The movie “Being There” may seem like an odd theme on an article concerning data science entrepreneurship. Yet, this movie highlights certain characteristics which are essential to success as an entrepreneur within all disciplines including data science. In the movie, Peter Sellers is the main character who is recognized as having an expertise which in his case is an obsession with watching TV. Though a series of circumstances and unusual situations, he becomes recognized by the both the business and political leaders as being a trusted confidante in both affairs of the state and business.
Although, I am not suggesting the comedy as inspiration for data science entrepreneurs, the rationale for leading off this article with such movie trivia is that Sellers or Chance as was his name in the movie had achieved recognition and more importantly the perception that he would be a key advisor to both business and government. Ultimately he was in high demand by both the business and government sectors. So how this apply to data science today.
In starting any business, including data science, demand for products and/or services is mission-critical for any kind of success. Within data science, two routes can be followed with the first being the development of specific products while the second is one of services that can be used to solve specific business problems which is really more of a consultative role. But again demand is the key to success assuming one has achieved the requisite level of expertise. So how do data scientists create demand?
In the first area of creating products, many data scientists, especially the younger and more technically-inclined, are more focused toward this area as the payoff in terms of selling their business to a potential investor can be significant. We have all seen the emergence of tech start-ups with specific products that have the potential to yield in some cases an almost obscene payout. The challenge with this model, although the payout can be significant, is the increasing noise and competition amongst all the new start-ups in this space. Many fail and only a minute few become the “lottery” winners in the innovation ecosphere which is being funded by both government and the private sector.
A quick review of the many innovation hubs that are emerging in many North American cities is a testament to this fact. My hometown of Toronto is certainly one central hub of “innovation” within North America. Yet, due to the potential payoff as well as the high degree of risk, many tech start-ups in the data science space rely solely on the technical skills of the creator who hopefully has some foresight of the demand for that product. In many cases, that tech creator will have a partner who is also very young and most likely a college mate but one who has focussed less of their studies on the tech but more within business and marketing.
The value of such a partnership is the recognition of demand or perhaps future demand for a product that has yet to be created. One obvious outcome of this new paradigm in the data science sphere has been the emergence of AI platforms and software as tools or enablers within the data science discipline. This is no surprise given the emergence of AI and its public recognition as having huge benefit within virtually all sectors of our society. But as I have discussed at many seminars and conferences in the past, these tools including AI, represent just one small facet of data science.
Looking at the entire discipline of data science beyond just tools, the need for practitioners who can apply their skills to a wide variety of business problems is not only great but continues to grow with increasing tools and technologies. Although one might think of data science as a specialized discipline, this could not be further from the truth. Success in today’s era for data scientists will be those individuals who adopt a more “generalist” approach when attempting to solve business problems. This demand for a more “generalist” approach in fact represents the second area of opportunity for data science entrepreneurs. Tools and technologies will attempt to automate as much as possible the so-called “generalist” skills. But these skills will be of the more technical nature that are just a portion of the data scientist’s skillset. But how does one become equipped to operate as a generalist but under the banner of entrepreneurship which is the area of entrepreneurship with the most growth potential.
The key ingredient in this second area is experience and more importantly experience with organizations that are leading-edge in their use of analytics and data science. My own personal experience revolved around such leading-edge organizations such as Reader’s Digest, American Express, and Air Miles or Loyalty One. In working with such organizations, identifying a key mentor and/or champion can really be helpful in ramping up your learning curve. One personal story is how my boss at American Express supported my efforts in advanced analytics despite the many I/T roadblocks which I encountered in many different projects.
At the same time, I was very fortunate to work with very bright product managers who at the same time were very respectful and enthusiastic. This allowed a much tighter collaboration in applying predictive models to their respective products. Besides the obvious overall business benefit, at a personal level I gained more domain knowledge on each of these specific products while the product managers acquired more knowledge concerning the value of data science to their specific product.
In a way, this environment of applying my skills towards a variety of very different products was really the genesis of my journey towards entrepreneurship. This sense of entrepreneurship was galvanized even further when I was transferred from the marketing area to the credit risk area. Besides having certain specialized skills, the range of areas in how I used these skills provided the necessary foundation in becoming an entrepreneur. One great book to read which reinforces this notion is: “Range” by David Epstein. In the book, he highlights many examples in all kinds of different areas of how people who may not be the best in a specialized area of discipline but are leaders in how the skills of a certain discipline can be best applied in completely different areas. The ability to think laterally as well as serially is and will continue to be a growing demand and expectation amongst data science practitioners. For many organizations, they will certainly develop their own internal teams in order to create their own data science competencies but they will look to external firms in order to provide that breadth or range of thinking.
As stated above, both product development and consulting services represent different areas of entrepreneurship. But many of these data science firms are now thinking about how to combine these areas together. This combination can be a very powerful offering in providing best of breed products but at the same time providing the more generic consulting data science services that can help to expand the product offering to a much broader audience.
Growth in data science continues unabated as it becomes a core discipline in virtually all organizations. As with other more established professions such as law and accounting, the integration of both external and internal data science capabilities will become more of a reality. The need to fill those external gaps will represent the areas of opportunity for data science entrepreneurs. For young data scientists just beginning their careers whose end objective is entrepreneurship, the goal is quite simple. Acquire experience within organizations whereby data science is a core discipline but which also foster development of their employees. It is this kind of corporate culture that will provide the necessary foundation and journey towards being an entrepreneur in data science.
About the Author
Richard Boire, B.Sc. (McGill), MBA (Concordia), is the founding partner at the Boire Filler Group, a nationally recognized expert in the database and data analytical industry and is among the top experts in this field in Canada, with unique expertise and background experience. Boire Filler Group was acquired by Environics Analytics where he served as senior vice-president. Richard recently launched Boire Analytics.
Mr. Boire’s mathematical and technical expertise is complimented by experience working at and with clients who work in the B2C and B2B environments. He previously worked at and with Clients such as: Reader’s Digest, American Express, Loyalty Group, and Petro-Canada among many to establish his top notch credentials.
After 12 years of progressive data mining and analytical experience, Mr. Boire established his own consulting company – Boire Direct Marketing in 1994. He writes numerous articles for industry publications, is a well-sought after speaker on data mining, and works closely with the Canadian Marketing Association on a number of areas including Education and the Database and Technology councils.