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Business Intelligence vs Data Analytics: 7 Critical Differences

• June 16th, 2021

Business Intelligence vs Data Analytics | Hevo Data

With the field of Data Analytics gaining popularity on a global scale today, many companies are leveraging multiple tools and technologies in this field to gain insights from their customers. Business Intelligence, a concept that is widely used by Analytical companies plays an important role to visualize the customer data so that customer behavior patterns can be predicted. Hence, when it comes to the field of Analytics, the choice of Business Intelligence vs Data Analytics is a relatively tough one.

Today, Business Intelligence and Data Analytics are used interchangeably for the convenience of communication. However, this creates confusion among people, especially beginners who do not understand the underlying difference between the two widely used terms in the Analytics world. The reality, however, is that Business Intelligence and Data Analytics are significantly different. Both have a different scope of work and require a varied set of skills for helping organizations flourish with data-driven decision-making. While Business Analysts oversee the requirements of data and build reports, Data Analysts carry out in-depth analyses. 

This article provides you with a comprehensive overview of both techniques and highlights the major differences between them so that you can make the Business Intelligence vs Data Analytics decision easily. It also provides you with their types and benefits. Read along to find out how you can make the Business Intelligence vs Data Analytics decision seamlessly for your organization.

Table of Contents

What is Business Intelligence?

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Business Intelligence is a process of converting raw data into meaningful insights to drive business decisions. It provides an overview of business processes, assisting companies in analyzing their efficiency and productivity. The workflow of a business intelligence professional includes summarising, reporting, dashboards, graphs, charts, and other forms of visualizations.

What are the Types of Business Intelligence?

According to Cindi Howson, a former VP Analyst at Gartner, there are 2 types of Business Intelligence approaches:

1) Traditional Business Intelligence

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Traditional BI provides simple reporting where accuracy is prioritized over other aspects of insights. This is widely used with regulatory or financial reports.

2) Modern Business Intelligence

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Practices involved in Modern BI are associated with quick insights delivery where the speed is mandatory over getting a cent percent correct information. For instance, an E-Commerce company can increase sales by quickly identifying the trend of changing buying patterns with Business Intelligence.

What are the Advantages of Business Intelligence?

Business Intelligence offers a wide range of benefits that make it such a competitive market today. Some of those benefits are:

  • Reporting: With Business Intelligence platforms, companies can quickly generate reports to gain new insights about the companies’ current state. For example, organizations can identify patterns in sales processes, operational costs, and more.
  • Real-Time Insights: With numerous Business Intelligence tools, companies can get real-time insights, allowing them to respond quickly to stay ahead of their competitors.

To learn more about Business Intelligence, click this link.

What is Data Analytics?

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Data Analytics is a process of analyzing data with sophisticated tools like Python with the aim of assisting organizations with strategic and tactical business decisions. With Data Analytics, companies can unearth insights that might not be possible with Business Intelligence. Data Analytics is an advanced version of Business Intelligence.

What are the Types of Data Analytics?

There are 4 types of Data Analytics:

1) Descriptive Data Analytics

Descriptive Analytics is similar to Business Intelligence practices where historical data is used to gain insights like mean, median, and average. Performing Descriptive Analytics does not require extensive analytics skills and can be carried out with ease.

2) Diagnostic Data Analytics

Diagnostic Analytics is a vital step in data analytics that is focused on evaluating correlations among different variables to perform root cause analysis. With Diagnostic Analytics, organizations can find the factors that are either causing hindrance in their operations or variables that are providing the most value.

3) Predictive Data Analytics

Predictive Analytics is leveraged to forecast future performance based on historical data. With insights from Predictive Analytics, companies can amend the ways they operate to potentially change the outcomes.

4) Perspective Data Analytics

Perspective Analytics is used to predict the future based on the changes a company is willing to incorporate. For instance, if a company is determined that the sales will decrease in the next quarter with predictive analytics, decision-makers can change the strategies and perform perspective analytics to understand how the result can be influenced in the future.

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What are the Advantages of Data Analytics?

Data Analytics houses a wide range of advantages that make it one of the top emerging industries to work in the market today. Some of those advantages are:

  • Advanced Insights: With Data Analytics, companies can carry out in-depth analysis, which assists them in comprehending their business operations effectively. A clear understanding of business processes can eliminate confusion while making decisions.
  • Future-ready: Data Analytics allows companies to garner insights that help uncover shortcomings in business processes, thereby assisting them in decision-making to change the future outcomes. 

To learn more about Data Analytics, click this link.

