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Ac#onable  BI  Analy#cs  for  Managing  the  Global  Workforce

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As a result of technology innovation, the business value of data analytics and business intelligence is beginning to have a t a n g i b l e i m p a c t o n t o t a l t a l e n t management. The realized business value is only as good as how well an organization understands how to use data analytics—both tactically and strategically—for sourcing and managing talent. Those that do so put themselves in a position to win the war for talent.

Table of Contents

Sabermetrics …………………………………. 3 - 4

Talent Data Analytics from the Top ........ 5

"Data, Data Everywhere …” …………………6

Figuring Out Where to Start ………………..7

Challenges of Talent Management ………8

Decision-Making and BI Outcomes ….….9

BI Maturity Model ……………………….…….10

Actionable BI Frameworks ..………. 11 - 12

Requirements for Actionable BI….13 - 14

Five Key Takeaways.…………………………..15

Endnotes ………………………………………….. 16

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Sabermetrics

The potential impact of using data analytics to make

intelligent business decisions was brought to life in

Michael Lewis’ book Moneyball: The Art of Winning an Unfair Game that was turned into a film called Moneyball

in 2011 starring Brad Pitt and Jonah Hill. The central

premise of the book and movie is that data analytics on

player   performance can predict how well they will

perform in given circumstances. Through rigorous

analysis, Oakland A’s General Manager Billy Beane and

his staff concluded that on-­‐base   percentage and

slugging   percentage are be=er   indicators of offensive

success than other outputs such as batting average, runs

batted in, and stolen bases.

A small-market Major League Baseball (MLB) team with

a payroll dramatically less than big-market teams such as

the New York Yankees, the Oakland A’s had to find other

ways to compete beyond identifying and keeping “best”

players, at least based on the standards typically used to

evaluate performance. Beane and his staff found the

answer in what is known as Sabermetrics, which enabled

them to rebuild their team with cheaper players based

on predictable  outcomes using data analytics. To do so,

he hired a team of data  scien#sts and business  analysts

to help develop the algorithms needed to predict future

performance.

The premise of the book Moneyball: The Art of Winning an Unfair Game and the movie

Moneyball is that data analytics on player performance can predict how well they will

perform in given circumstances. This turns into strategic-prescriptive analytical model that

teams use to plan their rosters, draft prospects, and make trading decisions.

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With the success of the Oakland A’s in 2002 and 2003,

other MLB teams began employing Sabermetrics to

evaluate talent. Suddenly, every team hired data

scientists and business analysts to help them develop

the   right   algorithms and siA   through  mounds  of   data

on thousands of players in the major and minor leagues

to intelligently predict what their future performance.

The same tools that are being applied in MLB to win  the  war   for   talent are also being leveraged in the private

sector. Human resources (HR) and procurement leaders

employ ac#onable   business   intelligence   to help their

organizations to recruit and source better candidates

(both full time and contingent), pay at competitive

market rates, gain strategic market intelligence, and

m a ke b u s i n e s s p r e d i c t i o n s r e g a r d i n g t a l e n t

requirements.

When   Sabermetrics   are   applied   to  con#ngent   talent   management  organiza#ons  can…

• Recruit  and  source  be=er  candidates  • Pay  compe##ve  market  rates  • Gain  strategic  market  intelligence  • Make  data-­‐driven  business  predic#ons  

regarding  talent  requirements

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Talent Data Analytics from the Top

75% of business executives report talent data analytics are a

critical issue for their organizations.

However, only 8%   believe their

organizations are doing a good job.

Data analytics can play an important operational and

strategic role if used correctly. Seventy-­‐five  percent  of

business executives report that talent data analytics are

a critical issue for their organizations, enabling them to

achieve   be=er   results and gain   a   compe##ve  advantage.

Yet only   eight   percent believe their organizations are

strong in this area.1 The outtake is that much work

remains to be done.

Executives are not wrong in their assessment.

