A systematic analysis of Talent Analytics position descriptions aimed at revealing the backgrounds, skills, competencies and responsibilities most central to the role and how some requisitions stand apart from the crowd.

1. 6 Talent Analytics Quarterly © 2018 Gartner, Inc. and/or its affiliates. All rights reserved. CLC180121
Making the Cut:
A Review of Open
Talent Analytics
Job Postings
D
emand is soaring for
talent analytics skills, as
evidenced by the number
of open analytics positions
within HR.
We analyzed 85 job postings in this field
from companies small and large1
and
learned that good talent comes from many
backgrounds — and organizations expect
that talent to handle a diverse set of tasks.
The sheer variety of backgrounds, tasks
and required skills may amaze you, as it did
us, but among the clutter we’ve extracted
insights in three areas to help you craft
your own analytics job requisitions.
1
Job postings that matched “talent analytics” or “people analytics” on Indeed.com on 25 March 2018. Posts were manually reviewed for inclusion in this analysis to ensure relevance. Representative
companies in the data set include AIG, Amazon.com, Capital One, Comcast, Google, McCormick, PepsiCo, Saleforce.com, Staples, The Home Depot, Uber, United Health Group, Vanguard, Wayfair, Yelp
and Zillow Group.
By Andrea Kropp
Insights From a
Data Scientist

2. Q2 2018 7
What educational and work experience do talent
analytics professionals have?
The roles we examined are typically midcareer roles requiring
at least three to eight years of relevant experience. Since the
entire field of talent analytics is barely as old, requiring this
amount of experience in the field would mean competing
for the small number of self-taught people who got in on
the ground floor and played a role in developing it. And
while organizations may want to fill a program management
position with one of these premium pioneers, those that
need rank-and-file analytics doers must think more flexibly
about other experiences relevant enough to serve as good
career precursors.
Most position descriptions we reviewed show an open-
mindedness toward other experiences (see Figure 1) but
failed to list specific positions that would make relevant
precursors. Adding these career precursors helps people
searching for those other positions by name discover your
position, immediately see themselves in the role and know
their applications are welcome (see Figure 2).
Source: CEB analysis.
Figure 2: Example of Listing Experiences
to Attract More Applicants
Original Text
Suggested Addition
Five or more years working
in an analytics, research or data
science environment; two or more
years leading a team of analytics
professionals; experience working
with progressive analytics
(e.g., R, Python, JMP) and data
visualization (e.g., Tableau)
tools to drive actionable
business insights
Data engineers, business
intelligence engineers, Python
developers and data scientists
working in R, Python or Julia in
marketing, sales, finance, supply
chain or IT are encouraged
to apply.
Figure 1: Terms Used in Required
Qualifications Section of Talent Analytics
Job Postings
Source: CEB analysis.
Educational qualifications are even more intriguing than
prior experience because they reveal hiring managers’
beliefs about the relative importance of formal training in

