How to explain Business Intelligence (BI) in plain English

What is Business Intelligence and what can it do for your organization?

Business intelligence (BI) doesn’t seem like a tech term that requires much introduction. It doesn’t even sound all that techie: We know what a business is, and we know what intelligence is – put them together and you’ve got what, a smart business?

That’s not that far off. The challenge with defining, understanding, and explaining BI is that the term tends toward the abstract. That’s really what it boils down to, though: Helping people and organizations to make smarter decisions based on all the relevant information available to them.

Today, every company on the planet has to be ‘data-driven’. Business intelligence is how companies actually achieve that.

Business Intelligence vs. Big Data

Consider the Big Data (with a capital B and a capital D) frenzy: You could have the biggest data, but that’s not worth much unless you can translate said data into usable information. BI is essentially a discipline (with a corresponding category of platforms and tools) that aims to make data-driven strategy and decision-making possible, instead of just a good soundbite.

Business intelligence refers to the strategies and technologies used by companies to analyze valuable business data beyond traditional simple reporting. Data is the most valuable asset of today’s industries, and if utilized correctly and securely, it’s the best way for company leaders to make informed decisions.

BI unlocks the power of data. On its own, data may not reveal much, and won’t drive consensus, reveal problems, or inspire solutions. But business intelligence has transformed enterprises, allowing them to gain insights and make decisions in real-time.

The key takeaway here: BI takes raw data that most people would not be able to make any sense of and turns it into comprehensible information that they can use to do their jobs well.

What is Business Intelligence software?

There are a wealth of BI technologies, from complete platforms to specific BI tools to applications embedded inside other applications (such as a CRM or ERP system).

Some of the most common business intelligence applications are data analytics tools, supply and demand planning tools, predictive analytics, and warehouse management systems. These automated applications take time-consuming, tedious tasks off the hands of employees, so they can spend valuable time on knowledge-based tasks.

Even a spreadsheet could technically qualify as a BI application – a handy, if relatively rudimentary, reference point when explaining BI to non-technical people. Speaking of which, wider audiences probably care most about how they’ll interact with BI, rather than how it works. Two of the most common user interfaces or user experiences (UI/UX) they’ll interact with are dashboards and data visualizations. Sometimes, those are one and the same, though not every dashboard is a graphical interface.

Dashboards are essentially the UI/UX payoff of BI’s initial promise: turning massive amounts of raw data into insights people can understand and use. They don’t need to be data scientists or have any other technical expertise to access, consume, and act on the data expressed in a dashboard.

Dashboards allow decision-makers and employees to visually see and understand business performance metrics at a quick glance. Business intelligence tools pull and analyze data, and dashboards present that data in an easy-to-understand format to improve business decisions.

What can you do with BI?

Given all the above, the better question is probably: What can’t you do? The data analysis possibilities are significant. One way to wrap your head around them and apply them to your particular business function or job is to think in terms of questions and answers. Let’s say a marketer wants to understand something about a particular customer segment’s behavior based on certain variables: Frame it as a question, and BI is the mechanism (or one of them) that can provide credible, valuable answers. Perhaps a logistics manager wants to understand a correlation between weather events in one part of the world and shipping delays in another. Again, pose it as a question. Turn to BI for answers.

This approach can be strategy-oriented but also results-oriented. BI can produce the metrics by which various business units measure themselves, as well as the information those departments use to make new decisions.

Is BI still relevant?

This one’s easy: Absolutely, positively yes.

The reason the question might come up in conversation is because the term itself is not especially trendy. Other terms and technologies have come along to soak up the limelight. It’s not even the hottest term with the word “intelligence” in it: That would be artificial intelligence, of course. By tech hype standards, BI has more in common with ERP and CRM rather than cloud or AI.

Business intelligence continues to be a huge part of many companies’ digital transformation strategy, especially to survive in this new normal. Predictive analytics, for example, is crucial for sales teams and C-suite executives to gain insights into purchasing decisions, future trends, and customer sentiment.

Again, it’s also a measurement discipline: “How did we do?” is as important as “what should we do next?” That’s also part of BI’s endurance.

Today’s decision-makers also rely on business intelligence tools to pull data reports that prove the ROI of the various investments a company has made. Since many companies are still working remotely, understanding this data is very important to see what’s working within the company.

This article, by Kevin Casey first appeared on The Enterpriser’s Project, and has been shared under the CC-BY-SA license.

Read the original story here – How to Explain Business Intelligence (BI) in Plain English