Data: Are we speaking the same language?
04 October 2016
Britain and America, the saying goes, are two countries divided by a common language. There have been numerous occasions during my working life in data analytics when a similar thought has crossed my mind. We have extraordinary power to collect, collate and analyse data and yet we often fail to ask ourselves the most basic of questions: Does this word mean the same thing to everyone?
Take, for example, data aggregation. At PwC we’ve developed the Intelligent Data Framework – an approach to integrating data from multiple sources. This isn’t, of course, a new idea – many organisations are juggling huge amounts of data from many sources and it makes sense that if you can bring it all together, your decisions will be based on richer evidence.
But where our approach is different is in putting data at the centre of the Framework, rather than letting technology lead. It’s often assumed that the best data analytics come out of the best (meaning the most expensive) systems. But that’s not the case at all. Good analytics begins and ends with good data; if you get the data right, you can capitalise on it allowing you to focus your technology needs. The best and most expensive technology in the world is worth nothing if the data it uses is inconsistent or of poor quality. Good data, on the other hand, will make your existing systems all the more valuable.
That means that data is well-governed and most importantly, standardised across the organisation. And that means everyone must speak the same language – in data terms, a common taxonomy.
In the accounting world, the profession has been collaborating for years to produce a standardised taxonomy under which data can be managed, shared and compared – the chart of accounts. In the HR world, though, there’s no equivalent initiative and, so far, no sign of one appearing. So people are making the language up for themselves.
And that can be a real problem within organisations, particularly in siloed companies. It’s not unusual to find two people within the same company using a different definition of ‘head count’, for example. If the definitions aren’t consistent, data analysis will make absolutely no sense.
So under the Intelligent Data Framework we begin with the taxonomy. There’s no short cut – everyone has to sit down together and create a definition of terms that works for their organisation. It’s an iterative, collaborative process but one that’s often forgotten or avoided altogether because it just seems like a drag. When combined with visualisation tools that are now available to play back outputs almost real-time, this gets everyone bought-in on the issues and wanting to fix them.
This is a new way of aggregating data, putting the business and data at the heart. And there are quick gains to be won if you get it right. The result is more consistent data and the confidence that everyone is on the same page, providing the platform for bolder and more insightful decision-making. But most importantly, it makes everyone think far more carefully about data and what it means; the organisation becomes more data conscious. Life’s a lot simpler when we all understand each other.