3. 8 Talent Analytics Quarterly © 2018 Gartner, Inc. and/or its affiliates. All rights reserved. CLC180121
Source: CEB analysis.
Table 1: Desired Educational Backgrounds Listed in Talent Analytics Job Postings
Educational
Background
Number of Times
Mentioned
Specific Degrees
Mentions
Business 56
Business, Economics, Finance, Management Science,
Finance, Accounting
Math 40 Mathematics, Applied Math, Statistics
I/O Psychology 39
Industrial-Organizational Psychology, Psychology, HR,
Organizational Behavior, Organizational Development,
Psychometrics
Computer Science 24
Computer Science, Data Science, Information Systems,
Information Technology
Other Hard Sciences 20
Engineering, Chemistry, Physics, Neuroscience,
Bioinformatics, Biostatistics
Other Social Sciences 13
Social Psychology, Social Science, Sociology,
Political Science
domain knowledge, computer science and general numerical
thinking for success in the role. Because degrees in data
science or business analytics are relatively new, very few
midcareer professionals carry such degrees. Organizations
must list degrees that applicants graduating from college
prior to 2014 are likely to hold.
Suitable degrees are typically presented as a list, which
can be amusing to read individually and can lead to some
interesting insights when viewed collectively (see Table 1).
One position we examined described the following desired
educational qualifications:
M.S./Ph.D. in a field where the research and quantitative
methods have direct applicability to people research in
organizations (e.g., quantitative psychology, economics,
political science, sociology, bioinformatics/biostatistics,
computational neuroscience, operations research,
statistics, data science, management science).
This description is actually quite astute. It’s one of the
few posts that covers all six categories we identified in
our systematic analysis: business, math, the quantitative
psychology field of industrial-organizational psychology
(I/O), computer science, hard sciences other than math and
When crafting your own position
descriptions, be aware of the
signal you send by leaving out
any of the six degree areas.
social sciences other than I/O psychology. But viewed out of
context, this description should evoke an incredulous look
from recruiters because of the diversity of fields as possible
educational backgrounds.
When crafting your own position descriptions, be aware
of the signal you send by leaving out any of the six degree
areas. By leaving out computer science and data science
you indicate the role doesn’t require building technically
integrated data solutions. By leaving out psychology or HR,
you signal the role isn’t expected to use domain knowledge
to form hypotheses or persuade business leaders.

4. Q2 2018 9
Source: CEB analysis.
Figure 3: Terms Used in
Responsibilities Section of Talent
Analytics Job Postings
What are talent analytics professionals'
responsibilities?
The responsibilities associated with talent analytics positions
span the full spectrum of the analytics value chain from
data acquisition and data wrangling to visualization and
oral presentation to stakeholders (see Figure 3). Too
often, the complete value chain is part of a single position
description. Organizations large enough to have a people
analytics team rather than a people analytics person should
look to specialize roles within the team. Finding people who
excel at the full pipeline is a rarity, and using them prevents
the organization from developing scalable processes
run by teams.
We also caution organizations against equating analytics
with reporting or technology. This confusion stems from the
newness of the people analytics and talent analytics fields.
The reporting layer and the technology layer are being built
in parallel to analytics capabilities, but these skill sets and
ambitions are typically not found in the same person.
In analytics job descriptions, the lists of duties and
responsibilities varies widely and often strays far into other
areas of expertise. See how these responsibilities fall into
three categories: core to analytics, adjacent to analytics and
outside of analytics.
Core Analytics Responsibilities
• Translate business questions/needs into appropriate
analytical problems, do high-quality analysis and turn
outputs into concrete, actionable business insights.
• Deliver workforce analytics that answer key strategic
questions as identified by business leadership.
• Conduct both qualitative and quantitative statistical
analyses (this includes, but is not limited to,
data screening and imputation techniques, chi-
square tests, t-tests, correlations, ANOVA, simple
and multiple regression, relative weight analysis,
logistic regression, discriminant analysis, MANOVA,
exploratory and confirmatory factor analysis, cluster
analysis, path analysis, psychometric assessment, test
development and validation).
• Storyline, create and deliver visually compelling
presentations used to educate, inform and influence.
• Think innovatively, and challenge the status quo.
Responsibilities Adjacent to Analytics
• Develop and implement dashboards and reporting
solutions.
• Quickly respond to various data requests
from SVPs and HR leaders.
• Train internal clients on self-service reporting tools
and how to interpret data.
• Lead design, development and delivery of leadership
training to guide the use of people information.
• Drive data quality and influence the development
of people processes and technology to allow for
effective data collection.
• Maintain data integrity, and observe data for
inconsistencies.
• Gather qualitative data through focus groups and
interviews.
• Assist in designing, building, distributing and
analyzing ad hoc surveys to meet business needs.
Probably Someone Else’s Jobs
• Create mobile apps for company executives.
• Build robust and scalable data integration
(ETL) pipelines.
• Manage internal reporting platforms including
troubleshooting, development and access security.
• Explore and recommend emerging technologies and
techniques to support and enhance BI landscape
components.
• Compose technical documents in support of the
development, implementation and maintenance
of BI systems.
• Develop and maintain appropriate benchmarks with
other organizations to include quarterly and annual
performance reporting.
• Evaluate effectiveness of all HR and recruiting
investments and programs.
Source: CEB analysis.
Note: Responsibilities taken verbatim from job descriptions.

