Gartner states that companies need to invest in training and certifications for their employees to help them achieve their analytic capability goals. Of course, this is being stated from a place of business profitability – but from the employee side, it makes sense from both a place of personal profitability (data-centric roles offer some of the highest salaries in tech), but also a place of personal pleasure.
This April, the FT stated that degree courses in data science specialisations are booming, whereas in the US, more traditional MBAs have been declining for several years. Yet Masters degrees in data analytics are a real growth market: 74% of the big data courses in the US reportedly experienced an increase, versus 32% of the two-year full-time MBAs, according to the Graduate Management Admission Council.
It’s not such a surprise, but it is still a whole new world to many in business. The term data scientist was only coined in 2008 by the leads of data and analytics at LinkedIn and Facebook. The massive growth in these skilled statistical graduates is testament to one of the most obvious business revolutions of the digital age - the data flood that digitalisation launched.
Back in 2012, the Harvard Business Review published the bold claim: “Data scientist - The sexiest job of the 21st Century”. Since then, as the FT’s statistics indicate, the claim is coming true!
But it’s not only because of the money. Though, of course, that’s a key part of the current rise and rise of the data scientist and of general analytical skills among those who just want profitably to get involved with their data without a formal analytics qualification. It’s also a thrilling prospect for a workforce, for whom much of the day is humdrum, actually to discover real answers and make change at a rapid pace.
Yet all people are different and there’s an estimated addressable analytics market of around 350 million business users (you might term them everyday analysts with no formal analytics training, but who use data to do their jobs), around 50 million of what are being called citizen data scientists (inspired to use data for their own projects), and around two million certified data scientists.
Formal qualifications are important only to a relative fraction of practitioners.
Many types of analytics-engager are coming through from the world of business in all industries. Formal qualifications are important only to a relative fraction of them. What unites them is a shared data cultural mind-set.
According to the magic quadrant for business intelligence and analytics platforms, the number of citizen data scientists will grow five-times faster than the number of data scientists. Citizen data scientists (a technical term that could be described in more friendly terms as an everyday or everyman analyst) are the data analysts of tomorrow.
Citizen data scientist are defined as a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics. In plainer terms, it’s people who use data properly to do their jobs better.
But for those business leaders impatient for this to breakthrough to create a real data literacy in their business, then a culture has to be shaped and encouraged.
Those who want to get involved, but don’t know how should be encouraged whole heartedly.
A common language and culture should be built around corporate data. This will need a careful consideration of the way the enthusiasms of those who are already doing the job are channelled to best effect. Those who want to get involved, but don’t know how should be encouraged whole heartedly - but it’s best to learn from those who already apply best practices or to start from a clean base.
In an annual survey last year, CarringtonCrisp, an education consultancy, discovered that 10% of female and 15% of male prospective business school students said big data and analytics courses were their preferred MA choice.
High performers might come from MBAs or Masters in data analytics courses or be self-taught, or certified on a particular method or software platform. As long as they combine some quantitative skills, such as analysis and the concepts of databases, plus an understanding of business issues, they’ll have a formidable set of skills to deploy to take on business challenges.
It’s not surprising. Analytical work is more rewarding. Problem-solving is how humans feel accomplishment. Overcoming business challenges should be enjoyable and when line of business employees use data everyday they immediately become line of business analysts. Solving problems is what humans do and where they can’t, there’s no thrill. Analytics gives employees and the business the ability to have an “aha!” moment every day.
The hardest part of the analytics process is figuring out the right questions to ask.
It’s clear that there’s a grassroots data culture percolating through the world of business, but directing it to the best ends should be a major part of the business strategy. One method to best direct the insatiably curious and keen new data analysts in a business is to develop internal certifications that essentially provide a “driver’s license” to be a data analytics professional.
People asking clever questions become cleverer. The hardest part of the analytics process is often figuring out the right questions to ask. Deep, functional knowledge is required to get at really meaningful questions. Promoting data literacy skills should quickly inculcate greater levels of engagement, enjoyment and productivity.
Gartner’s recent survey of more than 3,000 CIOs ranked analytics and BI as the top differentiating technology for their organisations. It attracts the most new investment and is also considered the most strategic technology area by top-performing CIOs.