What is Business Intelligence?

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“So, what is Business Intelligence?”

This is a question that comes up a lot. Everybody in business has heard of it. Everybody in business seems to recognise that they should have it. But it’s one of those things that, when you ask people to get specific, people struggle to really define. People have a sense of it rather than a really clear idea. So, in the next 600-or-so words, let’s see if we can nail down a nice simple definition.

This difficulty partly comes down to the fact that Business Intelligence, or BI as it’s more commonly known, is more of a concept than a tangible thing. A bit like “Cyber” – a word that gets thrown around constantly but is really just a label for a number of concepts and ideas under one convenient umbrella term.

10 or 15 years ago Business Intelligence was usually a term that was interchangeable with Management Information. It was often just used as a smart-sounding term for pulling together reports of what was happening in a business last week or month or year. But a lot has changed over the last two decades as data has gone from something only used in a really concerted way in sectors like research and high finance, to something that we’re all aware of as individuals, and that is used to power apps and devices that we’re all in contact with every single day.

It arguably can cover anything from a small business wanting to understand the relationship between customer satisfaction and call answering times, to massive corporations like Mark Zuckerberg’s Meta analysing your social media usage to target advertising to you across a wide range of smartphone apps like Facebook.

As a result, like “Cyber”, it’s an umbrella term that covers a massive range of other ideas and concepts. It means many things to many people. It incorporates pulling together data from disparate sources into a useable whole. It incorporates AI, machine learning, and data science. It relies on Data Governance, Data Quality Management, and data cleansing. It incorporates everything from simple backward-looking management information measures like Key Performance Indicators (KPIs), and it incorporates hugely complex forward-looking predictive analytics like those used by Facebook and Amazon.

But, ultimately, they’re all just tools and techniques to achieve the same broad goal: to use data to provide answers.

So, what is Business Intelligence?

Time to nail my flag to the mast and commit to a definition:

Business Intelligence is turning your data into answers that are useful to your business.

This simple model (the DIKW Model) sums it up the concept neatly:

In this model, you are simply moving up from your raw data at the base of the pyramid, all the way to an actionable insight at the top, by joining data up and adding your own context to it.

You’ll hear a million-and-one different names, technical terms, and bits of jargon on the subject of data and BI, but it really all distils down to this one idea. All of those tools and techniques like AI and Data Science are ultimately just methods for achieving that same journey shown in the DIKW Model – of turning your data into answers that are useful to your business.

Whether your business is about to embark on a journey to introduce Business Intelligence, or if you’re already some distance along that journey, there is always value in keeping that simple definition in mind.

All of your decisions about investing time, effort, and money in BI should come back to that. If you’re presented with a proposal for developing any sort of data solution in your business, ask yourself two things: Is it going to give you answers that are useful and valuable to your business? Will having an insight into trends or information allow you to do something differently and more successfully? If the answer to either of those questions is “yes” then you are on the right track.

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