Data relevance: how to turn ‘straw into gold’

Data relevance: how to turn ‘straw into gold’

Data relevance: how to turn ‘straw into gold’

In the business of extracting value from data, parallels can be drawn to the book/movie entitled Moneyball.
Admittedly, because it’s such a great story, Moneyball is in danger of being over used as a case study for how to use data to create competitive edge.
The movie tells the story of how the Oakland A’s manager, Billy Beane, employs sophisticated analytical models of player stats to drive his player recruitment strategy.

In turn the A’s are then able to outperform their peer group and, in fact, be competitive with teams spending much more money on players. The received wisdom from this story? That clever data analysis, like Rumpelstiltskin turning straw into gold, can transform an organisation’s competitive abilities.

Well, yes and no. Because what people often forget is that automated data analysis alone didn’t turn the As into a success.
Instead, it took a human, in the form of Assistant GM, Peter Brand, to translate the data output into something meaningful that then allowed Billy Beane to identify and hire the right talent.

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In short, Brand made the data relevant to the use case that Beane put in front of him – help find players who will win, but at a fraction of the budget it normally takes to be successful.

We know today that we have no shortage of data. Quite the opposite in fact: the frequent complaint heard from people is that they are drowning in data.
But what people are actually struggling with is the question of data relevance. All too often, business decision makers have data pushed at them from their internal teams without context or a real understanding that data.



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