5. 10 Talent Analytics Quarterly © 2018 Gartner, Inc. and/or its affiliates. All rights reserved. CLC180121
Figure 4: Verbs Used in Responsibilities
Section of Talent Analytics Job Positions
Source: CEB analysis.
Research
Communicate
Design
Drive
Manage
Analyze
Lead
Understand
Change
Develop
Identify
Provide
Direct
Help
Make
Present
Translate
0 6030
Compare this circumstance with that of the more mature
finance function. Accountants trained in the rules of finance
handle financial reporting, while forecasting and scenario
planning is the domain of financial planning and analysis.
Neither of these functions would be asked to recommend
which IT system should handle the company’s financial
transactions, nor would they be asked to develop a mobile
app to show financial trends.
Another way to assess duties and responsibilities is to focus
on the action verbs that appear most frequently in analytics
position descriptions (see Figure 4). Looking at descriptions
in this way directly answers the question, “What are people
in this role expected to do?” The top four words paint a
clear picture that the crux of the role is to “design research
studies, communicate the findings and thereby drive results.”
Number of Times Mentioned

6. Q2 2018 11
Source: CEB analysis.
Table 2: Mentions of Software, Methods
and Platforms in Talent Analytics Job Postings
What skills do talent analytics professionals need?
Hiring organizations list a large number of hard and soft skills
in the qualifications and duties sections of analytics position
descriptions. While the verbs in that list (e.g., communicate,
change, translate) largely reveal the soft skills, mentions of
specific software, methods and platforms within the text
reveal the hard skills required.
The word frequencies (see Table 2) illustrate the crossroads
at which the field currently finds itself. A simple interpretation
of this table offers a few insights:
• The large prevalence of Excel represents the historical
norm for data analysis and visualization.
• The BI platforms and analytics software as well as the
languages are ensnared in an all-out brawl pitting the
free, open-source route for analysis and visualization
that requires original programming skills (e.g., R,
Python, JavaScript/D3) against the proprietary route
for accomplishing the same via a graphical user
interface (e.g., Tableau, SAS, Alteryx).
• The organizations looking for Hadoop and other big
data skills are the ones that have collected so much
data on their workforce that traditional storage and
query tools are no longer an option. This segment is
small but growing.
• SQL is the new lowest common denominator to replace
Excel. Getting data into your local environment in order
to do analysis and modeling generally requires a basic
knowledge of SQL.
Programming Languages
SQL 40
R 35
Python 25
Javascript, Java, JS 7
Visual Basic 5
BI Platforms and Analytics Software
Tableau 37
SPSS 13
SAS 10
Alteryx 4
Visier 4
Cognos 3
Matlab 2
Microstrategy 2
Strata 2
Big Data Storage, Access and Manipulation
Hadoop 4
AWS (Amazon Web Services) 3
Impala 2
Spark 2
Hive 2
General Purpose
Excel 50
PowerPoint 22
Outlook 3
SharePoint 2
Visio 2
Enterprise HR Systems
Workday 21
Oracle 9
PeopleSoft 9
SAP 7
Taleo 2
The right mix of hard
skills depends on your
technology infrastructure
and existing investments.
Number of Times Mentioned

7. 12 Talent Analytics Quarterly © 2018 Gartner, Inc. and/or its affiliates. All rights reserved. CLC180121
In writing new requisitions, hiring managers and leaders
must ask themselves: (1) whether the goal of hard skills is
to hire for the present state or hire for the future state, and
(2) how much time a person in this role will have to learn
new languages, interfaces and platforms. The right mix of
hard skills depends on your technology infrastructure and
existing investments. Advanced Excel skills are no longer
enough, even if Excel-based tools and analytics are the only
things you currently have in place to support. Any person
you hire now, regardless how entry level, needs to have
some exposure to newer BI platforms or open-source
languages so they can help grow the talent analytics
function’s future capabilities.