Big Data has exploded in popularity.
And as is with any new idea blown in proportion, we embraced it without actually understanding what a lot of it really means.
How common have terms like “data analytics” and “business intelligence” become — umbrellas for a wide array of tools and technologies that are believed to be vital to running the modern business.
But what are they? Are they the same thing?
Many people would say yes, since they are often talked about in the same breath.
But they are not.
Turns out, there is a difference. A big one.
What is data analytics?
Data analytics is the practice of collecting and analyzing data. But that does not explain anything.
Here is a slightly more advanced definition: data analytics is the practice of collecting, storing, and sorting raw or unstructured data and analyzing it to harvest structure or meaning from it.
The structure or meaning emerges in the form of patterns or trends, which are commonly referred to as insights.
Here is a simple example.
Suppose you track all the purchases made by your customers, online and offline.
If the data set is small enough, you can examine it yourself to discover insights such as people who buy Product A are also likely to buy Product B.
Now, whenever someone buys A, you could also recommend them B, since your analysis suggests that B well complements A.
Consequently, you drive cross-sales, and hence revenue.
Congratulations, you have just used sales analytics to drive growth!
Okay, that should do. Let us now move on to business intelligence.
What is business intelligence?
A business is considered intelligent when it makes strategic decisions based on data instead of intuition.
Such decisions are identified with the aid of a range of tools and software.
What business intelligence enables is the ability to understand insights and use them to develop a plan or strategy to drive growth or mitigate risks.
In our example above, let us suppose that your customers are not in tens but in hundreds or thousands.
Now, given the problem’s scale, data must be analyzed by a sophisticated tool.
But that is not it.
Next, the trends or insights must be reported and visualized, so that they can be understood. Data visualization provides a visual measure of the gaps in your strategy, so that you can come up with innovations to fill them.
Remember that the insights and the new course of action also have to be relayed to your team.
Analyzing, reporting, visualization, automating — business intelligence solutions offer all four.
Can you see the difference?
The difference between data analytics and business intelligence
One difference is right in the name: data analytics is a broad concept that covers everything data, from collecting and cleaning to analyzing and applying. It can be used by any enterprise, private, public, or scientific.
Business intelligence, however, is chiefly the object of businesses who wish to operate more productively, efficiently, and intelligently, in general. BI is more practical and application based.
Application! That is key here.
It is not just the fact that data analytics is more general and business intelligence more particular.
The difference becomes clear when we ask the right question: what is our goal?
Modern enterprises use analytics to make strategic decisions through a three-step process:
- See what has happened/happens
- Predict what will happen based on that information
- Make a plan based on that prediction
The first step is called descriptive analysis. The second step is called predictive analysis. And the last step is called prescriptive analysis.
Notice the circular nature of making decisions? In other words, the feedback loop?
Describe. Predict. Prescribe. Repeat.
Data analytics is chiefly concerned with predictive analysis. It involves the use of cutting-edge coding techniques and algorithms to make sense of collected data, to draw insights that inform predictions or forecasts.
Data analytics, therefore, demands more technical know-how. It demands expertise in mathematics, algorithmic coding, quantitative analysis, and other disciplines at the heart of solving problems with data.
Business intelligence, on the other hand, is chiefly concerned with descriptive analysis. It involves the use of tools that facilitate and visualize the inputs and outputs of data analytics.
Business intelligence, therefore, requires, not technical know-how, but application know-how: how do I use the tool? It is fair to say that mastering a business intelligence tool is much less demanding than mastering algorithmic coding.
To answer, then, what achieves what goal — data analytics tells us why is something the case, while business intelligence tells us what can we do about it, and how.
In the case of our simple example above, data analytics helps us identify why your customers also buy Product B when they first buy Product A — they are complementary.
Though it is on business intelligence tools that we make the analysis. Further, the tools help deliver, report, and visualize the insight in the form of a graph.
The tools help make sense of data, turning it into a plan of action (prescriptive analysis) — make Product C that complements A and B, driving even greater cross-sales.
Here is a more realistic and complete example.
You sell art online.
Business intelligence tools enable reporting of the past and current purchases of your art.
The tools convey that in the past month, sales have surged in your local area.
Of course, to keep up with the demand, you increase the supply: you create more art.
Data analytics, however, asks, “Why did sales surge?”
You collect and analyze your website data and learn that most traffic can be traced to a local influencer brandishing your art on her blog.
In any case, you use the historical data to anticipate what, and how much, art will you need to create to keep up with the demand.
How much do I need to invest in supplies? What about reaching out to more bloggers? Which ones? And why?
Why vs. What, and How. That is the difference.
Of course, the two work best in combination.
Data analytics is as critical to business intelligence as business intelligence is to data analytics.
How can we analyze data if we don’t even understand it? And how can we make big changes, spend valuable resources, without knowing why?