Businesses today all agree on the fact that data is their new goldmine. Making sense of it is their number one priority, giving them priceless insight into their customer base, their operations, their successes and challenges. To help them harness data, Business Intelligence (BI) was developed in the 90s to offer organisations the ability to work out all the insight they need from volumes of data, freeing up human time to focus on decision making and thinking creatively.
But organisations quickly realised that this dream was a bit too good to be realistic, and even the most modern BI tools weren’t delivering the intelligence they promised. They are now trapped in a state of continually trying to gather and combine data efficiently from numerous sources and inputs, into one central place. But businesses need the insights now to have a real time overview of what is happening in the organisation, and today’s tools are simply not up to the challenge of delivering on this. We might end up getting those insights eventually, but who can wait weeks when the board is asking for it yesterday?
And even if you can find out what happened, that’s no longer enough. We need to know why.
A new approach to data analytics
The data explosion means organisations are scrambling for fast, trustworthy, actionable intelligence. Making sense of data from increasing volumes of sources and spotting unusual behaviour is incredibly tough, with data spread and constantly moving across increasingly complex IT infrastructures and a potentially huge number of internet-connected things. It’s almost impossible to keep track of the pace at which data is created nowadays, making businesses’ lives incredibly difficult when it comes to understanding why behavioural abnormalities and issues happen when they do. Yet, at the same time, we’re under more pressure than ever to understand what’s going on in our department or organisation.
Businesses have, of course, long tried to address this challenge: any organisation with more than a handful of employees will have some form of tool they use to attempt to show what’s going on. Enterprises have dozens if not hundreds. Add in all the partners, suppliers, agencies and data silos that form the hyperconnected nature of modern business, and the data landscape (and the tools trying to make sense of it) becomes an overwhelmingly complex picture.
So overwhelming in fact, that current business intelligence tools are simply not up to the task – they’re too slow and cumbersome for today’s demands. Too much time is wasted needlessly processing and analysing old or unverified data and creating visualisations that have nothing to say. Businesses cannot afford to continue wading through the labyrinth of data for every ad-hoc request they get, particularly when the initial results need refining. By the time an answer emerges, it’s no longer relevant. Yes, after much wrangling with the data, you can get some powerful insights. And yes, there are products that can produce really nice visualisations to help illustrate the findings.
But to find out why things are happening, and allow businesses to act on it immediately, a new approach is required.
The age of augmented intelligence
Augmented intelligence and machine learning are transforming every industry. And few areas are more suitable than data management to exploit the power of machine learning. There’s a huge potential to put machine learning to work crunching large volumes of data across complex models, finding hidden insights and alerting employees to take action, is tailor made for algorithms to augment human intelligence.
In business analytics, the principal benefit of Augmented Intelligence is to help business leaders understand issues as they arise and, in the future, predict trends that will benefit the business. And, crucially, to do this quickly, so that businesses can immediately act on insights.
But where AI really comes into its own is by adding the why to the what. Because however much data traditional BI tools can analyse, finding the root cause is beyond them. The limit of traditional tools has been reached.
Whether it’s used to get a deeper insight into breakdowns in customers journeys, unearth previously unexplained anomalies in financial reports, or find regional blips in product launches, AI is required to know what’s unusual and automatically flag it, ensuring actionable insights at speed.
What next?
Ultimately, the goal of smart analytics platforms is to free employee time to make intelligent decisions based on the data gathered and analysed by the platform. This type of decision making requires skills that only humans possess, but smart interaction between human and machine (through automatically surfacing insight) is just as crucial to the decision-making process.
The new challenge for businesses is to aim higher than finding out what happened. The new differentiator is understanding why it happened. This is what is going to give businesses the competitive advantage that they need to succeed and using that knowledge to learn and improve.
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