Database Reference
In-Depth Information
The McKinsey study forecasts that by the year 2018, the United States will have a
talent gap of 140,000-190,000 people with deep analytical talent. This does not
represent the number of people needed with deep analytical talent; rather, this
range represents the difference between what will be available in the workforce
compared with what will be needed. In addition, these estimates only reflect
forecasted talent shortages in the United States; the number would be much larger
on a global basis.
The second group—Data Savvy Professionals—has less technical depth but has a
basic knowledge of statistics or machine learning and can define key questions
that can be answered using advanced analytics. These people tend to have a base
knowledge of working with data, or an appreciation for some of the work being
performed by data scientists and others with deep analytical talent. Examples of
data savvy professionals include financial analysts, market research analysts, life
scientists, operations managers, and business and functional managers.
The McKinsey study forecasts the projected U.S. talent gap for this group to be 1.5
million people by the year 2018. At a high level, this means for every Data Scientist
profile needed, the gap will be ten times as large for Data Savvy Professionals.
Moving toward becoming a data savvy professional is a critical step in broadening
the perspective of managers, directors, and leaders, as this provides an idea of the
kinds of questions that can be solved with data.
The third category of people mentioned in the study is Technology and Data
Enablers. This group represents people providing technical expertise to support
analytical projects, such as provisioning and administrating analytical sandboxes,
and managing large-scale data architectures that enable widespread analytics
within companies and other organizations. This role requires skills related to
computer engineering, programming, and database administration.
These three groups must work together closely to solve complex Big Data
challenges. Most organizations are familiar with people in the latter two groups
mentioned, but the first group, Deep Analytical Talent, tends to be the newest role
for most and the least understood. For simplicity, this discussion focuses on the
emerging role of the Data Scientist. It describes the kinds of activities that role
performs and provides a more detailed view of the skills needed to fulfill that role.
There are three recurring sets of activities that data scientists perform:
•
Reframe business challenges as analytics challenges.
Specifically,
this is a skill to diagnose business problems, consider the core of a given
problem, and determine which kinds of candidate analytical methods can
