Way back at the turn of the decade, data science looked like a career path that was only on the up. HBR had named it the sexiest job of the century and organizations were hiring as fast as they could. But are these halcyon days over? In a previous article, we asked whether data science could be automated. Perhaps a more clear and present danger, however, is self-service analytics tools and the rise of the citizen data scientist.
Gartner predicted that by this year, the number of citizen data scientists will have grown five times faster than their highly trained counterparts, while global analytics and advisory firm Quantzig listed self-service data analytics software as its number one trend in data analytics for 2017. The cost benefits of using self-service big data discovery platforms that require no coding knowledge to use are potentially massive. According to Glassdoor.com, the average annual salary of a data scientist is $119,000. A trained employee could leverage the data in the same way, but is unlikely to command the kind of salary someone with coding expertise does. Having someone with actual industry and business experience analyze the data may actually yield greater insights. IBM prefers to teach people with professional tennis experience how to analyze the data at Wimbledon rather than teach data scientists about tennis because it’s easier to do it that way round, and this logic applies across many industries.
What does this mean for data scientists? At first glance, it may look like it is a bad thing. However, Bob Laurent, VP of product marketing for self-service analytics platform provider Alteryx, argues that rather than push data scientists out, it will simply push them up, and give them more interesting work to. He notes that ‘IT has become more of a facilitator. If they're able to give people access to data with the proper guardrails, then they're out of the business of having to do mundane reports week in and week out.’ There are also advantages to be had from giving so many people the greater appreciation of data that can only be gained when they use it for themselves, particularly when it comes to getting senior buy-in for data projects. We spoke to five leading industry figures about how they felt self-service analytics would impact business intelligence and the role of the data scientist.
Dr. Robyn Rap, Business Intelligence Analyst at Indeed.com
Self-service tools are just that - tools. Using them effectively depends greatly on the person who's using it, their judgment, and their intuition to dig into the data and its quality. Tools are going to come and go, and you can teach people how to use them. It's much harder to teach someone how to approach data appropriately, with curiosity and a healthy amount of skepticism. That's why on Indeed's BI team we ask ourselves: Does this person like to learn new things? Do they demonstrate curiosity about data, and question its accuracy or reliability? Do they come up with hypotheses for why the data looks the way it does? At the end of the day, if you can answer 'yes' to those questions, it shouldn't matter what tools you're currently using.
Saurabh Bhatnagar, Senior Data Scientist at Rent The Runway
Tools are helping us dissect larger data sets with speed, but ultimately it is up to the Data Analyst to understand the business problem and find the insight.
It is the responsibility of the Data Scientist to package that insight into a data product.
There are efforts like automated statistician but they are not up to the mark yet. For the foreseeable future, humans using these descriptive and predictive tools will the norm.
Matt Kautz, VP of Business Intelligence at Machinima
The ubiquity of self-service tools frees BI professionals from the rote, pedantic numbers delivery aspect of the job, and puts a lot more value on BI teams that can offer more intuitive and proactive analysis. It requires analysts who thrive on digging deeper to determine why the numbers went up, rather than simply delivering reports showing that they did.
Kevin Harrison, Assistant CDO of the State of Illinois
I believe the future is in combining the data, providing governance, and working with/maturing the business users to best utilize these tools. The self-service tools combined with business users becoming more savvy helps put the value of Business Intelligence into the businesses hands directly, which shows the value of BI directly. In short, BI work has changed from a reports/metrics ‘doer’ role, into an enabler role for the business and can make BI more of a partner and not just a function.
Mario Trescone, Senior Director of Business Intelligence and Data Analytics at YMCA of the USA
Self-service analytics tools leave the future of Business Intelligence one without boundaries. Organizations big or small, for-profit or not-for-profit, now more than ever have the ability to gather, organize, and distill information to produce actionable insights quicker, thereby enhancing their decision making process. These tools ultimately provide them the ability to react more quickly to market shifts and give them a glimpse as to what tomorrow may bring. These tools will also create opportunities for those with backgrounds in statistics, predictive analytics and data science on both the development side as well as the end-user side.