Contemporary businesses today are remarkably data rich as compared to their predecessors due to a plethora of factors. But having this data at the tip of your fingers alone does not ensure successful data-driven decision-making. How does a business ensure that it is able to utilise its data to its full potential? The answer of course consists of various parameters, but two important factors are: (i) the firm’s management must think data-analytically, and (ii) the management must create a culture conducive to data science and data scientists.Data Analytics’ point of view, ReduxCriterion (i) does not mean that the managers have to be data scientists. However, managers have to understand the fundamental principles well enough to be able to provide the appropriate resources to data science teams, have a clear vision of the implications of these projects and be willing to take calculated risks in investing in data and experimentation. Furthermore, unless the firm has on its management team a veteran, practical data scientist, often the management must steer the data science team carefully to make sure that the team stays focused on the objective toward an eventually practical, applicable and commercial business solution. This is as good as a shot in the dark if the managers don’t really understand the principles. Managers need to be able to ask probing questions of a data scientist, who often can get muddled up in technical jargon. We need to accept that each of us has their pros and cons, and as data science projects span so much of a business, a diverse team is essential. Just as we can’t expect a manager necessarily to have in-depth knowledge of data science, we can’t expect a data scientist necessarily to have deep expertise in business solutions. However, for a data science team to be effective, there needs to be a close collaboration between the two fronts with at least a basic understanding of each other’s respective fields. Just as it would be a Sisyphean task to manage a data science team where the team had no understanding of the fundamental concepts of business, it likewise is excruciatingly frustrating at best, and often a tremendous waste, for data scientists to labour under a management that does not understand basic principles of data science.For example, a management that does not have enough appreciation for predictive modeling, even though they see the potential benefits of it, does not invest enough in proper training data or evaluation procedures, thus leading to an unsatisfactory result and quite a huge loss for the company. Such a company may “succeed” in coming up with a model that is predictive enough to produce a viable product or service, but will be at a severe disadvantage to a competitor who invests in providing the necessary training data for their model well.The far reaching strategic implications of data science are backed by a solid grounding in the fundamentals. We know of no systematic scientific study, but broad experience has shown that as executives, managers, and investors increase their involvement in data science projects, they see more and more opportunities in turn. We see extreme cases in companies like Google and Amazon (there is a vast amount of data science going on in web search, as well as Amazon’s product recommendations and other offerings). Both of these companies eventually built by-products offering “big data” and data science related services to other firms. Amazon’s cloud storage and processing services are used by a majority of data science oriented startups. Google’s“Prediction API” is increasing in sophistication and utility (we don’t know how broadly used it is).Those are extreme cases, but the basic pattern is seen in almost every data-rich firm. Once the data science capability has been developed for one application, it just propagates itself for other applications. To quote Louis Pasteur, “Fortune favors the prepared mind.” Modern thinking on creativity focuses on the juxtaposition of a new way of thinking with a mind “saturated” with a particular problem.Working through case studies (either in theory or in practice) of data science applications helps prepare the mind to see opportunities and connections to new problems that could be dealt with in a better manner with the help of data science.For example, in the late 1980s and early 1990s, predictive modeling was applied to the problem of reducing the cost of repairing problems in the telephone network and to the design of speech recognition systems by one of the largest phone companies. With the increased understanding of the use of data science for helping to solve business problems, the firm subsequently applied similar ideas to decisions about how to allocate a massive capital investment to best improve its network, and how to reduce fraud in its flourishing wireless business. The progression continued. Data science projects for reducing fraud discovered that incorporating features based on social-network connections (via who-calls-whom data) into fraud prediction models improved the ability to discover fraud by a huge margin. In the early 2000s, telecommunications firms produced the first solutions using such social connections to improve marketing—and it showed results, showing huge performance boost over traditional targeted marketing based on socio-demographic, geographic, and prior purchase data. Such social features were added to models for churn prediction,in the field of telecommunications with equally beneficial results. The ideas made it to the online advertising industry, and using the data acquired on online social connections there was a subsequent flurry of development of online advertising (at Facebook and at other firms in the online advertising ecosystem).This progression was driven by managers and entrepreneurs with a sound knowledge of data science, as well as by experienced data scientists handling business problems, as they saw new opportunities for the advancement of data science in academic and business literature.
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