In the two decades since big data became a ‘thing’, business intelligence (BI) has been a vital part of extracting valuable insights from it. BI tools began with basic analytics features, before developing to include more powerful suites of tools able to aggregate huge swathes of data for analysis. In more recent years, BI capabilities have evolved to include dynamic visualisation and real-time insights.
Leading companies invest millions into BI in the pursuit of better customer experiences and ROI. Yet the era of business intelligence is coming to an end, thanks to the advent of a much more powerful technology: Artificial intelligence (AI).
Business intelligence vs. artificial intelligence: What’s the difference?
Understandably, there remains some confusion about the difference between BI and AI. Both technologies help businesses harness data to aid critical decision making, after all. However, there are some very significant differences between the two.
What is BI?
BI tools are used to gather, store, access and analyse data in order for businesses to make better decisions. The technology takes datasets and turns them into meaningful insights, typically by using reports and dashboards. It’s used to help understand historical data, so provides businesses with descriptive analytics (by which we mean it can be used to help businesses understand what has actually happened in their digital metrics).
What is AI?
To put it simply (and trust us, we could dedicate thousands of words to the matter), artificial intelligence is a way of making a computer system think intelligently like humans do. AI algorithms learn from past experiences, are able to apply problem-solving measures and mimic human intuition. The technology also has the capacity to be used for prescriptive analytics (by which we mean it can be used to predict future data and business trends).
If you want to learn more about AI, we’d suggest checking out Towards Data Science’s great intro on the topic.
The limitations of BI today
BI tools help organisations stay on top of all the data they process and draw meaningful conclusions from it. Instead of users having to use spreadsheets to manually process data, with BI they can more easily locate the insights which help them make better, data-driven decisions. It focuses on giving the answers to things you need to know in regards to digital performance, such as how well your site is performing, which campaigns had the highest ROI, or what customer journeys the user is taking.
Yet BI is only focused on historical data. It offers insight into what has already happened, not what’s happening right now, or what’s going to happen in the future. It’s limited to simply showing users what the result of an action was.
Furthermore, with BI tools, the human still has to be involved in drawing conclusions from the data. The BI might analyse the data, visualise it and provide insights, but the employees are the ones who have to crunch that data, find patterns in it and understand what it represents in the broader business context. But, with the huge amounts of data businesses are now gathering, this is getting harder and harder by the day. As a result, 65% of companies report having ‘too much data’ to analyse and turn into actionable insights.
AI: Real time insights and intelligent conclusions
With AI, or, more precisely, machine learning (a subset of AI), these conclusions can be delivered immediately, no matter how much data there is to analyse.
This is because machine learning algorithms can mimic humans’ ability to detect patterns – with much greater accuracy. Thanks to its intelligent ‘thinking’ and capacity to comb through multiple datasets in a fraction of the time humans can, the technology can infer more complex patterns, rules and interactions between different dimensions and variables. This means it’s able to detect issues in data as soon as they happen and deliver insights in real time.
AI is particularly helpful for companies who are dealing with a lot of data. For example, if you have hundreds of campaigns on the go, it’s difficult to see which ones are underperforming. With standard BI tools, you’ll have to wait to see the performance report before you can see which campaigns didn’t deliver, which could take weeks. Without manually digging through thousands of metrics across hundreds of dimensions, you can’t see where the issue is in real time to fix it before money and time is lost.
But AI can do this for you in moments – and deliver these insights straight away.
How automated data analysis supercharges digital performance
AI tools that automate data analysis, like our platform Millimetric, massively speed up the time it takes to detect issues in vast datasets. That means that companies are able to take action immediately, rather than after the fact.
For example, we worked with an online retailer who struggled to keep up with new browser updates that kept causing their site to crash. With their existing BI tools, there was no way for them to see the issue until it was flagged in their reports, sometimes weeks later. As a result, the software team was wasting time searching through data to see the patterns and trends that indicated the browser had crashed. But, when they integrated their data with our automated KPI tracking platform, Millimetric, the AI algorithms were able to immediately identify the unusual data and alert the user. This ended up saving the client’s software team hours each week which could be put to much better use.
Is it over for BI?
Of course, BI isn’t going to just die out completely! Many BI tools (like Millimetric) are starting to use AI technology to gather more powerful insights. And companies utilising these new, more powerful BI/AI hybrid tools are reaping the rewards, with supercharged customer experiences and ROI.
But, over the next decade, we’ll see the BI tools that don’t evolve to harness artificial intelligence and machine learning technology becoming obsolete. In this data-driven age, innovation is happening faster and faster, and BI on its own simply can’t keep up with the requirements of today.
Want to learn more about how AI supercharges data analysis? Check out our blog: How AI supercharges data analysis and KPI tracking.