Business intelligence and business analytics both revolve around how a business can tap into available data to improve its decision-making. However, many people are unsure what separates the two terms, if anything. In this post, we’ll explain the difference between business intelligence and business analytics so you know what each of them is referring to and what that means for your own business. We will examine two different viewpoints. The first involves how business intelligence and business analytics describe different approaches to using data and gathering insight. The second focuses on how usage of the terms themselves has changed and what that means for their application.
Business Intelligence vs. Business Analytics: A Difference in Time
One way to differentiate business intelligence and business analytics is that both use data, but business intelligence uses historical data to learn from past decisions while business analytics is forward-looking and attempts to predict what will happen in the future. For example, business intelligence might describe a company’s attempt to examine why a marketing campaign did not draw as much interested as expected. Business analytics would use marketing data to predict how consumers would respond to a different campaign in the future.
Both approaches are important, and one is not greater or lesser than the other. The two are complementary in that they provide different insights and draw upon different sources. Neither one can answer all of the questions and solve all the problems a business has. They also call for different statistical techniques: forecasting is a different form of statistics compared to drawing conclusions from past data.
The difference is, to a degree, academic. Any business needs to understand its past and be able to look into the future if it wants to succeed. But it is still worthwhile to separate them because they require different datasets, skills, and tools. That means different employees will be working on each and each may use different vendors to supply the necessary software. From a logistical perspective, then, BI and BA can be defined by the extent to which they need different resources. This can vary from company to company. In some organizations there might in fact be considerable overlap, while in others the gap might be large. This comes down to the IT culture of each company and how it prefers to collect and process its data.
The insights drawn from each approach play into the company’s overall strategy. Business intelligence helps to show what works and what doesn’t work in retrospect. That’s critical for honing in on strengths and shoring up weaknesses. It’s difficult for a business to thrive when it cannot understand what attributes of its product are the most and least attractive. BI also guides how well the company met its goals. For example, products and campaigns typically have intended audiences that are the targets of marketing and design. Proper use of BI allows the company to determine if it succeeded in reaching that audience. Business analytics then carries the insights into the future. The company can attempt to model a particular change or new campaign to see how well it will do based on projections.
While the goals and tools in each approach differ, both rely on gathering as much data with as high quality as possible. Not having enough data means that you cannot rely on the conclusions you draw because the sample size is too small. Low sample sizes just don’t provide enough evidence to support insight. Likewise, if the data is hard to access, stored in a difficult format, or isn’t detailed enough, then it will be difficult to arrive at useful conclusions no matter what approach you are taking. The company needs to make an active effort to gather and clean data so that both processes can proceed smoothly. To that end, the exact border where business intelligence ends and business analytics begins perhaps matters less than a company’s data culture. Data is a resource and the business needs to be prepared to make full use of it in whatever capacity it can. That takes effort and investment of staff time and infrastructure. Moreover, as data and analysis in general becomes increasingly accessible thanks to cloud computing and inexpensive storage, the boundary will continue to blur. The end result is that it boils down to data and exploiting that data for all the information and guidance it can yield, no matter what the name of the process is.
A Difference in Terms
That leads us into another viewpoint on the difference between business intelligence and business analytics, which is that there is no real meaningful difference. According to this view, making use of data and information has been a part of business for many decades. The only thing that has changed is the terms we use to describe that process. The evolution comes as a result of vendors and other stakeholders who want to distinguish their new offerings from what came before. They introduce progressively newer ways to describe their products in order to convey how the most recent edition adds more value than previous generations. As a result, business analytics has simply begun to replace business intelligence as the way to discuss business-data products and services.
There is some evidence for this effect. If you use the Google Trends tool to compare “business intelligence” with “business analytics” you will immediately observe that usage of BI has been declining steadily while the usage of BA has been increasing over the last several years. That might indicate that the two are not really complements, but that industry use has shifted from one term to another. In other words, BA is just the newer version of BI, but they both refer to the same thing: using data to solve problems.
That is not to say that the distinction is without any meaning at all. It does matter that the business world wants to change how it describes data, because there is a real shift occurring now. Several recent advances have made it possible for more and more businesses to collect, store, and analyze data at a scale that was previously too expensive to contemplate. At the same time, giants like Google have demonstrated that it is entirely possible to let data lead product development and improvement. Data can lead you toward growth even before you have a completely settled business plan. Simply coming up with a way to collect data and then figuring out how to monetize it later has become a common theme in many startups. While that approach has its limitations, it certainly demonstrates just how powerful it is to come up with a unique way to gather or organize data.
The newest data products and services can handle greater sizes and scales of datasets than ever before and can do so with simpler interfaces and more powerful tools. That means less staff time and less training is necessary to tap into data’s power. A clean GUI and simple, approachable analytical tools means you do not need to have a fully-trained data scientist on staff to take advantage of the data you have. On top of that, the emphasis on cloud readiness means that the company does not need to own its own data architecture. That is a significant improvement because data storage and administration in-house can be expensive and time consuming. Cloud solutions are increasingly cheap, secure, and remotely accessible. The new world of business analytics is not just about coming up with new techniques or applications, but the open access to those tools.
Another major new trend is the ability to integrate different data projects together. For example, while it may be useful for marketing, sales, and customer support to all collect data, there are even more gains to be had by combining all of that data together into a single birds-eye view of the customer encounter. That concept is known as customer relationship management, or CRM, and it’s transforming how businesses approach their operations. CRM software draws data from every department and combines it for new insight that would not have been visible from one alone. CRM as an analytical toolkit is becoming cheaper and easier to use just like other forms of analytics.
All of this comes down to saying that the change from using the term “business intelligence” to “business analytics” denotes an important change in the relationship between business managers and data. Now, managers and owners need to be more conversant with what data can do and how they need to proactively harvest data to generate future returns. The importance of data hasn’t changed, but its accessibility has.
The bottom line is that the question of business intelligence vs. business analytics is secondary to the greater point: now is the time to commit to establishing standards and a method for using data. There are more tools and solutions available than ever before. Whether it comes from social media interaction, website and app interaction, purchasing and financing, email marketing, support, or any other source, it is hard to justify not taking advantage of the available data to guide how you create and market your product.
There are tons of different business intelligence and analytics metrics you can track. It can get a little overwhelming! Setting key performance indicators can help you spend your time analyzing only the most important metrics for your business. Not sure where to start?