Data-driven analytics such as talent data analytics

have substantial potential to impact bottom-line

results. For example, a study in Harvard Business Review from a few years ago found that

organizations using data-driven analytics see a five  to   six   percent   improvement   in   profitability.2 Of

course, to achieve these types of results, and

organization must possess a high degree of

readiness; saying  versus   doing is quite apropos in

this case.

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“Data, Data Everywhere …”

One of the most famous poems written in modern

western civilization is “The   Rhime   of   the   Ancient  Mariner” by Samuel Taylor Coleridge. The poem is a

ballad that relies heavily on repetition and archaic

diction and is told by an old mariner to a man on his way

to a wedding feast.

The “ancient mariner” recounts how he shot an albatross

that was serving as an escort to the ship with his

crossbow. The previous luck he and his fellow sailors had

experience under the guidance of the albatross

evaporates and the ship is left to aimlessly drift when all

wind disappears. The   en#re   crew   except   for   the  ancient   mariner   dies, though they hang the albatross

around his neck before perishing to the depths of the

ocean. Eventually, through ghostly visions and

supernatural interventions, the ancient mariner is

spared. intervention

There is one stanza in the poem that is particularly

salient. Dying from thirst and without hope of any

intervention, the ancient mariner issues the line: “Water,

water everywhere, nor any drop to drink.” Data analysts

likely feel that way today.

The parallel stanza, “data,  data  everywhere,  and  not  a  drop   to  use,” is apropos. Indeed, surrounded by almost

unfathomable amounts of data, data  analysts  are  oAen  overwhelmed   by   its   sheer   volume   and   uncertain  where  to  start. As a result, they are unable to leverage

the data or, at the most, merely scratch the surface of

possibilities.

“Water,  water  everywhere  but  not  a  drop  to  drink…”

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Consider the challenges facing data analysts and

scientists today. The amount of data is exploding: 90  percent  of data is new every two years.3

Analyzing and making this immense sea of data

understandable and actionable is immensely challenging.

And HR is not unaffected. A report by IBM found that 60  percent   of companies admit having disorganized HR

systems and moreover admit no way to make meaningful

data-driven decisions.4

Achieving actionable business intelligence from HR-

related data is most certainly a problem. Only   17  percent  of HR organizations claim they actually use data

analytics, despite 73   percent conceding that data

analytics are important. 5

Figuring Out Where to Start

90% of data is new every two years

60% of companies admit having disorganized

HR systems and no way to make meaningful

data-driven decisions

Only 17% of HR organizations use data

analytics, though 73% concede analytics are

important

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Challenges of Talent Management

1. Controlling Costs 2. Talent Quality 3. Risk Management 4. Market Dynamics

Talent management is most certainly not without its

challenges. Controlling costs is a big issue. Multiple

talent-sourcing channels make it complex to manage.

When it comes to contingent workers, lack of

competitive bidding models drives up costs. Indeed,

organiza#ons  may  pay  anywhere  between  50  and  100  percent  markup  rates without competitive bidding and a

comprehensive understanding of market rates.

Talent quality is a second issue. Organiza#ons   report  they   hire   the  wrong  worker   27   percent   of   the   #me.6

And when they do so, the cost is substantial. A single bad

hire costs more than $50,000. Differences in market

rates from one location to another and one skill set to

another create an additional set of challenges;

overpaying for workers drives up your costs, while

underpaying means you likely are losing high-value talent

to competitors. Further, the time it takes to source often

is the difference between securing the best talent and

losing them to your competitors.

When it comes to contingent talent, there   are   some  very  real  tax  and  compliancy  risks. There are a spate of

new tax and benefit laws on the books such as the

Patient Protection and Affordable Care Act and paid sick

leave laws. Organizations must understand the risks

associated with each of these and how to manage their

contingent workforce to mitigate them. In addition,

contingent labor often resides in silos, and thus

organizations lack enterprise visibility.

