Data analytics and business intelligence: Understanding the differences

A popular saying among industry professionals — “data is the new oil” — implies that data, when harnessed (or, like oil, “refined”) properly, can provide high value. And they’re not very far off from the truth. In the last two decades, many organizations have added business intelligence features to their repertoire, concentrating their efforts on dashboarding, managed reporting, and data discovery. In the last few years, however, a new position has emerged — that of the data analyst. Spurred on by the success of data analytics-based businesses such as Uber and Amazon, plenty of regular businesses have tried to transform themselves into insight-driven organizations. But the journey has been far from easy, as is evident from organizations that are merely “dabbling” instead of “doing.” The thing is, despite sounding similar and serving many of the same purposes from an outsider perspective, data analytics and business intelligence deliver separate outcomes depending on the business requirement. Just how far-reaching are the differences between data analytics and business intelligence? Let’s find out.

Role of BI vs. role of data analytics

Business intelligence involves handling complex technologies and strategies that allow end users to analyze the data and perform decision-making tasks to improve their business. The first usage of the term “business intelligence” dates back to 1865 and it describes how a banker by the name of Sir Henry Furnese made profits by analyzing his environment and trumping his competition.


On the other end of the spectrum is data analytics, which allows users to convert unstructured or raw data into a comprehensive format. The converted details are used to cleanse, convert, or model the data so that it supports the decision-making process, derives conclusions, and sets up predictive analytics. Most organizations now treat data analytics as common or standard practice, as per their business requirements.

Impact on the organization

Data analytics and business intelligence

Business intelligence is applied throughout various organizations to improve their decision-making abilities, perform data-mining tasks, analyze business information, create reports, and enhance operational abilities. However, it is important to note that business intelligence is predominantly for historical data stored in the data mart and significantly impacts business performance management and data management.

Data analytics, meanwhile, is meant for converting raw and unstructured data into a data format clearly understood by the user. Several business operations, including data modeling, data transformation, and data cleansing are the major trends of implementing data analytics within an organization.


The implementation of data analytics typically occurs in a situation where the company is fairly new and requires considerable changes to their business model. Data analytics help the business users to analyze historical and present data, thereby predicting future trends and change the proposed business model for the better.

On the other hand, business intelligence is applied in situations where the company does not have to alter their present business model and their sole focus is to meet the organizational goals. Business intelligence helps the users figure out where the loopholes are when it comes to managing data and fixing them by offering efficient decision-making scenarios.

Data analytics and business intelligence: Situational usages

A major feature of both data analytics and business intelligence is reporting. But the reporting varies depending on the type of business scenarios and business data. In case some business scenario pops up where the client must contend with everyday trends in the market and form specific reports, then the best possible option might be data analytics. This process also comes to mind where businesses must forecast future data trends based on previous details.

But, if a circumstance arises where the client must handle the data collected in the data warehouse and draft reports by accessing the same data from the warehouse, business intelligence is a more accurate fit. Business intelligence also makes sense where the company needs to organize data or track targeted sales delivery for sales intelligence.

Debugging methods

In the case of business intelligence, it is possible to debug the mechanism only via the historical data offered as well as the end user needs. On the other hand, data analytics is debugged as per the proposed model so that the data is converted into a meaningful format.


Data analytics and business intelligence

In terms of strategy, business intelligence groups seek to offer a more high-quality method of delivering information for the entire organization. Plus, they ensure the principle of “one version of the truth.” The teams seek to assess the most situation-specific details for the benefit of the decision makers as well as other data-driven teams within the organization so they optimize the decision and business processes.

For that purpose, business intelligence teams are tasked with periodically monitoring the strategic targets, reporting the major performance indicators, and figuring out the drivers of underperformance and over-performance. Business intelligence teams require the reporting and analysis to align carefully with the corporate strategy.

On the other hand, data analytics groups focus on solutions for certain business problems through the application of state-of-the-art complicated algorithms for the different data sources. Combined with their thorough understanding of business, data analytics focuses on new value propositions with an experimental edge to provide the market competition some competitor advantage. For data analytics teams, it is absolutely necessary to align with the business priorities of the organization, so they can focus on the correct, high-impact issues together.

Place in the industry

With more and more businesses becoming data dependent, the significance of data analytics as a prime decision-making technology will increase further. Data science will automate the majority of the business intelligence or analytics tasks in the future. Business intelligence, despite being an indispensable part of business decision-making, still remains a predominantly IT activity.

Data analytics transcends that barrier and brings BI and analytics activities to mainstream business. But in the future, BI professionals must work with data analysts to build the systems necessary for real-time insights. Data analysis is currently viewed as a game changer and it has progressed rapidly to provide technologies required for handling complex data, custom reporting, data analysis, and cleansing.

Similar but not the same

While data analytics and business intelligence both share a passion for data as well as insights, the two are drastically separate methods. They work separately and fulfill a different purpose. But both have a significant hold in modern insight-driven organizations, which means instead of pitting them against one another, organizations must bring them together so they can improve themselves and each other by leveraging capabilities and skills.

Featured image: Pixabay

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3 thoughts on “Data analytics and business intelligence: Understanding the differences”

  1. Very impactful and impressive guide. It’s interesting to note that data analytics and business intelligence are drastically different though they do serve the same business purpose. Instead of pitting them against one another, if companies could bring them together, they could do better with all the resources at their disposal.

  2. Richard Gardner

    Hmmm, not sure I agree with that. Data Analytics (in your definition) is just the analysis of anything which isn’t currently formalised in the BI stack. As soon as you understand the levers driving the insights then you can build it into your BI solution, regardless of whether it’s live or historic.

    You’re talking about the practice of applying a few algorithms (usually simple regressions) to data that isn’t currently understood. As soon as you understand it you need to build a mechanism to repeat it. That’s just BI. So if ‘Data Analytics’ is anything it’s BI requirement definition.

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