Business Intelligence vs Data Analytics: 6 Major Differences You Need to Know

No matter the niche you are operating in, the ultimate purpose of your business, your competitive surroundings and such—data is king across all existing industries and workflow approaches. High-quality data drives business in a lot of ways, from outlining the detailed picture of all underlying processes to granting invaluable insights for future improvements.

To achieve that quality and keep it consistent, however, you must have not only your data in check but also expertly wield the relevant methods of gathering, analyzing, processing, and working through it in all other ways. This is where it is crucial to have a clear understanding of business intelligence vs data analytics. 

These two terms are mixed up or used interchangeably often, which is why we are here to help you make out the difference once and for all, and start ultimately turning both data analytics and business intelligence solutions to your advantage. But let’s start from the beginning.

Various productivity benchmarks can be built, relevant market trends spotted, and compliance indicators made out based on regularly collected BI.

In the long run, BI encourages error-free procedures with regard to verifiable and concrete data. It also provides information on the company’s records, which aids officials in assessing the company’s growth both economically and in other areas.

The technological processes of data mining, data cleansing, data transformation, and development of data management systems are all covered by data analytics. In order to identify trends and resolve issues, data analytics employs vast amounts of data to be analyzed across a variety of fields, including government and research, and is not only limited to corporate applications.

All of the mentioned processes enable, push, and streamline the essential aspects of business analytics, such as:

• Data mining (extracting and sorting big data volumes to make out patterns and trends)
• Data aggregation (collecting and structuring data for further analysis)
• Data visualization (converting gathered data into graphs and charts for simpler “naked-eye” overview)
• Business forecasting (analyzing historical data accumulated during certain periods of time for future business outcome estimation)
• Building predictive and other data analysis models (this is where data analytics may branch out into a bunch of formats with different purposes)
  • Descriptive analytics (pointing out major business performance occurrences, shifts, and trends based on historical data)
  • Diagnostic analytics (understanding past business performance occurrences, shifts, and trends)
  • Predictive analytics (forecasting future trends based on historical data)
  • Prescriptive analytics (coming up with specific actions to tackle future business performance and trends)

In such a way, a dedicated data analyst wields specialized tools and employs various approaches to first bring all the relevant data together and prepare it for processing, and then either describe, diagnose, predict, or prescribe data insights, workflow optimization efforts, and other data-driven goals and specifics.

With that info in mind, let’s dig into the business intelligence vs data analytics discussion.

Major Differences Between the Two

To give you a fuller understanding of the picture right away, here is how business intelligence and data analytics differ in a nutshell:

• Business intelligence is a set of tasks and approaches focused on the foundation of existing operations.
• Data analytics is a set of processes for working with collected data (analyzing, processing, etc.) for further business improvements and innovations.

And while both are focused on expanding business potential in a smart, data-driven way, it is important to make out the difference between data analytics and business intelligence in-depth. Let’s take a look at the major differences between the two, aspect by aspect.

Difference #1: Working with Data Insights

Both terms in discussion are heavily focused on acquiring and elaborating data-driven business insights in some way or another. However, the ultimate goal essentially differs.

Data analytics is more about creating insights. This can be done in a variety of ways, with a variety of tools, all of which help convert, clean, and format data into actionable insights. The resulting insightful initiatives can then be implemented to tackle a range of issues and goals, including business intelligence tasks.

Business intelligence is about using the excavated insights. In terms of BI performance, actionable insights are taken as a foundation for streamlined decision-making, as well as workflow organization, optimization, scaling, etc.

Difference #2: History and Predictions

Next up, there is a slight difference in terms of “data analysis timing.”

Data analytics can be fed various data inputs, including historical data, in order to build forecasts and outline potential future shifts and changes via predictive analytics. All in all, we can see that a definite focus is put here on the data-driven future.

In turn, business intelligence, while it helps inform business strategies to be implemented in the future, is focused only on past historical or recent data that helps get a better understanding of running processes. Instead of forecasts and predictions, BI outlines the existing, relevant state of things.

Difference #3: Raw vs Structured Data

The initial format of data employed in terms of business intelligence and data analytics also differs.

Thus, most tasks in data analytics are kicked off with raw, unstructured data (which is also often extracted in real time). The essential task of any data analyst, which we mentioned above, is to clean up that data and bring it to proper order.

In order to carry out BI tasks, specialists employ data that has been previously structured and is stored in specialized storage (database, data warehouse, cloud, you name it). Based on neatly pre-organized data sets, they can make descriptive business summaries and reports.

Difference #4: Who Uses What?

With all the above-highlighted specifics in mind, it comes as no surprise that different specialists employ different BI and data analytics techniques.

Data analytics is the work handled by specialized professionals, including data scientists, data analysts, and, in some cases, software developers and engineers. This is a narrow-profile field that requires both theoretical knowledge and practical skills.

On the flip side, BI is a prerogative of non-technical specialists, including Top and Project Managers, Team Leads, Chief Information Officers (as well as other chief executives), and heads of financial departments. 

However, despite the logical subdivision of profiles, you can still occasionally see data analysts that handle both data analytics and business intelligence tasks (which is more of a universal approach enabled by the in-depth data-driven education of such professionals).

Difference #5: Business Coverage

The two terms in discussion cover different extents of underlying business aspects.

Data analytics is about going in-depth, focusing narrowly on a certain task or issue, and finding a specific answer or approach to it through dedicated technical analysis.

Alternatively, BI processes are more about outlining the big picture of business workflows and performance specifics. A lot of business “surface” decision-making and strategizing is carried out in terms of BI without going into a lot of technical details.

Difference #6: “Dirty Work” and Clean Strategizing

To sum up the overview of the major differences between business intelligence and data analytics, we can safely state that there is a clear, thick line between the two business-running initiatives.

Without doubting its paramount importance, one may deem data analytics “dirty work” that requires specialists to go in-depth, course through the core data-related mechanisms, and achieve results with a range of expert tools.

Equally important to the overall business success, BI, in turn, is far less nuanced. Specialists here work with comprehensive reports, pre-organized data structures, and easy-to-grasp tracking, monitoring, and visualization tools.

Essential Takeaways

The bottom line is, you can clearly see exactly why the terms we discuss are so frequently mixed up, combined, or used interchangeably. In real-world practice, tasks, techniques, and certain other underlying factors often seep from data analytics into BI, and vice versa. 

Having all the nuances straight, however, arms you with knowledge that will certainly help you smoothly manage workflow specifics, set business priorities straight, and even cut costs by employing exactly the specialists you need. Just keep in mind that you can elaborate and set relevant goals with data analytics while BI techniques will help you achieve those goals.

To make your life easier, here is a cheat sheet table that should help you make out business intelligence vs data analytics.

Bottom Line

The argument of data analytics vs business intelligence is a very common one, and a lot of companies and entrepreneurs out there tend to confuse the approaches or not give fair attention to their meaning whatsoever. That only puts them a step behind knowledgeable market players that have their priorities straight and know the proper means to achieve goals.

Turn to experienced professionals that will guide you through the nuances firsthand and clarify the most individually efficient approaches to both data analytics and business intelligence. At NIX, we have whole separate teams of data scientists and BI specialists ready to tailor the powers of high-quality data to your business needs to give you a sharper competitive edge and streamline sales.