Finally, this   on-­‐demand  workforce   is   highly   dynamic;

market rates often fluctuate week to week, coupled with

multiple sourcing segments.

.

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Decision-Making and BI Outcomes

When it comes to talent analytics, there are different

types of decision-making. To begin, the fundamentals of

decision-making are either strategic or tac#cal. Strategic

decision-making examines issues across functions and

departments, often at a macro-level. The analysis looks at

emerging trends—both opportunities and threats—and

helps organizations prioritize them in terms of

importance. Tactical decision-making focuses on real-

time issues, enabling organizations and individuals to

make the best decision based on available data.

Decision-making also involves different business

intelligence outcomes. The base level is descriptive.

Descrip#ve  analy#cs are retrospective in nature, using

data to explain what happened. Predic#ve  analy#cs are

prospective in nature, using data to forecast future

outcomes. Prescrip#ve   analy#cs—the most advanced

level—use artificial intelligence to show potential

outcomes and prescribe optimal recommendations.

Decision-Making

- Tactical

- Strategic

BI Outcomes

- Descriptive

- Predictive

- Prescriptive

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The intersection of strategic and tactical business approaches and the different business outcomes are overlaid on top of

each results in a BI Talent Maturity Model. For the purposes of this investigation, the focus is on contingent workforce

management. Contingent workers are defined as all non-employee talent (e.g., temps, independent contractors,

consultants, freelancers, et al.) sourced through staffing suppliers, independent contractors and consultants, freelancers

routed via freelance marketplaces, or supplied s part of a statement-of-work (SOW) project.

Descriptive: Data used to explain

what happened

Predictive: Data used to forecast

future outcomes

Prescriptive: Show potential

outcomes and prescribe optimal

recommendations

Tactical Strategic

Utilize individual behaviors and

hiring patterns in addition to profiles

and preferences. Graph algorithms

to map “people like you.”

Analysis of key insights on company-

wide hiring, interview trends, and

behaviors. Did you pass on top

candidates for the wrong reasons?

Leverage multiple data sources:

market rates, unemployment

rates, who is hiring = predict what

you will pay.

Model KPI results and make decisions

for locations, divisions, and skill sets

based on empirical data to predict cost

savings based on initiative.

Status and real-time metrics:

SOW funds depleted, time to fill,

market rates, ratings and reviews,

feedback, etc.

Metrics, KPIs, performance across

locations and divisions to feed

strategic planning. Analysis of self-

sourced vs agency-sourced talent.

BI Talent Maturity Model

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BI Maturity Frameworks

1. Tactical-Descriptive. The focus for tactical-descriptive

analytics is on real-­‐#me  metrics  and  status. An example

here is a manager seeking to know the current status of

funds for a SOW project bucket. The managers can

analyze funds depleted and determine if the project is

running under, on, or over budget. They can also sift

through quotes and identify those that are above, below,

or within market rates per skill set and location. Yelp-like

ratings, reviews, and feedback help guide them in

identifying the right SOW vendor.

2. Strategic-Descriptive. Strategic-descriptive analytics

look at metrics,  performance,  and  KPIs  across  loca#ons  and   divisions. These are used to feed organizational-

wide strategic planning. An example of strategic-

descriptive analytics would be a comparison of self-

sourced talent versus talent from staffing suppliers. The

comparison might look at data such as cost, performance,

and temp-to-conversion ratios.

3. Tactical-Predictive. For tactical-predictive data

analytics, an organization can leverage   mul#ple   data  sources such as market rates, unemployment rates and

trends, and those who are hiring (including competitors)

to predict pay rates for both talent and SOW projects.

These types of analytics enable organizations to source

top-talent talent and project suppliers at competitive

market rates. The ability to make these types of decisions

quickly based on predictive modeling also allows

organizations to avoid scenarios where they lose out on

sourcing hard-to-find talent.

