Data and information are becoming more and more important for businesses. While it used to be that only real hi-tech giants like Google and Facebook were experts at consuming and leveraging data, that has changed. As time goes on, businesses must become better at working with data; those that don’t will be left behind.
There is a lot of innovation happening in the AI (Artificial Intelligence) space, and there is a very symbiotic relationship that exists between data and AI: the more data that AI has, the better it tends to perform. While there are numerous ways of applying AI within your business, in this post I’m looking at how you can use AI to leverage all of your organisation’s data more easily, with greater impact and revolutionise reporting.
The state of reporting right now
Many businesses today suffer from having too much data. Ingesting all that information, preparing the reports, building dashboards – all of this takes time and effort, but does it really help the business? In my experience, much of this data is ignored or eventually forgotten.
I believe the key to addressing this problem is AI, and transforming the way we present information to our colleagues and departments. In the same way Facebook or Amazon provides a personalised experience for their customers, AI can provide a personalised data experienced for everyone in your organisation.
Focusing on the people
We need to recognise that there’s no ‘one-size-fits-all’ solution when it comes to reporting: John in marketing doesn’t need to know the same stuff as Mary from sales. Just because you can access information doesn’t mean you should.
While this sounds obvious, the same is true within departments or even small teams. For example, every member of your sales team needs subtly different information based on their goals, role, and interest.
But it’s not just the type of information; it’s how we process it. The reason your daily sales reports are full of graphs and charts is because most people are great at processing information visually – but what about the rest? For some, a line graph or text summary is infinitely easier to digest.
In an age of flexible working, members of the same team can also have significantly contrasting habits. Say one member arrives at 8am and another at 10am: if your daily report is circulated at 9am, then each user will consume it at different times and neither of them first-thing on arriving at work.
Sure, they’ll read it eventually, but if we could optimise report generation so that it arrives at the ideal time, this will mean an overall efficiency improvement within the team. Such subtle improvements are often the key to reaching major achievements.
Questioning the status quo
The most crucial area for improvement is also the most counterintuitive: we must reduce the number of reports each person sees to the absolute minimum to revolutionise reporting.
There is a tendency to send all information to all people, just in case. Why do we do that? Consuming irrelevant reports not only disrupts your focus, but those 10-minute disruptions add up on the clock. Perhaps more crucially, exposing team members to only the most relevant data helps them focus on what really matters, something which can only make them better at their job.
Why now is the time for AI
So far I’ve talked about some of the different ways we all consume information. But let’s take a step back and revisit why we’ve gone to the trouble of generating all these reports in the first place.
At its core, data represents the heartbeat of the business, and these reports offer the best approximation for judging the current health of the company. Getting this information is the final step in a fine-grained feedback loop, helping us evaluate our recent activities and judge whether we are heading in the right direction.
Without the right information, however, we could start moving in the wrong direction and making decisions which harm the business.
Learning to provide the ‘right’ information to revolutionise reporting
Fundamentally, I believe we can leverage AI and machine learning (ML) to provide a much more personalised data experience for users. All of the factors I’ve described above – plus others – could be fed into a machine learning model which then gives you a much more personalised reporting platform .
An overall feedback loop would also be put in place. The user would use this loop to indicate whether they are getting the right information or not, therefore continually refining and improving the model.
In essence, the ML model would help determine which information to display to which user, in which format, and at what time. A module or layer would also be created that took decisions from the ML algorithm and sent customisation commands to the reporting app to make those necessary changes.
How would it work?
The reporting application would need to capture historical information on which users accessed which reports at what times. It would also monitor which reports they accessed most frequently spent the most time looking at. Manual data would be gathered on the personal preferences of each employee. All of this would be amended or approved by managers to ensure that each individual’s goals aligned with the department and overall organisation.
For optimal performance, the algorithm would also need to factor in nuances of human behaviour, in particular how habits are formed and broken. Working properly, this augmented approach would seem effortless to the end user. In fact, such a system would almost merge into the background, like an invisible butler that provided useful information at a convenient time.
This approach is a significant departure from how reporting is done today, it would simply revolutionise reporting. To accomplish it will require not only cutting edge machine learning, but also a change in mindset to how data is consumed at an organisational level. However, those willing to press ahead will reap the rewards of a more augmented approach, where personalised reporting means the most crucial information is always consumed in the most effortless manner.