Factors that Drive the Business Intelligence vs Data Analytics Decision

Now that you have a basic idea of both technologies, let us attempt to answer the Business Intelligence vs Data Analytics question. There is no one-size-fits-all answer here and the decision has to be taken based on the business requirements, budget, and parameters listed below. The following are the key factors that drive the Business Intelligence Data Analytics comparison:

1) Business Intelligence vs Data Analytics: Scope

The most significant difference between business intelligence and data analytics is the scope of work. While the former is about gaining operational insights, the latter is used for performing a wide range of analyses. With Business Intelligence, the idea is to build dashboards and prepare reports. But, Data Analytics goes a step further by finding correlations between different variables to determine the factors influencing the results. 

Business Intelligence will help you with a straightforward analysis to get an overall picture of the business operations. Data Analytics, on the other hand, assists you in obtaining intricate insights into business operations. For instance, with Business Intelligence, you can gain year-on-year sales performance. But, Data Analytics will tell you why there was a variation in the outcome.

2) Business Intelligence vs Data Analytics: Code

Coding requirements with Business Intelligence and Data Analytics are the complete opposite. Business Intelligence can be performed without coding as several tools allow professionals to drag and drop data for visualizing and building dashboards. Data Analytics, however, involves the use of programming language to carry out complex analyses. Programming languages like Python or R are mandatory for professionals to go beyond Business Intelligence for uncovering interesting patterns.

But, Business Intelligence can be performed with BI tools like Power BI, Tableau, and QlikSense. Although these tools have evolved and included features for Data Analytics, the scope for in-depth analytics is limited. Nevertheless, Business Intelligence tools are one of the go-to platforms for more straightforward Data Analytics requirements due to their ease of use and quick turnaround time.

3) Business Intelligence vs Data Analytics: Math

You can be a Business Intelligence professional even without core math skills like linear algebra and probability. But, a Data Analyst needs these skills to assess data in ways that cannot be carried out without customized commands.

Business Intelligence tools do have features for command, but you need to learn platform-dependent languages like Data Analysis Expressions (DAX) for Power BI. But, learning any command transcends business intelligence skills and lies in the scope of Data Analyst workflows. Maths is an integral part of Data Analytics, and it helps in analyzing data comprehensively.

4) Business Intelligence vs Data Analytics: Statistics

Business Intelligence is mostly related to descriptive statistics, which assists in finding the mean, median, and average. To go beyond simple analysis, you require statistical analysis like inferential statistics. Data analysis compromises descriptive and inferential statistics to better understand data and find insights with predictive analytics. For instance, with business intelligence, you can showcase a company’s current and historical sales performance, but Data Analytics empowers you to predict future sales based on historical information.

Statistics is also widely used to perform different A/B testing to help decision-makers make informed decisions with respect to the introduction of new features. Statistical analysis is the key to data analytics for finding critical insights that can have a major impact on the customer experience or revenue of a company.

5) Business Intelligence vs Data Analytics: Data Type

Business Intelligence is carried out on structured data that are curated for analyses with tools like Power BI and Tableau. But, Data Analytics is not limited to tabular data; analysts can perform analyses with text, audio, and video file formats. Analysts can leverage libraries like ‘requests’ and ‘beautiful soup’ to scrape structured or unstructured information from websites.

With Data Analytics, it is highly common to use unstructured data for uncovering insights. For example, a Data Analyst can gather information from Twitter using the Tweepy library and generate word clouds to understand the sentiment from collected data. Business Intelligence, however, is for leveraging tabular data for descriptive analysis, thereby limiting the spectrum of the use cases.

6) Business Intelligence vs Data Analytics: Data Quality

For Business Intelligence, Data Warehousing is mandatory as it transforms the data to improve the quality of information for streamlined Business Intelligence. But, Data Analytics is not necessarily dependent on Data Warehouses for analyses. A Data Analytics professional can directly collect information from Data Lakes or disparate sources. Data Wrangling is a routine task with Data Analysts, which Business Intelligence professionals do not perform.

Often Data Analysts have to enhance the quality of data before getting started with analysis. Cleaning data to make it suitable for analytics is a core part of Data Analytics, but this goes beyond the ambit of Business Intelligence.

7) Business Intelligence vs Data Analytics: Reports

Business Intelligence reports are mostly executed at a specific time based on business use cases. Although it can also be used for ad-hoc reporting, typically, the idea is to streamline regular reporting. Data Analytics, however, is very flexible with analyses as several new approaches are implemented to optimize reporting. In other words, Business Intelligence is for generating very standardized reports, but with Data Analytics, organizations can blaze a trail to come up with advanced analyses.

Conclusion

This article talked about 2 very similar concepts, Business Intelligence and Data Analytics. It gave a brief overview of them and explained their types and benefits. It also gave the parameters to judge each of them. Overall, the Business Intelligence vs Data Analytics decision depends on the types of analysis you are performing.

Data Analytics and Business Intelligence might seem closely related but are very different when it comes to the skills required. However, both have their own advantages that help organizations to stay ahead in the competitive world with data-driven insights.

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