From Ironman to customer journeys, we’ve all got a big data problem

By Stephen O'Reilly, SAS Ireland

Like many others in Ireland, I’ve been consumed by the get fit/keep fit craze. Many a weekend you’ll find me tackling half marathons, triathlons or adventure races up and down the country. But my competitive nature is leaving me with a problem: a big data problem.

For example, I’m training for an Ironman at present. It’s a daunting challenge that requires a huge amount of planning and training if I’m to perform as well as I’d like, and not break myself in the meantime.

So I collect information about a number of Key Performance Indicators (KPIs). My swim watch tells me information such as the number of lengths, strokes per length, average speed, stroke efficiency, etc. My bike computer tells me similar kinds of stats, plus cadence (revolutions per minute), heart rate measurements, temperature, elevation and more. If I’ve got any energy left at all, I’ll go for a run and measure all the KPIs then too. Add to that my resting heart rate, BMI and weight…the variety of data is endless.

And there lies my big data problem. It’s not the volume that’s the issue; we’re only talking bytes here. It’s how I combine and process such a variety of data and turn them into meaningful information about the effectiveness of my training. My solution is to use a sports performance app, Garmin Connect, to integrate and then analyse all the different sources of information – and it often throws up some unexpected insights.

For example, let’s say I underperform on the bike. My gut feeling is to look at factors such as cadence, power output and speed. But that doesn’t give me the whole picture – the underlying causes. Yet my app’s analysis can quickly tell me that the key difference between a good performance and a bad one was actually the weather or wind speed, or my resting heart rate that morning. These insights are crucial if I’m going to devise a winning training strategy, adapt and improve, and compete at my best.

I may be just one crazy man tearing across the countryside, but my big data problem is no different to that of any commercial organisation. Companies collect a huge variety of data: from diverse customer bases to wide portfolios of products, channels and customer touch points.

Turning that data into meaningful information to drive strategy can be the difference between success and failure here too. Customer retention strategies are a great example. Rather than relying on gut feeling, wouldn’t it be better to analyse the huge variety of customer data available to truly understand customers, their experience and the factors that influence their choices? Particularly as the answers are not always in the obvious places.

Using data analytics, companies can quickly gain insight into which customers are the most profitable, which are likely to churn and why, and how to improve their experience to retain them. By using all available data in this way, companies are not only able to design better customer retention strategies in the short term, but also measure their effectiveness and adapt for long-term success.

Find out more about SAS Customer Intelligence or follow me @Cluain7 to see how I overcome my big data problem for Ironman success!