Data Analytics and Business Intelligence – A Comparative Study

With the rise in data science and data-driven business, companies leverage customer-generated data to understand user requirements by enabling business intelligence. Business intelligence in data analytics got accepted & is in practice on a global scale by both small and large-scale enterprises. But people often confuse these two terms, and many use them interchangeably. Here you will get a quick walkthrough of the difference between Business Intelligence and data analytics. Also, you will go through the different data analytics and Business Intelligence tools used in enterprises.

What is Data Analytics?

Data analytics is a branch of data science that deals with analyzing raw data to find trends & answers to business questions. Data analytics comprises different techniques with different goals. Through data analytics, companies can explore data in real-time & identify patterns to make informed business decisions for future prospects. Data analytics help businesses render a data-driven culture and construct innovative, forward-thinking business methodology. Companies hire data science professionals, data analysts, and data scientists to do such jobs. Data analytics are of four types:

·        Predictive Data Analytics: Predictive Analytics helps predict future performance depending on past data for future possibilities. From the insights drawn from predictive analytics, enterprises can update their ways of operating or delivering services to customers for the benefit of the future.

·        Perspective Data Analytics: It helps predict the future depending on the modifications a company is inclined to integrate within their existing system. It helps decision-makers comprehend how the data-driven outcome can influence the business’s future.

·        Descriptive Data Analytics: It is similar to Business Intelligence practices which use historical data for gaining insights through statistical methods like mean, median, and mode. Descriptive analytics do not demand vast analytical proficiency, and can anyone at the beginning phase of data analytics learning can perform it.

·        Diagnostic Data Analytics: Diagnostic Analytics particular type of data analytics that focuses on assessing correlations among various variables for conducting the root cause of the analysis. Through this type of analytics, organizations can uncover the factors that are either obstructing to achieving certain operations or in providing the most value

According to some estimation, the data analytics industry will grow to 684.12 billion USD by 2030. Companies like Accenture, Tata Consultancy Service, Cognizant Technology Solutions, Amazon, Capgemini, Kaspersky Labs, Infosys, EY, etc., hire data analyst professionals.

What is Business Intelligence (BI)?

Business Intelligence is a domain under which companies perform various data-driven operations like data mining, data preparation, and management. Through business intelligence applications, BI analysts accumulate & process extensive amounts of structured, semi-structured, and unstructured data from both internal and external systems. Through business intelligence tools & techniques, companies and end-users can analyze performance metrics or recognize actionable facts from past raw data that can facilitate data-driven business decisions. Most companies hire data miners, data warehousing professionals, and BI analysts for such jobs. According to the former VP Analyst of Gartner, there are two different types of business intelligence.

·        Traditional Business Intelligence: Traditional BI delivers simple reporting wherein the focus resides on accuracy rather than other factors of insights. Organizations commonly use it with regulatory or monetary reports.

·        Modern Business Intelligence: Such types of practice involve modern BI operations associated with the quick delivery of insights. Here the company focuses on speed over other forms of details. Many companies use this type of BI intelligence to check the increased sales and quickly identify the user trend and buying patterns.

According to some reports, the global Business Intelligence market size will expand to 33.3 billion USD by 2025. Companies like Comcast, Apple, Boeing, Amazon, Google, Norton, Dell Technologies, Verizon, Micron Technologies, etc., hire business intelligence professionals. Let us now understand why business intelligence & data analytics are important for businesses.

Why are Business Intelligence and Data Analytics Important?

Both Business Intelligence (BI) and data analytics play a significant role in driving business decisions. While business intelligence focuses on converting all past raw data into meaningful insights or better analysis, data analytics focuses on extracting patterns from data that can help business executives make informed business decisions as per predictions. That is why business intelligence & data analytics are important for a company.

Similarities between Data Analytics and Business Intelligence

Although both data analytics and business intelligence are different verticals, there are certain similarities between data analytics and business intelligence.

·        Both had to deal with a large amount of data (structured, semi-structured, or unstructured).

·        Data cleansing and extracting meaningful data from a pile of raw data is also an operation requires in both.

·        Although data analytics and business intelligence tools are different – particular tools like Tableau, MS. Excel, Qlik Sense, etc., are common for both data analysts and BI analysts.

·        Often organizations use business intelligence in data analytics to first extract intelligence from past data & leverage them to predict the future requirements of the user.

·        The ultimate goal of both data analytics and business intelligence is to benefit the organization by raising its profit and allowing executives and leaders to make informed decisions from granular data.

Difference between Business Intelligence and Data Analytics

So far, we have understood the similarities between data analytics and business intelligence. Both business intelligence and data analytics complement each other at some point. But there are a lot of differences between business intelligence and data analytics listed below:

Business Intelligence	Data analytics
It infers the existing data needed to elevate trade and decision-making activities in a business.	It alludes the crude data and uses statistics, programming, and mathematics to discover hidden pattern matching for future possibilities.
The term business intelligence came into existence in the year 1865.	The term data analytics came around mid 19th century and became popular in the 1960s with the advent of computer systems.
Business intelligence deals with past data and focuses more on understanding the present situation or current goals of the business.	It extracts hidden patterns from the data that helps in predicting demands and future requirements for the business.
It is less flexible in comparison to data analytics. It is because such intelligence sources of data ought to be kept pre-planned.	It is much more flexible as the data analysts can add data sources according to the necessity.
Another difference between business intelligence and data analytics business intelligence renders much simpler data as compared to data analytics.	On the other hand, the complexity of data is much more as compared to business intelligence data.
It mainly deals with structured data. In some selective cases, business intelligence works on unstructured data.	It has to deal with structured, semi-structured, and unstructured data.
Organizations need to leverage business intelligence when the company does not have to make any changes to its current commercial model & the purpose is to understand the organization’s current status for making better decisions.	Organizations need to leverage data analytics when the company needs to make critical changes in the commercial model seeing the future opportunities and making decisions pivoting the future needs.
It makes use of analytical and reasoning approaches for extracting business insight.	It makes use of scientific approaches for extracting business insight.
It deals with questions like what will happen and how to make it more effective in years to come.	It deals with questions like what has happened & how it impacted the business.
Professionals can debug the BI outputs through past data supplied or based on the end-user requirements.	Professionals can debug data analysis through the proposed model used for converting the data into meaningful insight for future potentials.
Some well-known and standard business intelligence (BI) tools are MS. Power BI, ThoughtSpot, TIBCO Spotfire, Tableau, Domo, Klipfolio, InsightSquared Deals Analytics, Cyfe, Sisense, Looker, Alteryx Stage, MS. Control BI.	Some well-known and standard data analytics tools are SAS, Tableau Public, MS. Excel, Apache Spark, KNIME, Rapid Miner, QlikView, etc.

Conclusion

In this article, we have seen the similarities and difference between business intelligence and data analytics. Considering today’s market trend, almost all companies have become data-driven. With the evolution in developing business intelligence and data analytic tools, companies find them worthwhile in extracting data to measure the current trends and prospects of data. Both business intelligence and data analytics render a significant role in the overall growth of a business.

There are a lot of other factors that show the difference between business intelligence and data analytics in this comprehension. But both of them work in close tandem. Business intelligence helps in converting raw data into meaningful insight so that company can understand the current scenario and trend of the market and decide on various business operations. On the other hand, data analytics takes granular data as input to calculate the future metrics and ways business executives, managers, and corporate leaders can take business decisions. Organizations need both free and paid data analytics and business intelligence tools to perform analysis of data and extract business intelligence from those data.