Published February 9, 2023
Why Business Intelligence is Important
Discover the significance of Business Intelligence and how it helps organisations make informed decisions, improve efficiency, and increase profitability. Learn about the benefits of BI and its impact on business success.
What is Business Intelligence?
If you’ve been in the business world for a while now, you probably have some idea of what Business Intelligence (BI) is. In short – Business Intelligence involves leveraging and analysing data to improve decision-making.
That covers a pretty wide range! The above definition covers so much more – it also broadly includes reporting, benchmarking, and more advanced techniques like text mining and forecasting. These days, you’ll typically hear BI referred to in the context of self-service Business Intelligence tools, like Power BI and Tableau, differentiating it from traditional reporting or more advanced analytics.
But in a broader sense, a Business Intelligence tool is any tool that improves business decision-making with data. And a Business Intelligence analyst is someone who transforms, analyses and presents data to business stakeholders, to enable this better decision-making.
In a sense, we’re all likely familiar with the concept of using data in our lives to make decisions. We use the data from our monthly debit card spending, to track our personal and household budgets. We use the distances between key landmarks and accommodation to determine the best route to drive on a road trip. Business Intelligence is simply applying this principle to the business world, on a much larger scale.
So why is Business Intelligence important?
Why is Business Intelligence important?
Data > Intuition
As humans, we tend to intuitively trust our judgement on a number of topics. And why shouldn’t we? Often, the right answer is just common sense based on experience. We trust our intuition, and we trust it intuitively.
The problem is, there are countless areas in life where the ‘intuitive’ or ‘common sense’ solution is the wrong one. Even for experts.
To give a few examples:
So why are the above examples relevant? Because they highlight the stark contrast between what we intuitively think to be correct, versus what is actually correct based on the data. And always making decisions based on intuition, can lead us to making worse decisions in the long run.
If you ever ask someone for their reasoning behind a decision and they answer “it’s just common sense”, chances are that they don’t have the data to support their decision – or worse, they do, and they’re choosing to ignore it in favour of intuition.
We live in a world where an unprecedented amount of data is available for almost every area you can think of. So why do people continue to rely on intuition for their decision-making?
Why People Use Intuition
- Intuition is often faster and more easily accessible than data. It’s no secret that data can be difficult and time-consuming to work with sometimes, particularly when you don’t have the right data in the right format at the right time. These situations should provide a strong catalyst for proactively preparing your data systems, so you’re ready with answers the next time a decision needs to be made. But this often doesn’t happen, because…
- Decision-makers may be overwhelmed by the data at their disposal. Remember that comment about decision fatigue earlier? Overload of information is a real thing, and this is a good case for designing your data presentation in a way which gives decision makers the right information, but not too much to cause cognitive overload. Presentation is important because…
- Decision-makers may not understand the data. This is another situation where design and presentation is key. Bridging the communication gap between the technical data world and the business world is a critical skill. Data doesn’t have to be confusing, but it becomes confusing when analysts present the data in the way that they would want it, rather than in the way someone else wants it. This is where stakeholder engagement bridges the gap – so analysts and business representatives can speak the same language. But even then…
- Decision-makers may not trust the data. This may happen for a variety of reasons – data capture mechanisms, questionable data quality, or they may simply believe that their intuition is better. Past experience also comes into play here. If a decision-maker has had a bad experience in past with data-driven decision-making (eg. they made a choice based on data and it turned out to be wrong in hindsight), they may be less likely to trust data in future.
You’ll note that there is a pattern with all of the above reasons – ultimately, they all come down to decision-makers being comfortable and confident with readily-accessible data. And while there is an element of improving data literacy and education which plays into this, ultimately it comes down to proactive data preparation and presentation.
If you are having to start from a blank slate or generate an ad-hoc report every time you want to make a decision, of course using intuition is going to be easier. This becomes a barrier to entry, and this is where we cross the bridge from BI to Self-Service BI.
So how do we break down that barrier to entry?
Breaking Barriers with Data
- Firstly, engage with stakeholders about what they need to know to make good decisions. In an ideal world, we have every piece of data about everything available at our fingertips, ready to make decisions. Unfortunately, we live in the real world, where we have limited time and budget to do everything. Speaking to stakeholders to understand their highest priority needs, based on what can deliver the most value to the business, is the way to uncover where these limited resources can be spent. This can also help to build trust and buy-in from stakeholders early.
- Understand and prepare the right data, proactively. Having this data ready in advance means you’re not starting from a ‘blank slate’ every time. This involves identifying the data which supports the required decisions, validating data accuracy and completeness, identifying potential gaps, and taking steps to fill these gaps. The next time an executive asks a question about tracking employee turnover over the past three years, or for a breakdown of CapEx spending per department, you will have the data and the tool pre-prepared ready to go, in anticipation of these types of questions.
- Iterate with stakeholders to present this data in the right way. This begins by identifying the right format to display the data – a presentation often provides far more clarity than an Excel sheet. Creating visualisations (charts & graphs) or visual mockups is the first step – but to truly understand if you’re showing the right data in the right way, you actually need to get your solution in front of stakeholders. Doing this repeatedly can help the analyst identify which visualisations provide the most value, and which visualisations are either confusing or irrelevant, allowing the analyst to make changes to meet the business needs better.
- Build trust and understanding of the data. Once presented data is readily available to the business, you will start to get a feel for which stakeholders are early adopters, and which are late adopters. Some people may dive right into the data, while others may need more time and guidance for learning how this data can make their job and their decisions easier. At the end of the day, people won’t change the way they work if they don’t see the value. Helping people to show them the value through training and education is key to having a truly data-driven business.
As consultants, we at Aginic are experts in solving these kinds of problems. Business Intelligence is about far more than just analysing data – at heart, a good analyst is both deeply technical, but also a strong communicator, who asks the right types of questions to the right people.
Analysing data is only half the battle – helping others make good decisions is the end goal.
Get in touch today to learn more.