WHAT’S IN A NAME?Workforce analytics is the discovery, interpretation,

and communication of meaningful patterns in workforce-related data

to inform decision making and improve performance.

WHY?Workforce analytics allows organizations to gain insight about people,

offering competitive advantage to improve business performance

WORKFORCE ANALYTICSLEARN HOW SUCCESSFUL ORGANIZATIONS USE WORKFORCE ANALYTICS TO IMPROVE BUSINESS PERFORMANCE

Part I – Understanding the Fundamentals

The need for HR to

contribute to business value1. The changing

nature of HR2.As organizations seek to improve performance, theonus is on HR to build value. The best way to dothis is through an analytical approach.

Workers, managers, and executives are demandingmore from their HR function, that is:• The need for more information to ‘run the

business.’ The required response to this is thedemocratization of HR. HR is being asked formore information, better insights, and moreprecise recommendations to help executives andmanagers run their business.

• The desire for personalized services. Therequired response to this is theconsumerization of HR. Many workers wouldappreciate recommendations to improve theirworking experience.

The future of

work3.This is an important time for the HR profession toadapt and create momentum in the field of workforceanalytics to capitalize on the changes shaping thefuture world of work like the rise of robotization.

Focus of the

function1.Activities of

the function2.The functional descriptor ‘Workforce’ isrecommended because this relates to the entiregroup of workers for an organization. The termalso allows for the future inclusion of machinesthat will replace current jobs performed byhumans.

‘Analytics’ is the most accurate word to describethe work of the function.

A name fit for the

future3.The name ‘Workforce Analytics' is future-proof. Three key changes will impact workforce analytics in thefuture:• artificial intelligence, robotics, and other technologies will transform work that humans currently

perform;• the gig economy, an environment in which temporary positions are common and organizations contract with

independent workers for short-term engagements, will expand;• the amount of workforce-related data will exponentially increase with the ‘Internet of Things’.

THE WORKFORCE ANALYTICS LEADERInternal reporting structure, key job responsibilities, the importance of business acumen and influencing,

and core leadership attributes

Internal reporting

structure1.Key job

responsibilities2.The leader needs strong human resources (HR)connections, as well as ready access to otherparts of the organization. Reporting to the chiefhuman resource officer (CHRO) addresses theserequirements and also sends another message: TheCHRO is putting analytical decision making at theheart of the HR function.

The leader’s primary role is to deliveranalytically driven recommendations that, whenimplemented, improve business performance. Someimportant associated responsibilities alsoinclude:• manage - all of the stakeholders must be

managed appropriately so that projects getdelivered;

• challenge - be able to challenge the thinkingof the specialists, to ensure that their workis accurate and complete as well as be able tochallenge the thinking among businessexecutives and other leaders to ensure thatprojects deliver new insights to improvebusiness outcomes;

• integrate - galvanizing a diverse team around acommon vision and mission;

• represent - be able to represent the positionof technical specialist in the team tostakeholders and at the same time, the leaderneeds to talk eloquently about the details ofanalytics projects to business leaders.

Business

acumen3.The one skill a leader must have and shouldcontinuously improve is business acumen. Spendtime with other leaders, read voraciously about anorganization, understand the marketplace, beintimately aware of the metrics and keyperformance indicators of an organization, ensurethat you are financially literate, and networkextensively with leaders.

Core leadership

attributes4.Four key leadership attributes are considered most important for the workforce analytics leader to bringpeople together as an integrated and cohesive workforce analytics team:• capacity to think - be able to think carefully about prioritizing actions as well as be able to think

about very complex projects;• willingness to develop others - integrate the work of people from different backgrounds and disciplines

into a cohesive team, nurture and grow talent, take a developmental outlook toward employees, and show apassion and drive for collective action and teamwork;

• ability to inspire - instill a belief in the team to succeed, build confidence in their team members’ability to succeed and inspire them to work as a coherent team around a common vision and mission;

• drive to achieve – have tenacity and resilience.

PURPOSEFUL ANALYTICSA model for purposeful analytics

Frame business

questionsStep 1

Build

hypothesesStep 2

Gather

dataStep 3

Conduct

analysesStep 4

Reveal

insightsStep 5

Determine

recommendationsStep 6

Get your

point acrossStep 7

Implement and

evaluateStep 8

Framing the business question isanother way of clarifying the businessproblem. This step must come first toavoid undertaking the wrong analysisand also to give the project the bestchance of success. A clearly framed andwell-defined business question ensuresthat the project or analytics work isactually necessary. Without suchclarity, the project is unlikely togain investment and sponsorship.

