If not, then you should be. Data now plays a crucial role in business decision-making and it’s never been more important to consider how your company manages, analyses and uses it. The mere fact that there is data now available on pretty much every aspect of our lives is not enough.
Marketers need to consider the right way to approach data and how to create the right models that will produce good quality data to provide meaningful customer and business insight. The vast quantity of data that has exploded onto the scene has only increased the pressure on marketers to stay ahead of competitors and ensure they have a good grasp of its benefits.
The move towards a data-driven decision-making culture has been helped by innovative technologies producing enriched customer insights. Processes have evolved – from obtaining insights through customer surveys, to witnessing brand interaction in real-time and a greater depth of understanding about your customers. But there’s still more to be gained from tapping into the data, and not enough marketers are doing this in my opinion.
Why? Because they stop short in their processes and as a result, are unable to turn data into smart data. Only then can data truly add value to your brand decision-making and give you a competitive advantage – that all important edge.
But don’t just take my word for it. A McKinsey study which explored the attitudes of C-level executives found that investing in analytics was seen as the best way to help create value that leads to competitive advantage, scoring higher than the other two key trends in digital business: digital and social media and cloud computing and mobility.
Having the right management system in place is crucial. It requires a combination of technology, processes and people, something we at Mu Sigma like to call the ‘man-machine ecosystem’. To generate a successful analytics effort, you need a seamlessly integrated ecosystem that can scale and sustain the use of analytics. That means the following foundations must come together:
- Right eco-system comprising of people, processes, tools, and learning technology platforms
- The ability to create, translate and consume analytics
- Developing the art of problem solving and insight generation
Let’s take a slightly deeper look at these three ingredients.
For the right process to be in place, brands need to make sure they have structured frameworks in place that will help to generate insight, define problems and govern analytics. Also one needs to make sure that the right mix of questions are asked when working with analytics – which we refer to as DIPPTM analytics (Descriptive, Inquisitive, Predictive and Prescriptive). These four concepts need to be viewed in a holistic manner rather than sequentially, in order to give value insights. The four-stages describe the following:
- Descriptive: knowing what is happening in your business
- Inquisitive: why is it happening?
- Predictive: what is going to happen?
- Prescriptive: the so what and now what?
Asking the right questions will enable the creation, translation and consumption of analytics. This means that business problems will need to be articulated and translated into analytical problems in order to be effective. These problems in turn will need to be solved and translated back into business solutions. These solutions need to be communicated, socialized, implemented and consumed by the organization to realize the benefit from data-driven decisions.
The next element in the mix is the technology – the platforms and products that are needed to support this entire analytical value chain. Their contribution to the process involves creating a continuous and sustainable mechanism that allows for more innovation and greater efficiencies.
It requires an attitude which says ‘I want to learn about new opportunities’ rather than ‘I already know what they are’.
But of all the man-machine technology elements, what truly separates the wheat from the chaff, is the human resource element: forward-thinking businesses need to ensure they have the right people in place with the ability to think from first principle, who can think outside of the box, as well as spot trends and subtleties that a machine might not.
Central to having the right expertise in place is an interdisciplinary approach that not only combines the application of maths and technology to their analytic processes, as offered by data scientists, but also the additional layers of business context, design thinking and behavioural sciences, offered only by ‘decision scientists’.
It involves a cross-wiring between algorithmic processes and heuristics that incorporate both cerebral hemispheres – so logical and analytical left brain thinking as well as intuitive and thoughtful right brain thinking. They also need to be able to work closely with customers and stakeholders as part of this effort to institutionalise analytics not just in your marketing department but across the whole of your business.
Being a data-driven brand does not just involve the marketing team; the company as a whole needs to build a culture around data that involves creating a ‘lab like’ environment. It means testing and experimenting with it to always strive to find new ways to innovate and become more efficient across the whole organisation. It requires an attitude which says ‘I want to learn about new opportunities’ rather than ‘I already know what they are’. Only then can brands and their products or services lead the way ahead and ensure they always maintain their competitive edge.
There is little doubt that marketing professionals, like others, understand the value that data can bring to their brand and their ambitions. The information is there and can provide rich pickings for those who know how to use it. To do so you need the right processes, the right technology, and behind those you need the right people, decision scientists. In the words of the writer and philosopher Elbert Hubbard: “One machine can do the work of fifty ordinary men. No machine can do the work of one extraordinary man”.
