Data Science, IoT Automation, and the Autonomous (Deskless) Worker

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By Ryan Martin | 3Q 2016 | IN-4218

IoT analytics revenue is on track to exceed $30 billion in the next five years, yet fewer than one-third of U.S. News & World Report’s Top 100 Global Universities offer degrees in data science, and only six of the 29 universities that offer data science programs make them available to undergraduate students (versus graduate-only). While companies like Accenture, AT&T, Cisco, and GE started to adapt their respective professional development programs to address talent gaps in-house, advances in self-service analytics, machine learning, and artificial intelligence (AI) beg the question: which jobs will or won’t be displaced by IoT automation?

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Addressing Data Science Skills Gaps: Higher Ed vs. the Private Sector Pursuit

NEWS


IoT analytics revenue is on track to exceed $30 billion in the next five years, yet fewer than one-third of U.S. News & World Report’s Top 100 Global Universities offer degrees in data science, and only six of the 29 universities that offer data science programs make them available to undergraduate students (versus graduate-only). While companies like Accenture, AT&T, Cisco, and GE started to adapt their respective professional development programs to address talent gaps in-house, advances in self-service analytics, machine learning, and artificial intelligence (AI) beg the question: which jobs will or won’t be displaced by IoT automation?

Data Science is Diverse

IMPACT


Data science is an interdisciplinary field that employs a combination of statistics, software engineering, and computer programming to gain insight from structured and unstructured data. It is also the backdrop for the primary ecosystem components that comprise the IoT analytics value chain: data integration, data storage, core analytics, data presentation, and professional services.

Although the specific function or role assigned to an individual data scientist can vary depending on the industry, application, or use case, it’s generally accepted that most of these fall under one of two broad groups:

 

  • Analysis: the identification, management, and extraction of information to support evidence-based decision making (e.g., Analysts)
  • Building: the design, development, and creation of software used to model and analyze information (e.g., Software Engineers)

Beyond the raw skills required to be a data scientist is the common need to cultivate domain-level expertise, as well as the operational resources to upgrade and maintain modern IT/OT (operational technology) infrastructure.

The introduction of new tools designed to help integrate, monitor, and manage IoT data exhaust adds another dimension to this dynamic and is underscored bythe push to bring a similar suite of productivity applications to the blue collar worker as those that have traditionally been found at the desk. Longer term, the availability of such tools, coupled with Millennials’ (born 1980-1995) workforce migration, will become part and parcel of best practices surrounding employee utilization, OPEX cost reduction, and the democratization of decision-making authority across a more informed and evenly-distributed set of stakeholders.

Tackling the IoT Talent Gap

COMMENTARY


While it’s natural for labor markets to fluctuate with economic, political, and cultural constraints, neither purchasing power, partisanship, nor the level of attrition brought on by an aging population can truly account for the way that technology and friction-free access to information impact business. The rise of self-service analytics, machine learning, and, in turn, IoT automation is a prime example. But facing the inevitability of a talent overhaul doesn’t mean drinking from the firehose; there are a number of ways to address skills gaps.

The challenge for a company like AT&T is an order of magnitude that could entail the re-appropriation of performance metrics for more than 280,000 employees and a subsequent need to re-educate a population whose average tenure at the company is 12 years (22 years not counting people in call centers). The other option would be to procure talent on a wholesale basis (new hires). AT&T, citing a recent Deloitte survey that found 39% of large-company executives are either “barely able” or “unable” to find the talent their firms required, chose the former of these approaches—a program it refers to as Workforce 2020.

Although about 140,000 AT&T employees are currently engaged in acquiring skills for newly created roles as part of the program, there isn’t a one-size-fits-all blueprint for tackling talent gaps. Companies like HP, FedEx, Pepsi, and Walgreens, for example, each employ roughly the same number of people as AT&T yet likely face a different set of issues (higher turnover among them). The introduction and wider availability of self-service analytics, machine learning, and AI simply helps level the playing field by focusing the conversation on how to supplement—rather than supplant—today’s employees, putting the lens on the job itself rather than the organization or industry in which it resides.

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