Johannesburg, 08 Mar 2022
We live in a time where we have access to so many different types of data, and multiple models for us to get data from customers in ways that don't make them feel as if their privacy is being violated. This data allows businesses and governments to make data-driven decisions, through actionable intelligence.
So said Keith Carter, associate professor, School of Computing, National University of Singapore, who was presenting a keynote address on data-driven decision making, at the ITWeb Business Intelligence Summit, being held in Sandton and online this week.
Actionable intelligence (AI) essentially means having the right information in the right person's hands, at the right time, in order to improve outcomes. However, while 74% of firms say they want to be data driven, only 29% say they have done this successfully - and it's going to get harder still.
Redefining AI a little, Carter said what humans have always wanted is to know what's going to happen next, or if something is happening, wanting to know about it right away. “Or can you delight me because you know what my needs are, and perhaps provide a product or service at the time that I needed it.”
South Africa has a huge opportunity here for AI, to reach the point where businesses are aware of what their customers want. “The first three words in this journey are ‘know, decide, act’,” said Carter.
Bringing data to life
Delving more into what data-driven decision making is, and how to make it come to life, Carter said there has been a shift in the business environment. Back in 2001, the market was led by big box companies such as retailers and banks, which were paperwork intensive, as online banking was in its infancy. But then, just 15 years later, the top five most valuable companies in the world were all data companies, and just five years later, these companies have grown in value three fold.
This is because data is being used to anticipate customer's needs. “Hyper personalisation is being enabled by knowing the customer's entire journey, and these companies converted from big box into data-driven companies because of timely, relevant offers.”
He says actionable intelligence has to be fast. "It's not about analytics, which is the study of math. It’s about knowing what to do next, and how a situation should be handled.”
Value realisation
According to Carter, the most important questions for senior leaders is, how do we win in this business? What do we need to do? My customer said we fail at this, why? “Whatever the strategic business question may be, it should come from the top and the answer should provide direction on how to deliver results.”
He said one thing that is often missed is the business value of the answer. “This is also critical, and any analytics project should have this in its project plan – value realisation. How much money was it worth for the answer that we provided, and then the action that was taken.”
The business will get more on board when people start to realise that their analytics project isn't just analytics, it actually delivered sales and other activities to the business, be it better customer service, or something else.
To build a data-driven business you have to have a data lead with some business background, and a business lead with some technical background. The goal of the team must be very clear, too, Carter said.
Three key questions
And because there’s a huge gap between data science speak and the business speak, Carter said we need dashboards that answer three key questions. “Where are we today? Are we winning? And how do we win tomorrow?”
He said any dashboard needs to answer these three questions to be successful. “If there's no text on the dashboard and it's just numbers and charts, I call that the dashboard of a lazy analyst, who didn't go and try to find out what was really going on.
Also, they need to be brave enough to ask, ""so how do we win tomorrow?”
Analysts need to write that down, commit to it, own it, so that later on, when the data proves that that was the right direction, they will know how much they earned as a result, and how much improvement was made too, Carter concluded.
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