predictions for future sales, client acquisitions, and other relevant metrics. 
  • Prescriptive Analytics – it relies on descriptive analytics and predictive analytics to offer suggestions on the future course of action essential to achieve the desired consequences or mitigate the probable risks.    

In other words, Data analytics helps organizations to plan and predict future outcomes. 

Read more: How Big Data & Analytics Has Disrupted the Gaming Industry  

Difference between BI and Data Analytics 

Using the three categories of Data Analytics, we can create a better distinction between BI and Data Analytics. 

All descriptive analytics belong to the category of Business Intelligence. Predictive analytics also constitute BI.  After all, why scrutinize analytics if you do not intend to employ them to take action to enhance your future outcomes?  

Prescriptive analytics ascends beyond BI and can be placed in the realm of Data Analytics. 

So, where do we need to draw the line of distinction?  

Business Intelligence relies on data that business administrators work with. If trained in employing visualization tools, like Microsoft Power BI, Tableau, or Looker, they can build their BI reports. 

Data Analytics demands a higher level of mathematical expertise. Data scientists apply algorithms on big data sets to organize and model them to a point where the data can be utilized for drafting predictive reports. It relies on algorithms, simulations, and quantitative analysis to define the relationships between data that are not present on the surface. Data Analytics works on trying to comprehend why things happened. That does not happen with BI. 

Why just Data Analytics Is Not Enough? 

Smart decisions cannot be made without the right information to act on. 

The right tool will help exemplify the data in a meaningful manner and assist in distributing this information to the right people at the right time. This is what business intelligence is all about. To reap the maximum potential benefits of employing data analytics and business intelligence software in your organization, the first factor to undertake is to clearly define the problem to be solved with business intelligence. Organizations that focus on scaling business intelligence target one problem at a time. 

Factors that Drive Business Intelligence vs Data Analytics Decision 

A) Scope 

The most significant difference between BI and Data Analytics is – the scope of work. While the former is about gaining operational insights, Data Analytics is used for executing a wide range of analyses. With Business Intelligence, the ideation is to build dashboards and prepare reports. But, in the case of Data Analytics, this goes a step further. It is employed to find correlations between different variables that help determine the factors influencing the results.  

With Business Intelligence, a straightforward analysis that offers an overall picture of the business operations can be obtained. Data Analytics, on the other hand, helps to locate intricate insights in business operations. An organization can gain year-on-year sales performance with Business Intelligence, but with Data Analytics, they can find the factors driving the variations in the outcome 

B) Reports 

Business Intelligence reports are directed at a specific time, based on the use cases. Although they can also be used for ad-hoc reporting, the strategy is to streamline regular reporting. However, Data Analytics is adaptable with analyses as several new techniques are implemented to optimize reporting. To sum up, Business Intelligence is deployed for generating standardized reports, whereas, with Data Analytics, organizations can blaze a trail to curate advanced analyses. 

Read more: 71% BFSI firms use big data analytics to gain competitive advantage – its uses in equity research  

Common Grounds for Business Intelligence and Data Analytics 

Business Intelligence addresses the ongoing operations, allowing corporations and departments to meet their organizational goals. Data Analytics, on the other hand, helps organizations transform the way they do business. Both domains can benefit from a little data preparation. 

Data Analytics requires data modeling, in which raw data is gathered, cleansed, organized, converted, aggregated, validated, and transformed. Clean data is also beneficial for BI. 

Once the data is clean, it is stored in a structure that lends itself to reporting. This signifies that the data is stored in a data warehouse — a columnar data storage. The data stored in the warehouse symbolizes a single version of truth for all organizational reporting, for both Business Intelligence and Data Analytics. BI and Data Analytics both call for an analytics stack on a data warehouse. 

The Uncommon Truth  

Essential in business operations, BI and Data Analytics help in accumulating raw data and analyzing them for future operations. Although they sound similar, there are major disparities between the two.  

From decision-making processes to achieving goals, Business Intelligence focuses on studying business growth and patterns, basis the data accumulated from past operations. On the other hand, Data Analytics deals in transforming raw data into meaningful material, eventually helping organizations to outline future trends on predictive grounds by questioning past strategies and patterns. The two concepts are distinct yet intertwined in a way that BI cannot function without Data Analytics and vice versa.  

The Business Intelligence vs Data Analytics decision depends entirely on the types of analysis being performed. While Data Analytics and Business Intelligence seem closely related, they are very different in their ways. However, both hold their advantages in helping organizations to stay ahead in this competitive technological world with data-driven insights. 

With offices in New York, San Francisco Austin, Seattle, London, Zurich, Pune, and Hyderabad, SG Analytics, a pioneer in Research and Analytics, offer tailor-made services to enterprises worldwide. With our Business Intelligence and Data Analytics Services, we help enterprises realize the true potential of their data, thereby assisting them in making crucial business decisions. If you are looking to make critical data-driven decisions that stimulate accelerated growth and breakthrough performance, contact us today.