5 more ways leaders and analysts can think differently to be data-led

This post continues a short series on how to think differently to be more data-led.

Guest blogger Harry Powell returns to add to his original list of 5 ways to think differently. Regular readers will know that Harry is Director of Data and Analytics at Jaguar Land Rover. He has shared with us before on the how to measure the ROI from your analytics team & avoid stakeholder disappointment.

As before, this post is a slightly different style to our normal guest blog posts. Below I have collected together what were previously 5 separate micro-posts from Harry. To help you achieve this aspect of data literacy, I think it’s helpful to see a wider diversity of different thinking styles.

As you read through them, I challenge you to consider which thinking style you could practice. Bring to mind a current problem you are working on. Recall how you are approaching thinking through that problem. What have you tried already? Now imagine, for each of the thinking styles that Harry outlines, approaching it that way. Could it work? What fresh insight might it offer?

Over to Harry now, to challenge us with 5 more ways to think differently. Helpfully for each he shares a real life case study of how his team applied that thinking style (in green text). Over to Harry…

Thinking Style 6: Rearrange the equation

Often business analytics problems are framed as “manage x in order to increase y”. It’s very easy to get fixated on one particular formulation, and to stick with it even when the world around you has changed. Doing so could leave you trying to solve yesterday’s problem. Then no matter how clever your approach you’ll get the wrong answer. You’ll need to find a different way to get the result you want.

We spent a lot of time and effort building an amazing hierarchical dynamic Bayesian forecasting engine. It worked really well, was widely accepted and was integrated into the BAU process. When Covid hit, everyone wanted us to adapt it to help us forecast the way out of the global crisis. And we tried, but it didn’t work because the world had become inherently unforecastable.

The answer was not to refine and improve what we already had, but to rearrange the equation to discover a better lever. We realised that instead of trying to improve our ability to forecast the future, we would be better to improve our ability to adapt to the present. So we built models to detect short-term demand signals and to respond to supply shocks by improving order intake and supply chain decision making tools.

Ask yourself: Does what worked yesterday still hold? Is there a better way to achieve the same result?

Thinking Style 7: Knowing when to stop

It’s not unusual for thinking patterns to get fixated on doing things rather than not doing things. But often the quickest and easiest way to make an impact is just to stop.

If a product makes a marginal loss, you might be better just to stop selling it than to try to make it profitable, especially if getting unit profit to breakeven is going to take time.

Don’t assume that a problem needs to be solved. It could be quicker and easier simply to eliminate it.

Thinking Style 8: Asking the experts

Obviously experience counts for a lot when dealing with daily issues. But expertise can get in the way when you are dealing with unusual events, or if you want to find a new answer to an old problem.

Some problems have existed unsolved for so long that everyone assumes that they aren’t a problem anymore. And sometimes questions remain unanswered for so long that people forget they ever wanted to ask them in the first place. But the most pervasive is the Expert that Knows the Answer to a problem (but somehow the problem still exists).

When I joined JLR, the team had been working on a big project for a while. The KPI they were optimising was Stock Turn, and the whole business believed that this was a proxy for Demand. No one questioned it. It was Gospel. The experts would have no other measure. But of course Stock Turn is a function of both sales rates and the level of stock. So if you have different levels of inventory in different retailers, you have different stock turn. It was all rubbish, but the experts never questioned their own certainty, and no one in my team dared question them, because they weren’t Experts.

One of the most powerful questions you can ask (after listening politely to what the Experts have to say) is some version of “what if the truth is completely opposite to what you currently think?”.

Thinking Style 9: Time is money

Everyone in business knows that time is money, in theory, but it is amazing how often people ignore it when solving pressing problems.

Whether you are trying to stem losses, or take advantage of opportunities, every day that you spend working on the problem is a day of lost profit. So there is an important balance to be struck between improving the outcome and implementation.

If you are building a product which is expected to make, say £52 million per annum, then if you spend an extra week improving it you should ask yourself “will this improvement make me £1m?”. Equally if you are negotiating to buy a tool that will make you the same £52 million, you need to shave £1million off the contract for every week of contract negotiation. And that might be hard if the cost of the tool is, say £5 million. Can you really save 20% every 7 days?

A good idea is to estimate the value that could possibly be added by delay (eg, we might be able to negotiate 10% off the cost of a tool) and then work out how many days of value creation this represents. In one recent case this came out as 2 hours of time: If we spent more than 2 hours negotiating, we would be wasting money.

Thinking Styles 10: Jumping across domains

Often there are structural similarities between rather different data, and seeing this can really help find a better solution. I am not talking about what the data represents, but the way that it is collected, stored or structured. One example is the similarity between sequences of events (for example customer interactions) and sequences of words (otherwise known a natural language).

We used this “grammar of interactions” to inform when mortgage borrowers were likely to repay, based on the channel, content and the order of their communications with their bank. We used NLP techniques to incorporate this event data into our model and significantly increased performance as a result.

You can do something similar in reverse. When presented with a list of banking transactions (kind of abstract), we realised that this represented real behaviour, a real person walking down the high street, going into shops and buying stuff. Any set of transactions bounded by a short time period could be thought of as a shopping trip. This allowed us to infer all sorts of interesting things about the location of transactions, and therefore the locations of shops.

Whenever you look at abstract data, always think about the form of the data as well as the content. You’d be surprised by the value of what can be inferred.

How could you think differently using one of those styles?

Thanks to Harry for again sharing his thinking & personal experience. I hope that helped you reflect on situations where you could think differently, try a different thinking style. Which one of the above strikes you as the most relevant for your current challenges? Why not try using that perspective to rethink your approach this week?

As I interview more leaders for the Customer Insight Leader podcast, I am struck by the diversity of their personalities and perspectives. So, I’d love to also hear what you think about data-led thinking. Are there other ways you have found of thinking differently about business challenges? Please share what has worked for you.