Along the “Silicon Highway” that follows the 101 freeway from Burbank and Glendale through Woodland Hills and past Camarillo, entertainment, health care and biotech firms have attracted and developed a new kind of professional: data scientists. And soon, I predict, many other kinds of Valley-area companies will hire data scientists too. While data and information (meaningful data) have always been important to the life of businesses, especially in supporting quality decision-making, the salience and centrality of the role of data in the firm in the past decade has been unprecedented. This growth is likely to continue unabated given the growing use of sensors, data input devices of all kinds, and new “big data” uses combining various databases from various sources. These specialties may have been once the province of “research-based” and “market-based” analytics firms, but the ubiquity of data and technology has pushed rich analytics into all mid-sized firms and even many small firms. A data scientist is, for now, somewhat of an amalgam. This individual is comfortable with mathematics, statistics and computer science, and has a strong knowledge of the revenue- and cost-drivers of a firm. A data scientist moves comfortably among these areas better than a traditional specialist. A data scientist knows not just about data analysis, but also about data preparation, data modeling, data management and data curation (archive). Contemporary data scientists can use modern visualization tools and techniques to ethically communicate assumptions, results, interpretations and limitations. Business analysts, business intelligence specialists and information systems staff have, of course, traditionally done much of this work too but there are differences. A data scientist can apply the mathematical knowledge to build models, including models that may not have been done before, with sufficient rigor to explain past phenomena and predict future outcomes. A data scientist can discern when exploratory analysis of observational data isn’t enough and confirmatory analysis of experimental data is needed. A data scientist complements her or his use of “rectangular” (spreadsheet) data with data in hierarchical, network, streaming and database formats. The data scientist is a proactive opportunist accessing data from the World Wide Web and other sources as needed. The data scientist can augment data analytic (“statistical computing”) technologies that can “fit” on a desktop or laptop (“small data”) with data analytic technologies that are better suited to execute on a cloud-based system (“big data”). To be sure, the idea of scale, including speed, size and cost, in all aspects of mathematical, statistical and computational thinking, is central to the contribution that a data scientist makes to the benefit of the firm. In Silicon Beach, chiefly on the west side of Los Angeles, an entire sub-economy of hundreds of firms – from big names you know to little names you don’t – are capitalized enough to hire data scientists at all levels of employment. And they can’t get enough of them. A data scientist is perhaps one of the best learners in the organization. The data scientist takes advantage of the regional “meetups” such as the Data Science group that convenes monthly in Glendale or Westlake Village. This individual leverages open source software at the visualization, database and programming language levels to design and develop new approaches to business data analytic opportunities and challenges. For some organizations, open source solutions may actually be the preferred solution. As part of service to the wider community, your data scientist can volunteer for Code for America or HackForLA, developing data-driven apps for numerous government and non-profit organizations. With respect to your data scientist, there is work to do at all levels in an organization. Managers of all kinds need to understand the questions data can help answer and just as important, the questions they can’t (or can’t yet). Human resources professionals will need to be aggressive in recruiting and retaining in the small talent pool of prospective data scientists in the short run; in fact, HR professionals may need new or expanded processes (especially in evaluating experience-based competencies in addition to education-based degrees) in talent sourcing and outreach to capture the best data scientists in the region. Executives will need to craft an enterprise-wide culture of “learning from data” and set an example for the entire organization. Your next hire should be a data scientist. In our region, this is true for both existing manufacturing firms and the many firms in critical service-sector industries. Wayne Smith is a lecturer in the Management Department at the David Nazarian College of Business and Economics at California State University – Northridge.
