Big data – big opportunity – massive challenge?

I was recently part of a panel discussing “The Data Centric Economy”. My fellow panellists are data experts, so my pre event concern was “what does a portfolio careerist and an accountant have to add to the debate?”

However, as it turned out, my stance (built on my experiences on boards of financial services, retail and AI based businesses) chimes perfectly with the data experts’ view… which was a relief!

Preparing for the discussion made me reflect on why we all think we have a massive data opportunity but really only see it capturing value in a disruptive context (think Uber, Facebook, Amazon).

Throughout my career, I took pride in thinking that my decisions were always based on fact and data. (In fact, I had a coach who had to work hard to help me to take a few more decisions based on gut instinct).

I have led large and complex transformation processes and delivered meaningful results. And I used data to inform my decisions along the way.

But what I have realised is that these data points were usually taken from a point in time, reflecting history and assuming that future conditions would replicate the past. An assumption that has been turned on its head in the last few years. The power of the consumer is driving individualisation, technology has changed many company processes and the financial markets are rewriting the definition of ‘normal’ behaviour.

It is little wonder, then, that many transformational changes were never really embedded.

For example, take a reorganisation. You use data (like spans of control, productivity, financial ABC models, etc) and strip a bunch of cost out of the organisation. Almost no sooner than you have congratulated yourself on the savings, you start to see the cost creep back into the company. And so, companies can swing from a centralised model to decentralised and back to centralised again. No wonder the employees think that management and the board are schizophrenic! I even know of some managers in large organisations who have thrived by “keeping their head below the parapet and waiting for the revolving door to come full circle!”

I am not sure that the blame for this can be placed solely at the door of the board and management. There is some responsibility that the shareholders need to shoulder. They often set a short-term agenda that forces actions that may not have the strategic longevity to add sustainable value.

So how do companies move away from the short term data application to something that unlocks this great value?

Several years ago, I came across the Ivey Business Framework, which helped my structured accountant brain wrap itself around the data conundrum:


Most companies operate happily in the Performance Management quadrant. The data is understandable, easily measurable and there are plenty of tools to help you consolidate it. But it is backward looking and assumes that the same set of circumstances will exist in the future.

Some companies (especially those with a strong brand or social media influence) are shifting up the data axis and into the Social Analyst quadrant. The data used by these companies mostly measures customer awareness, engagement and reach. The key questions are how to measure any return on social media (think Facebook campaigns) and how to monetise the data held by the company. For many boards, this is cutting edge in itself!

However, it is in moving into the Experimentation quadrants that you start to find the truly valuable data opportunities. It is here that you find new ways of thinking and really start to engage with a vast number of data points. These quadrants are the home of true AI and machine learning, amongst other emerging buzz words.

Operating in these right hand quadrants, it is imperative that you really understand your data: what it is and (more importantly) what it is not; and how data privacy laws might impact you. More importantly, you need to have the people who know what to do with this data and how to turn it into something other than a string of symbols and numbers.

In trying to implement these strategies, as part of my consultancy and angel investing, I have realised that understanding your data as both an asset and a limitation helps to define your quadrant and unlock the true potential.

I have also realised that you need to have a very clear business strategy where data is an enabler to deliver objectives.

But more than anything else, you need the right environment that allows the data magic to occur. Without it, you may never unlock that massive opportunity – but creating this type of workplace is not easy.

It starts with having the right CEO. They need to be humble enough and curious enough to consider new thinking (even more important when they are a founder who has already created  – in their view – the most perfect business!). They need to be truly welcoming of new and disruptive thinking – and tolerate even the most seemingly blasphemous thoughts. Those businesses that have successfully capitalised on their data have seen clear white space in the intersection of different data sources. It was a heady, often unexpected, moment of discovery for most.

The board need to be prepared to enter unchartered territory. And be comfortable grappling with thoughts and technology that they do not understand – often from people who are significantly less experienced and articulate (and younger!) than they are. The boards need to understand that the investment may take time to yield results and should be prepared for the journey to take a few interesting and unexpected twists and turns. Patience is a challenging game. But it can be rewarding.

Finally, but most importantly, you need to consider the people who have the skills to unlock the potential. Some companies have launched right into hiring an expensive data scientist and dropped them into the organisation “to make it happen”. (I have seen the same thing happen with digital directors). But one wo/man cannot work alone – especially when they have been set up as the ‘new rainmaker’.

Often it works best to take the data analysts that you currently have and give them the space to thrive and deliver some small wins that everyone can celebrate. Then build from there; hiring the data scientist only when the business sees the benefit of data analytics (aligning incentives to the commercial teams is a great way to achieve this).

The organisation must acknowledge that data analysts are happier behind a computer screen than being on stage; and they are usually not the people to lead or over communicate. They are generally reflective and need space to create insights. Most analysts value feedback massively… but are prone to over analysing it and reading all sorts of unintended messages from it. So be careful in your delivery!

So really unlocking the value in the data is no different to other big initiatives. It needs to be a strategic imperative, the teams need to be aligned to those objectives and the specialists need to be given the right environment to flourish. The challenge is for the management and board to step back and be brave enough to allow new ways of working, people with new types of skills and seemingly barmy ideas to have enough air to breathe and deliver.

Time, money, patience… bravery and curiousity… if it was easy, everyone would be successful at data monetarisation, right?

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