Tony Mooney, Managing Director of Decisionbox
For me, the only purpose of collecting any data is to generate better insight. And the only reason to generate better insight is to make better decisions.
What many people tend to forget is that it’s really all about the decisions – the end result. I’m not an advocate of the ‘collect everything, stick it somewhere and throw some clever science at it’ approach. For me, it’s important to really think about the decision you want to make from the beginning, asking yourself how material that decision is and how quickly you want to make it.
The data challenge is mostly about meaning and velocity
I often get asked about the shifting data landscape. There are certainly a lot more data sources around now but just how much of it is useful is a moot point. There is always the lure of more and more data but if all that does just makes the haystack bigger, then it is not helping find the needle. I’m not entirely convinced we all make best use the data in front of us, let alone gather even more.
The main difference that I have seen over the past two decades is velocity. There’s a greater need for more information much more quickly. That puts huge pressure on data capability; to really sort the wheat from the chaff quickly in order to make better decisions. Most businesses move at such a fast pace that velocity is king when it comes to data needs.
The main thing we have to remind everyone of is that somewhere in the middle of all this are human beings. Real people that make decisions to buy, or not to buy. Data is merely the means of understanding individual behaviours. Of course, it’s getting more complex as we live in a cross-platform, cross-media world. As consumers, we have more choice than ever and as businesses, we have more challenges than ever in influencing these choices
People get dangerously over-excited by Big Data and Data Science
I think Big Data approaches and technologies offer some really powerful capabilities. The first question that I tend to ask, however, is: am I making the best use of my small data? In my experience most organisations really don’t derive enough value from the data they already have.
There is also real danger of drowning in data. It’s important to ask, how much data can we actually handle? We’re not at a ‘the computer can do it all’ stage yet, so the decision-making process still a considerable human element to it. And, as human beings, we have a limited ability in terms of processing all that information.
Hence, the journey might start at Big Data but you often need to work your way back to something real people can get their heads around. The problem is that reducing granularity to something very high level to try and make it more manageable will often strip out the value of the insight
The interpretation and effective communication of insight is crucial
I believe the bigger problem in organisations is how data is interpreted, communicated and used. We often have a rather human inclination to change facts to confirm what we thought anyway. I see that constantly. It doesn’t matter if your data sources are research, surveys, big data, small data or any data, there is always a natural temptation to fit data to produce the answer we want or expect. I call it ‘decision-based fact-making’
It’s natural cognitive bias and we all suffer from it: whether its consumers not doing what they say they do or organisations post-rationalising. It’s just something to be aware of and to carefully manage. You can spend a lot of time and money collecting data and producing insight that makes absolutely no difference at all to a decision.
When you understand it is all about decisions, you also realise that providing actionable insight in a timely manner is the key to increased effectiveness in decision-making
The process of commissioning, and consuming insight must be made as simple and accessible as possible. My philosophy with insight development is to create it once and sell it many times. Once we have learnt something valuable, we should have that information stored and regularly refreshed so it’s always immediately available. It is highly wasteful and inefficient to have to generate insight from scratch every time a question is raised. That’s why I advocate the creation of Knowledge Management capabilities, including the significant process and cultural change that powers this. By this, I mean instilling a powerful ‘build it once’ focus in data and analytical teams, to drive the use of analysis and analytics towards the creation of insight products and artefacts like self-serve interactive reports, dashboards and data cubes.
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