Have you heard the phrase “data is the new oil”? I’m sure you have. I do appreciate the analogy, but it doesn’t really cut to the crux of the matter. Perhaps Bill Schmarzo, CTO of Hitachi Vantara, said it better: data isn’t the new oil – it’s so much more important than that. It’s the new sun.
Data is an asset unlike any other. It never wears out, never depletes and can be used infinitely. Whereas most resources deteriorate the more they’re used, data increases in value over time. What makes precious metals so valuable? Their scarcity. What makes data valuable? Its sheer volume.
And that’s what many businesses find so challenging: there’s just too much data.
The data challenge
Businesses have a lot of data to play with. Given how valuable data is, you would assume business leaders are as happy as prospectors who’ve hit a bottomless goldmine. That’s not the case. In 2017, a report by Harvard Business Review said that less than half of an organisation’s structured data is actively used for making decisions – and a mere 1% of unstructured data is analysed or used at all. What’s worse is that there is a lot more unstructured data in the world – soon it’s expected that 93% of all data will be unstructured.
Around 80% of a data scientist’s job today is spent just preparing data – finding it, cleansing it and organising it. That leaves 20% of their time to perform analytics – the really important stuff.
Data governance is essential, but if a company is collecting data, ensuring it’s compliant with regulation and just using it for basic business monitoring – that’s not enough anymore. Here’s why.
Survival of the fittest
Since 2002, 52% of the names on the Fortune 500 list are no longer here. They’ve either gone bankrupt, been acquired or faded out of existence.
In 1960 the average lifespan of an S&P 500 company was around 60 years. In 2011, it was just 18 years. If we extrapolate from that trend then by 2027, 75% of S&P 500 companies that are around today will be gone.
Simply put, companies that aren’t transforming through technology are becoming dinosaurs. It’s Digital Darwinism.
Most businesses today should call themselves technology companies. The CEO of Domino’s Pizza, Patrick Doyle, understands this. He was quoted in Harvard Business Review as saying: “We are as much a tech company as we are a pizza company.” He realised that Domino’s doesn’t just make pizzas, they deliver pizzas – that puts them in the transportation industry. If you’re in the transportation industry, you have to be in the technology industry too.
So, Domino’s committed 400 of their 800 HQ staff to software and analytics, enabling them to completely overhaul their consumer experience. It was a shift in mindset that helped to breathe new life into the company.
Similarly, Airbnb doesn’t just offer accommodation and Uber doesn’t own cars – cars and hotels have always been around. These companies have survived while others have gone extinct because they optimised their business model through technology.
So the big question every organisation should be asking right now is: how effectively are we leveraging data to power our business model?
Drowning in data? Don’t just tread water
This is about digital transformation, not digitalisation – two terms often confused. Digitalisation is using new technologies to replace typically human-centric processes. And that’s the easy part. Digital transformation is the hard part because it means evolving an organisation’s current business model using the data insights gleaned from these new technologies.
That only starts when we put data to work, and when it’s monetised. And that isn’t just a job for a chief data officer. Believe it or not, digital transformation isn’t about reinventing the wheel, it starts with a deep understanding of an organisation’s current business model – so digital transformation can’t just sit with the IT department.
Now is the time for businesses to unleash their data science team, making their job easier so they can spend the majority of their time deriving value from data. How does that happen?
DataOps is a good place to start. A relatively new methodology, it should really be considered standard business practice. DataOps automates many of those time-consuming processes taking up a data scientist’s time, ensuring data is in the right place, at the right time and accessible to the right people. It’s a more agile approach to data science that enables better insights and better business decisions as a result.
The bottom line is: when it comes to data, businesses can’t afford to just tread water. To survive, and thrive, they must ride the wave of digital transformation by leveraging data to power their business model.