Level of sponsor involvement: High

Building and clarifying a hypothesis isimportant for ‘testing’ beliefs aboutthe causes of business issues. Stronghypotheses should guide the datagathering and analysis phases in a waythat links to business questions.Appropriate hypotheses also make iteasier to select the most appropriateanalysis for the project in question.

Level of sponsor involvement: High

The data gathering step requiresidentifying the most relevant data fortesting the hypotheses and determiningwhether data quality is sufficient toproceed. Decisions need to be madeabout whether to gather existing data,collect new data, or do both.

Level of sponsor involvement: Medium This is where the methodology andstatistics are applied to data to testthe hypotheses and provide the basisfor insights. At this juncture,choosing the right method for analysisis critical because choosing the right– or wrong – method will determine thevalidity of the results.

Level of sponsor involvement: Low

For two main reasons analysts mustuncover insights. First, analystscannot assume that project sponsors andstakeholders are able to derive themost pertinent insights themselves.Second, if analysts present only dataand analysis without insights,executives and project sponsors mightdraw their own conclusion to best fittheir preconceptions.

Level of sponsor involvement: Medium

Although insights are interesting, onlyrecommendations will help improve thebusiness. Recommendations are whatbusiness leaders and, in this case,project sponsors need. A well-articulated recommendation makes agreat impetus for change.

Level of sponsor involvement: MediumAll analytics projects have a moment oftruth. This often happens as youcommunicate the outcomes of the projectto the sponsors or other stakeholders,to get your point across. This is themoment when you are able to informtheir decision making.

Level of sponsor involvement: High

This implementation and evaluation stephas three discrete aims. First, itensures that decisions are made as aresult of your project. Second, itformulates action for implementationbased on those decisions. Finally, itfacilitates evaluating the projectagainst whether it returned value tothe organization.

Level of sponsor involvement: High

Analytical

pitfallsReasons an individual analytics project might not succeed:• step 1 and 2 – a project with no clear purpose or lack of a sponsor is in high danger of failure;• step 3 to 6 – even the most expertly defined business problem and well-crafted hypotheses will not yield

benefit if the project is poorly executed, statistical technique or method is poorly chosen and aninadequate investment is made;

• step 7 and 8 – projects can fail when results are poorly communicated, no action results, or actionoccurs but with no evaluation to determine the business return.

Analytical

steps1.

2.

BASICS OF DATA ANALYSISResearch design, quantitative- and qualitative analysis,

machine learning, and bias and fairness in analysis

Research

design1.Quantitative

analysis2.Before quantitative or qualitative analysis canoccur, decisions must be made regarding what datawill be collected, when it will be collected, howit will be collected, and from whom it will becollected. These questions fall under the topic ofresearch design. The research design you applydetermines how rigorously you can reachconclusions about causes and effects followingyour analysis. The strongest research designs arerandomized experiments, followed by quasi-experimental designs, observational designs, and,finally, qualitative designs.

Most quantitative analysis (that is,statistical analysis of numerical data) aimsto do one of the following: explore,associate, predict, classify, reduce, orsegment information about employees andorganizations.

Qualitative

analysis3.The goal of qualitative research are tounderstand organizational phenomena withoutusing quantitative data. Objectives ofqualitative research include hypothesisformulation, interpretation, andcontextualization.

Unrestructured

data4.Today, unstructured data is oftenquantitatively analyzed by converting datathat were captured as language or images intoa numerical representation.

Machine

learning5.Machine learning is a field of computerscience that gives computers the ability tolearn without being explicitly programmed.Machine learning techniques tend to performbetter when building models to predictoutcomes from many variables and whenexamining complex relationships.

Social consequences

of algorithms6.Scrutinize your analyses for their potentialto produce adverse impact, bias, orunfairness, and take corrective action asneeded.

REFERENCES:

Guenole, N., Ferrar, J. & Feinzig, S. (2017). The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance. Pearson FT Press, New York.

FOR MORE DETAILS GO TO:www.thepowerofpeople.org

Content adapted from The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance. Pearson FT Press

For more details go to:www.thepowerofpeople.org