The big data train is leaving the station and everyone is scrambling to get on board. We’re told big data will shape the future of business and as a result, companies large and small, across a range of industries are talking data, desperate for any competitive advantage. And if we’re being honest, we’re OK with that because we love data. But we have a dirty little secret to share. Big data is irrelevant
…but don’t tell anyone…
The truth is that it’s not the data that is of core value to a business; it’s what it can help you do that makes it valuable. No senior executives truly care about big data. They care about the results it can deliver.
CEO’s are right in thinking big data is irrelevant
Having data does not guarantee positive business outcomes. You can have all the relevant data in the world, but if you don’t have a clear purpose for the data analysis that aligns to your company strategy, then your data analytics program runs the risk of providing zero (and ultimately negative) value to your business.
After all, data analysis is only a means to an end.
If we set aside the politics of energy production for a moment, do energy companies really care about coal mining? Is digging rocks out of the ground truly important to them? Of course it isn’t. The value of coal is its potential for energy generation – providing you have the equipment to turn coal into energy.
So, whether you’re mining coal or mining data – it’s not what you dig up that matters, but what you do with it in order to realise its full value. It’s about turning rocks into energy and information into knowledge.
So, how do we turn rocks into energy?
In a recent blog I described five key steps in establishing an effective data analytics platform:
The data analytics component gets a lot of the glory – and understandably so – but without the context established up front, you’re sailing into the open oceans without your map and compass.
Without answering the “what, why, when, where and how” up front and being crystal clear on your purpose, data analytics endeavours become exploratory exercises but will rarely deliver any explanatory insights.
And without an organised group of engaged people with the appropriate level of sponsorship from a senior leader within the business, you’ll struggle to sustain momentum.
It’s not how big it is. It’s what you do with it.
Too many big data projects fail because of one simple problem: a disconnect between the analysts and the business.
Without the right context established up front, or if you just lose sight of the context, data projects and data analytics may be approached as an IT problem, rather than a business problem.
This isolates the team digging for the data insights from those that will ultimately make game changing decisions based on them.
There’s the classic example of how automated algorithmic pricing employed by two competing Amazon sellers led to a situation where a text book on flies (yep, the ones that swarm your snags in summer) ended up being priced at a shade under $24 million, when the market price was around $100.
Clearly the sellers did not intend this situation when they setup their pricing algorithms. Data without sufficient context drove this outcome, creating a bad situation for the sellers and the marketplace – notwithstanding the excellent story that came from it.
While data analysts can and do find insights, the value and relevance of these insights is the crux of the problem; what appears valuable to a team of data scientists may not be of any tangible value to the executives. If the insights you’re getting from your data are unable to support strategic decision making for your business, then they’re useless.
So, while we love data, we have to agree with the execs – big data is irrelevant. What matters is what we can learn from it. That’s the challenge that gets us out of bed every morning.