Data is the latest fashion – but what’s your personal style?
It seems that everyone is talking about data - books, blogs and articles on the topic are everywhere. Data has a strong attraction with its suggestion of objective information to drive clear direction – ‘the new gold’ a common epithet. There is no doubt that developing data management capabilities should go to the top of the priority list.
One temptation is to focus on the need for data strategies and analytical frameworks and tools.
Clearly these are needed because, for all the talk about it, in and of itself data has no value. It’s only when approached strategically i.e. with conscious approaches for collection, integration, analysis, visualisation, sharing and application, that marketers will be able to use data to drive value for businesses and the customers they serve.
However, we can’t forget the human factor in all this. It’s true that there are (increasing numbers of) situations where software can do the heavy lifting e.g. the clever algorithms that drive Google’s brilliant search capability, or Netflix’s (automated) ability to create personalised recommendations. However, for most situations (for the time being at least!), human analysis and judgment are essential elements.
And here’s the irony – at the same time that the variety, volume and velocity of data is exploding, we’ve also learned that human decision making – so essential in making sense of it all - is more often than not flawed and prone to bias. (I'm sure you've read Thinking, Fast and Slow by Daniel Kahneman).
Understanding your personal style
So, in addition to focussing on strategies and processes, we also need to think about our own role – our own ‘personal style’ if you like.
There are a couple of dimensions to consider. The first is connected to how conscious we are of the ‘fast’/intuitive vs. ‘slow’/analytical decision making processes, and how we use these when thinking about data. The other is how much natural affinity we have with our current customers: do we have close personal subjective understanding or do we have a more distant ‘objective’ perspective?
These different dimensions will play out in different ways:
If we tend to rely on a fast, intuitive decision making style, then we will avoid the obvious potential pitfall of ‘analysis – paralysis’ as data amounts increase. We may not necessarily work hard to harness new data sources, but we’ll use what’s readily available and easy to understand to guide decisions, and keep moving at brisk pace. But will these be the best possible decisions for the medium to longer term? If we are a user of our own category, and therefore have a natural affinity with at least some of our customers, or spend time connecting with them, we can use personal insightfulness to guide the way. But to offset this, too much subjectivity may well mean having a blind spot to actual and/or emerging behaviours that only large sets of objective data reveal. So, as marketers, while we always want to be dynamic, and feel connected with our customers, we should recognise that ‘fast’ does not always mean ‘good’, and that our own personal experiences cannot tell us the whole story about what’s happening in our categories.
If we are more analytical in nature, we may relish the increase in data and have a far more systematic approach to its collection and analysis, and feel that a thorough approach is essential for decision making. The outcome will be slower decision making. This is not necessarily bad if the decisions are sound but, as the amount and variety of data increases, the danger is that customers will have moved on, and generated a whole stack of fresh data, before anything is done. The positive aspect to an analytical approach is that, when allied with a close affinity with customers, we can use data to confirm subjective insights, and develop and test hypothesis for more effective action. If we don’t have that closeness to customers, we could completely misinterpret data. One of the biases in thinking that can occur is to see patterns where none actually exist – if we are not grounded in customer reality we may well start putting different data sets together, and start to draw the wrong conclusions.
None of us are always one thing or another – we will be intuitive at time, analytical at others, and work with differing levels of connection with customers on different occasions. The ideal of course in approaching the challenges of faster, bigger and more varied data, is to keep a balance between intuitive and analytical styles, and stay close to customers but not become overly subjective.
The trick with dealing with data, as it is with any fashion that looks set to run and run, is to work out what our ‘default’ personal style is and recognise where we need to work a bit harder to stay current.