Regardless of the uncertain global economy, the amount of data being captured and stored shows no sign of abating. In fact, it has increased exponentially since the onset of the global financial crisis. Even as business growth slackened and budgets were cut, organisations continued to capture data.
So what have organisations done with their data? Some might have used it to cut costs or be more efficient – but what else can be done with it? There are now Big Data solutions available that offer faster processing speeds, more iterations of models and easy access to analyse the full arsenal of data.
These days, data is a significant asset that can be used for more opportunistic ventures. It can be used to prevent fraud, to get to know customers better, or as an asset to develop new lines of business.
The question now is – does the organisation have the talent or expertise to help translate between the analyst and the business? In recent times, it is this job description of a ‘data scientist’ that has aroused much interest.
When the term data scientist was first discussed 18 months ago, Frankenstein’s laboratory came to mind. As we continue to research the job descriptions of data scientists, we now realise it’s a legitimate role that is useful in a lot of organisations to help businesses get the most out of their data and to help bridge the gap between IT and business needs.
What does a data scientist actually look like? It is somebody who has a background in mathematics, statistics and computer science. Data scientists are not necessarily experts in any one of those fields but they can understand all three. They have to be very good at translating the business value of data to the business itself and helping analysts understand what they possess.
The communication piece is a missing link in a lot of organisations, and data scientists can really help take full advantage of data to overcome that challenge.
One obvious question that a lot of people ask is: Where do these data scientists live in the organisation? They’re not IT administrators. They’re not analysts. They’re not programmers. They’re the thing that brings it together and helps organisations communicate about the stories and the answers available in the data.
We’ve seen a lot of businesses find success with data scientists situated inside a Centre of Excellence (CoE). That is one viable possibility, and it offers other benefits to really streamline and unify efforts around analytics.
It is similar to creating a help desk to provide employees with support for questions about email, hardware and other technical problems. If data is really important and businesses want to increase the use of data to drive decisions why not have a help desk for what, after all, could be one of the most valuable corporate assets?
Of course, a true CoE is more than a help desk, and a data scientist is more than a call contact trouble shooter. You can’t just bring in the tools to solve your business problems and expect them to do all of the work. You need to have the right people in the right positions asking the right questions and teaching others how to use analytics to solve your biggest problems.
Ask yourself: How are you staffing your business analytics projects? Do you see a future for a data scientist role in your organisation? What benefits could a CoE provide for your analytics projects?
David Bowie is the Sydney-based Managing Director at SAS Australia and New Zealand. SAS is the leader in business analytics solutions and services, and the largest independent vendor in the business intelligence market.
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