87% of companies haven’t reached BI maturity. Will AI be the breakthrough?

With the majority of businesses still saying they aren’t at business intelligence maturity, we explore how AI will help them mature over their BI analytics over next decade

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Today, a whopping 87% of businesses have yet to reach business intelligence (BI) maturity (Gartner). This means that, despite the indisputable power of data analytics, the majority of organisations are still struggling to uncover the critical insights needed to track success, convert customers and build digital strategies that succeed.

“Low BI maturity severely constrains analytics leaders who are attempting to modernize BI,” points out Melody Chien, senior director analyst at Gartner. “It also negatively affects every part of the analytics workflow. As a result, analytics leaders can struggle to accelerate and expand the use of modern BI capabilities and new technologies.”

Some of the areas Gartner identified businesses were struggling with were:

  • Primitive or aging IT infrastructure; limited collaboration between IT and business users
  • Data not being linked to a clearly improved business outcome
  • BI functionality mainly based on reporting
  • Bottlenecks caused by the central IT team handling content authoring and data model preparation

With multiple teams, dozens of digital channels and hundreds of datasets, keeping track of all your data is a significant challenge for every business. And, since the amount of data in the world doubles every two years, this is a challenge that’s growing.

READ MORE: Data-driven to madness: What to do when there’s too much data

Luckily, there’s one budding technology that can automate and streamline business intelligence exceptionally well: Artificial intelligence (AI). And it might just be the breakthrough businesses need to mature their BI efforts over the next decade.


How does AI supercharge business intelligence?

Artificial intelligence (AI) is a way of making computers think intelligently, like humans do. AI algorithms are able apply problem solving measures and mimic human intuition to distil greater insights from datasets. These capabilities mean that when AI is built into BI tools, it supercharges the delivery of clear, useful insights and more efficient data analysis – taking business intelligence to the next level.

While it might have human-like intelligence, AI doesn’t get tired or make mistakes, so it can process data 24/7. It can also drill down into data much more effectively, so it’s able to understand each datapoint at a granular level. All this means AI is significantly more efficient and reliable than even the best data analyst or traditional BI tool.

If you’re struggling to use or modernise the business intelligence you already have, implementing AI probably sounds intimidating. But it shouldn’t. AI not only makes the gathering of insights from data much more effective, it also makes it easier. This means it’s the perfect tool for helping businesses reach the business intelligence maturity sorely needed to remain competitive today.

Let’s look at some of the ways AI makes business intelligence easier, more efficient and more accurate.


Automated, 24/7 data analysis

As mentioned, artificial intelligence is incredibly effective at data analysis. In fact, AI is even better than the most skilled team of data analysts, because it excels at crunching data and recognising patterns – two things humans tend to be terrible at.

Because AI does data analysis so well, it can constantly scan through multiple datasets to identify the critical insights that help power business growth. Since this analysis is being done by a computer, not a human, it’s done with incredible speed and efficiency, and can be scaled with ease. That means that you’ll no longer have to dedicate hours and hours each week to looking through dataset after dataset and spreadsheet after spreadsheet to find all the insights you need. With AI, this process is automatic, with insights delivered straight to you.

With a complete, in-depth analysis of all your data, you can be much more competitive, even with the industry Titans that have dozens of the best data scientists at their disposal.

READ MORE about how AI supercharges data analysis


Finding real-time anomalies

Anomalies are always going to pop up in your data, one way or another. And identifying them before they have a negative impact on your digital performance is critical. If a checkout page is crashing, for example, that’s going to affect your revenue pretty quickly, so catching that as soon as possible is important. But anomalies aren’t always visible to the human eye – or on traditional BI tools.

READ MORE: What is an anomaly?

Take this anomaly, for example. It’s very clear that there’s been a drop (to 0) in the amount of traffic arriving to this website. But how about the anomaly in this graph?:

If you’re struggling, you’re not alone! It’s nearly impossible to see the anomaly in this graph of user visits to an app. In fact, finding the anomaly would probably require in-depth analysis from a trained data analyst.

But an AI can pick up anomalies that are invisible to the human eye in a fraction of a moment. And the anomalies that tend to go overlooked in traditional BI tools can be identified.

What’s more, an AI can find these anomalies in real time. This means that, rather than you waiting for reports to come back and identifying anomalies after the fact, you can take immediate action.

In a world where customer expectations are sky high and competition is fierce, being able to respond in real time is a huge advantage. And real-time anomaly detection has a number of incredible uses, from fraud detection, to in-depth customer behaviour analysis, to detecting technical faults immediately.

Learn more about AI anomaly detection.


Predictive analytics

BI tools traditionally provide descriptive analytics for companies. That means they let them know what’s already happened in their data, and allows them to uncover insights on past behaviour that will inform future plans and strategies. However, with it’s ability to analyse patterns and think intelligently, AI is getting better and better at predictive analytics: Predicting what’s going to happen with data in the future.

Businesses can use these predictive analytics capabilities to determine customer behaviour or purchases, promote cross-sell opportunities, forecast inventory, manage resources and more.

Learn more about predictive analytics.


How AI helps companies mature their business intelligence

To summarise, AI can help businesses advance their business intelligence capabilities in a number of ways, including:

  • Automating data analysis, making it more efficient and reliable
  • Identifying real time anomalies and digital performance opportunities
  • Providing predictive analytics, not just descriptive
  • Replacing the need for data analysts by unearthing critical insights automatically
  • Taking data analysis beyond reporting, and instead giving teams actionable insights they can address immediately to improve digital performance

Getting started with implementing AI in business intelligence

Because it’s able to enhance data analysis so effectively, AI is a perfect technology for supercharging traditional business intelligence. And many BI tools today are beginning to integrate it into their capabilities, from simple automation features to more complex, prediction-making algorithms.

An anomaly as it appears on the Millimetric platform

If you’re still struggling with business intelligence maturity, starting with a simple AI tool will help you quickly and easily up your data analysis. So why not try out the free version of our AI anomaly detection tool, which automatically alerts you to unusual behaviour in your metrics so you can understand your data better?





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