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Managing the Global

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4. Strategic-Predictive. Using strategic-predictive

analytics, organizations can model  KPI  results  and  make  staffing   and   SOW   project   decisions   for   loca#ons,  divisions,  and  skill  sets  based  on  empirical  data. These

KPI models predict market rate changes for locations and

skill sets based on external factors. For example, a

location for a new data center may initially appear to be

an excellent decision based on salary analysis for full-

time workers and bill rate analysis for contingent

workers. However, decisions by other companies to

relocate data center operations to the area, organic

growth and expansion of existing data center footprints,

and/or changes in the local job market skew the initial

findings. A strategic-predictive analytics model accounts

for those, enabling organizations to make decisions  that  span   a   #me   con#nuum   and   consider   broader  market  forces  and  changes.

5. Tactical-Prescriptive. The prescriptive layer gets even

more strategic than the predictive layer. At the tactical

BI Maturity Frameworks (cont.)

level, analytics examine individual  behaviors  and  hiring  pa=erns   and  map   candidate   profiles   and   preferences  to   those. Then, leveraging graph algorithms, analytics

become much more actionable by making candidate

recommendations to hiring managers based on the

success of current and former workers. For example,

workers with certain degrees, work preferences, alumni

backgrounds may have a proven track record of better

success with a hiring manager than other workers. As a

result, talent management systems will recommend  those  candidates  over  others, with the recognition they

are more  likely  to  be  successful  in the role being filled.

6. Strategic-Prescriptive. For strategic-prescriptive

analytics, the intelligent recommendations extend  beyond   the   manager   level   to   analysis   of   company-­‐wide   hiring,   interview   trends,   and   behaviors .

Organizations are able to avoid the scenario where they

passed on top candidates for the wrong reasons (e.g., are

there social, educational, professional biases that are

impacting the sourcing and selection process).

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1. Internal / External Data 2. Technology 3. Visualizations 4. Humans

When it comes to actionable BI analytics, there are four

requirements that organizations need to have in place:

1. Internal and External Data. The first requirement is

that the data behind the business intelligence must come

from internal and external sources. These two data sets

must overlap on top of each other, providing deeper

insights and complimenting each other.

2. Technology. A technology platform is the next piece

that you need. There are two things organizations need

here, and both imply integration. On the one hand, a

talent analytics platform must include a strategic data

warehouse that is integrated into your ERP systems. On

the other hand, every organization requires tactical

integration for their sourcing platform that shows

market bill rates, staffing and project supplier

performance scorecards, and other analytics.

3. Visualizations. Visualizations are the third thing

organizations need to get to actionable talent BI. These

need to be customized and show various views of KPIs

and business measurements. Examples here might

include data visualizations on headcount, spend, and

engagements across departments, skill sets, and location.

4. Humans. The final area is that humans are required.

The power of talent-related data analytics is that they

have the ability to challenge established perceptions,

influence new behaviors, and enable business leaders to

make more intelligent business decisions that impact

business outcomes. The crux of the problem is that most

organizations that do employ data analytics for talent

management do so in a rudimentary manner. Further, for

those that are able to conduct some level of data

analytics, less than half admit that they can utilize data

from outside of their HR systems.7

Requirements for Actionable BI

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Requirements for Actionable BI (cont.)

Organizations are inhibited in embracing talent analytics

because many simply do not have the skills to interpret

and apply the analytics at their disposal. A study by

McKinsey found that less  than  18  percent of businesses

have the in-house skill sets needed for actionable BI.8

One of the core recommendations the study makes is

that technology is not sufficient on its own.

Organizations must supplement   data   analy#cs   with  actual   humans—data scientists, business analysts, and

other subject-matter experts—who understand how to

interpret and moreover apply the actual data findings.

Data analytics augment, rather than eliminate, the

knowledge and experience of domain experts. Indeed,

anyone who has listened to Billy Beane speak about how

the Oakland A’s employed Sabermetrics knows that the  data   scien#sts   and   analysts   behind   the   scenes   were  requisite   fundamentals to getting business insights out

of the data.

