A few weeks ago, I had the opportunity to speak with Dr. Olly Downs who now serves as SVP Data Sciences for Globys, a customer experience firm. The call was a real education into the role of the Data Scientist and how large companies use Data Scientists in their business operations.
Some background on Dr. Downs, he specializes in applying abstract analytical ideas from the world of math, physics, and statistical science to real world business problems. He has 21 US patents and significant experience in productizing Big Data solutions. Downs holds PhD and MA degrees in Applied & Computational Mathematics from Princeton University, and BA, MA and MSci degrees in Experimental & Theoretical Physics from the University of Cambridge, UK.
It was a wide-ranging discussion on the role of the Data Scientist, and here are some things I learned:
It starts with the “question of the day”
One of the most interesting facets of the Data Scientist role that came out of my discussion with Dr. Downs was what he called “the question of the day”, it’s the business question management tasks Dr.Downs and his team to tackle through their Big Data analysis. Downs emphasized how human activity drives data and it is easy to lose sight of that if you don’t work with Big Data directly.
The analytics plan
While the work of Big Data Scientists starts from the business questions, the next step is the creation of the analytics plan which some organizations call a “data analysis plan.” When you consider that the Data Scientist ranks draw from diverse backgrounds such as the sciences, software engineering, marketing science, and MBA programs - practically any area with quantitative roots such a plan can bring the diverse skillsets and backgrounds together to focus on resolving a business problem the plan. The analytics plan lays out how the data scientist and their team assemble the data set and formulates a plan for how the data is to be used to answer the business questions.
The analytics plan can also be another tool for the Data Scientist to communicate and collaborate with the business side prior to commencing analytical work.
Once the analytics plan is approved and in place, Data Scientists begin their analytical work using a range of tools and methodologies some of which might be proprietary to their organization.
The Data Scientist even plays a role in Big Data Security by setting the precedent and policy for how data is used and manipulated in the business. Typically the Data Scientist and their team have unfettered access to new datasets that might be being brought into the business, and then help the business define how the data should be sanitized to retain analytics value while minimizing security risk and meeting necessary compliance rules.
Interaction with senior management
While the role of a Data Scientist might attract a certain type of personality, they don’t always get out of dealing with corporate management especially if Big Data is still new and high profile within an organization. According to Downs, “It depends on the focus and size of the enterprise. In some cases the business of the company and its product are built on data science, as is true at Globys, in which case Data Science has significant representation at the Executive Level.
He further adds, “In businesses where data science drives measurement and optimization of the business rather than being the business’ product, then Data Science plays as a direct report to the Executive Level of the function in which it most significantly impacts the business - for example at a Mobile Operator that might be in Brand and In-Base Marketing, while in Financial Services that might be through the Chief Risk Officer.”
Productization of Big Data
While the Big Data efforts of our readers run the gamut, Data Scientists in large-scale Big Data operations may play a part in the productization of their Big Data projects. This typically happens only when there is market value for the output from the Big Data efforts and the work can be resold to external customers either on a one-time basis or through subscription.
Intellectual property and Big Data
Data Scientists may have securing intellectual property (IP) such as patent work as part of their role. Securing IP can be part of an overall plan or ad hoc based on a given discovery. While Data Scientists working in mid-sized organization or for an outsourcer may not have to deal with IP issues, Data Scientists in larger companies need to pursue IP protections for their employer when their work imparts a competitive advantage over the work of their competitors. IP work is for customer facing software and services. Protecting IP isn’t important for internal Big Data efforts.
With the current arc of Big Data, I expect that IP protection is going to become an ever increasing element of the Data Scientist’s job as organizations work to stake their claims as Big Data innovators and thought leaders.
Big Data industry and education activities
Dr. Downs and his team are active in industry level Big Data activities. Considering the diverse backgrounds of Data Scientists - not to mention ongoing shortage of them - its nice to hear the importance that Downs puts on giving back to the Big Data field.
Downs also works as an advisor to a local college’s Big Data certificate program and his company funds a University of Washington fellowship in the field of Big Data. This full circle of a Big Data Scientist giving back to the education of future Data Scientists is especially key at the current state of Big Data.
The example that Downs sets speaks to a bright future of the burgeoning Big Data field. With industry and education activities an (un) official part of the Data Scientist’s role it opens many more opportunities for cross pollination of ideas and professional development in the nascent field of Big Data. The industry and education activities of today’s Data Scientists like Dr. Downs will certainly contribute to the next generations of Data Scientists by establishing a broader body of knowledge
Data Scientists in the industry
Albeit in short supply, Data Scientists are playing leadership roles in the nascent world of Big Data across multiple industries while having their role touch multiple parts of the business.