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With Manchester United becoming the first football club in the world to be worth over €3billion the amount of money pumped into professional football continues to boggle the mind. Transfer fees for Premier League players are nearing the £100million mark and the spending doesn’t look like it’s going to end anytime soon.
This spend trend is reflected in the analytics world. MarketResearch.com expects the global business intelligence and analytics software market to increase from $17.9B in 2014 to $26.78B in 2019 – a growth rate of nearly 10% annually! Whilst some sectors are reaching maturity in their analytics offering, sectors such as banking, asset management, insurance, retail, IT and telecoms are still finding their feet in a market where there are increasingly more options than specific solutions.
The increase in spend on analytics and widespread adoption of analytics strategies has also increased the demand in the Premier League footballer equivalent of the IT world – the data scientist. Data scientists are now commanding six-figure pay packets in Silicon Valley and any person putting those two words together on their CV can expect to be inundated with recruiters trying to flog you off to the highest bidder – “You put data science on your CV and you take a 20 percent pay rise pretty much immediately…”.
The trend is almost an admission from medium to large organisations – we have too much data and we don’t know what to do with it. Please help us, we’ll pay you anything…
And the result? Lots of people being paid lots of money who are still not getting the insight expected from the ever-increasing pile of data landing on their desk (which, incidentally, mirrors the Premier League’s transfer spending compared to actual success in European football…). This has led to an increasing dissatisfaction with the job market coupled with an increasing spend on staff that is not yet yielding the expected returns.
The lesson is pretty obvious – it’s not about who has the most expensive data scientist, it’s about who has the best data strategy.
In Gartner’s ‘BI Strategic Planning Assumptions’ paper for 2017, the final assumption is as follows:
What is that in English I hear you say? It means the number of business users performing data science activities will increase due to improvements in technology and improvements in data strategy. Gartner fluff it up by calling it a ‘citizen data scientist’, but in layman’s terms, a ‘citizen’ means you, me and everybody in IT that doesn’t have a PHD in computer science from Cambridge University.
By getting in early on a machine learning analytics tool, you can unlock data scientist-level insights without spending Paul Pogba Premier League prices.
By mastering innovative new tools, you can define a data strategy that isn’t dependent on finding a data scientist that will change your business forever and is instead based on the requirements and knowledge of your company’s ground troops – the business users.
As technology gets smarter and begins to incorporate natural language processing, artificial intelligence and prescriptive analytics, we can all start trusting what the computer says without having to spend considerable resources deciphering and dissecting data. Democratising access to data across the organisation is core to defining a progressive data strategy that incorporates all the needs of the business.
To keep with the football analogy – the best football clubs in the world invest in their youth teams and promote from within, as well as investing in players from abroad (FC Barcelona, for example). There is only so much talent in the world and money can’t buy it all! Do you need to spend a fortune on a striker when you could have the best player of all time in your youth team? All he needs is the right tools and a little bit of trust and who knows how good he could be…
Empower your staff with self-service technology and don’t hinge your entire company strategy on the speculation of an overpriced data scientist!