.

Fewer than 18% of businesses

have in-house skills sets to

produce actionable BI.

Technology plus humans

required to interpret and

apply the data findings.

Less than half of HR

organizations that do use data

analytics for HR decision-

making admit they utilize

external data.

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The Sabermetrics of Talent Management hold great

potential for an industry segment that is an outlier when it

comes to the use of data analytics and BI. The following

are some key takeaways:

First, when data analytics are used for talent recruitment

and management, business performance improves.

Companies that use data analytics and specifically talent

analytics for strategic and tactical decision-making see

higher rates of return.

Second, a talent analytics maturity model consists of six

different areas—strategic and tactical that are overlaid on

top of descriptive, predictive, and prescriptive business

outcomes. Organizations that want to realize the full value

of talent analytics and business intelligence will ensure

their talent management solutions use all six.

Third, organizations that view data analytics as a

replacement to human judgment and experience will fail.

Certain business insights are simply not possible without

the involvement of humans.

Fourth, business platforms utilize many data sources and

must be integrated at the product level. For contingent

talent management, this includes integration into

procurement and HR workflows.

Fifth, there is a war for talent, and organizations that are

able to source hard-to-find talent at competitive market

rates have a strategic advantage. Actionable BI analytics

are one of the essential building blocks that

organizations need to institute in order to stay a step

ahead of their competition.10

A   joint   survey   by   MIT   and   IBM   discovered   that  organiza#ons  with  advanced  HR  analy#cs  see:9  

• 8%  higher  sales  growth  • 24%  higher  net  opera#ng  income  • 58%  higher  sales  per  employee

Five Takeaways

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1“Global Human Capital Trends 2015: Leading in the New World

of Work,” Deloitte University Press, 2015. 2 Andrew McAfee and Erik Brynjolfsson, “Big Data: The

Management Revolution,” Harvard Business Review, October 2012. 3 Paul Zikopoulos, Dirk deRoos, et al., “Big Data Beyond the Hype:

A Guide to Conversations for Today’s Data Center,” IBM, 2013. 4 “Advanced Analytics Report 2015,” Advanced Business

Solutions, September 2015. 5 Ibid. 6 “More Than Half of Companies in the Top Ten World Economies

Have Been Affected by a Bad Hire,” CareerBuilder Survey, May 8,

2013. 7 “Unlock the People Equation: Using Workforce Analytics to

Drive Business Results,” IBM Institute for Business Value,

Executive Report, December 2014. 8 Matt Ariker, Peter Breuer, and Tim McGuire, “How to Get the

Most from Big Data,” McKinsey, December 2014. 9 Andrea Capodicasa, “Why Are Big Data and Analytics Such a

Game Changer for HR?” Capgemini Blog, September 29, 2015. 10 Richard Dobbs, Tim Koller, and Sree Ramaswamy, “The Future

and How to Survive It,” Harvard Business Review, October 2015.

Endnotes

PRO  Unlimited  possesses  25  years  of  con#ngent  workforce  management   and   holds   a   number   of  industry   firsts.   It   has   been  working  with   global  enterprises   to   leverage   data   analy#cs   for  ac#onable  BI  for  15-­‐plus  years.  To  find  out  how  PRO   Unlimited   can   help   you   to   implement   an  ac#onable  BI  approach,  contact  us  today:  

Phone:  1-­‐800-­‐291-­‐1099  Email:  informa#on@prounlimited.com    Website:  www.prounlimited.com  

PRO Unlimited, through its purely vendor-neutral and integrated managed service provider (MSP) and vendor management system (VMS) solutions, helps organizations address the costs, risks, and quality issues associated with managing a contingent workforce. A

pioneer and innovator in the VMS and MSP space, PRO Unlimited offers solutions for e-procurement and management of contingent labor, 1099/co-employment risk management, and third-party payroll for client-sourced contract talent.

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