Big data and analytics are transforming the way we work. Many HR managers are even shortening the review and revision process on job responsibilities and the task lists on position descriptions to reflect the active role that analytics plays.
Those already in the workforce should take steps to improve their skills in analytics, as more employees will be expected to work in this area. For individuals seeking their first jobs, taking an analytics course or two are strong entries to add to your resume.
The big data and analytics boom is also leading to the creation of new jobs. Here’s a list of new analytics roles that some employees are filling.
Citizen Data Scientist
Data scientists are hard to find; in addition, many companies can’t afford to hire one. The result is the birth of the “citizen” data scientist—an analytically talented individual from the organization who does not have a formal degree in data science or engineering but who takes on the mantle of developing complex algorithms and queries of data that can yield breakthrough information for the company.
In April 2015, Gartner research analyst Alexander Linden described citizen data scientists as “people on the business side that may have some data skills, possibly from a math or even social science degree—and putting them to work exploring and analyzing data.” Shawn Rogers, Chief Research Officer at Dell Statistica, added, “I think that 2016 could be the year of the citizen data scientist because users throughout the business want a more democratized approach to big data and analytics. Not every company can afford a data scientist, which is a big reason why citizen data scientists will become a big part of the data ecosystem as it evolves.”
Manufacturing engineers and other machine operators and technicians at the “edges” of enterprises where machines are being used may soon take on roles that involve harvesting information from sensors in these machines and then moving the sensor-based input into software and systems that are running machines, coordinating machine-based operations and handoffs, and checking on machine health.
Other IoT roles will include engaging and programming advanced robotics and even stationary field-based sensors that report on environmentals at remote locations. IoT will transform field-based technician and engineering jobs beyond the current boundaries of machine maintenance and operation and into data gathering and software-based analytics that feed into onsite and remote, centralized systems.
These “by hand” employees who refine and clean up data come from administrative and clerical functions and will increasingly assume the role of internal data hygienists. Along the way, they will pick up new skills in data preparation and classification.
Most organizations have data architects who come from DBA ranks and create an overall architecture of the data throughout the enterprise. What’s needed now is someone who can orchestrate the movements of this data. How much data from the edges of the enterprise will need to be locally stored there, and how much will need to be instantaneously transferred to different points throughout the enterprise?
For data that requires real-time or near-real-time velocity, the questions that need to be answered are: How much data is there, and are the enterprise’s data pipelines and systems adequate to transport and store it? There is no formal job title for this function, but increasingly, application developers and systems analysts will assume key roles in determining the different speeds and resting places of data throughout the enterprise.
Analytics machines like IBM Watson are already assisting with medical diagnoses and legal research. In law, medicine, and other fields, these analytics are generating new forms of research work that paraprofessionals (e.g., physician assistants, paralegals, etc.) in organizations will most likely perform.
As analytics machines like IBM Watson play greater roles in organizations, they will have to be continuously “taught” so they can do their work better. In many cases, machine learning can be done by the machines, but in some cases, this learning will have be guided by a new generation of “teachers” culled from the ranks of business analysts and subject matter experts within the enterprise.