In a data economy, wealth is defined by extracting value from good quality data. The challenge is how to ensure a sustainable source of good quality HR data and how to turn that data into a compelling story that engages business leaders and creates a competitive advantage through talent.

1. We live in a Data Economy
www.peopletreegroup.com
info@peopletreegroup.com

2. Data is changing
the way we
Connecting strangers
AirBnB
Matching supply and demand
Uber
Changing the way we shop
Amazon
LIVE
On-demand contracting
Upwork
Employees choosing employers
Glassdoor
New business models
Etsy
WORK

3. Creating “Wealth” in a data
economy relies on a
sustainable source of
Complete
Data
Current
Data
Accurate
Data

4. The way HR
acquires data has evolved
Unification
One Multi-purpose
system
Focus on efficiency
Transaction
Many single purpose
systems
Focus on technology
Innovation
Best in Class
User-based Apps
Focus on user engagement
1980 1990 2000 2010 2015 2020

5. Being data poor can loose
you a seat at the table
The new CEO
will be
ready later
We made lots
of money
last year
CFO CHRO

6. The new CEO
will be ready
in 2.6 years
We made
$ 1,320,087,000
last year
CFO CHRO
Being data rich wins you a
seat at the table

7. Being data “rich” is
HR’s BIGGEST CHALLENGE
-32
*Based on 7086 response from global HR practitioners

8. Systems have evolved
Processes haven’t

9. When all you have is a
hammer, everything looks
like a nail

10. Data is not enough

11. It needs to be compelling

12. When meaningful data is
used to tell a compelling story,
we make better choices.

13. Compelling HR Stories
we’ve helped our clients tell

14. Succession Management
A multi-national telecoms company had been using the traditional opinion-based succession management process
to identify possible successors for key positions through out the business, but research showed that these identified
succesors were appointed in less than 1 in 14 cases (7%).
Most often successors identified as “Ready Now” were not appointed due to “lack of readiness” and external
appointments were made in 1 in 4 cases.
So how was readiness mis-identified in so many cases?
The process of estimating readiness is a complex one. Because it is trying to calculate an “Estimated Time of
Arrival” (ETA) for a specific person and a specific position it relies on accurately estimating two variables. The
“distance”, or gap, between the person and the target position, and the “speed”, or rate at which the person can
adapt and learn.
When readiness is estimated with an opinion, too many data points, and their relation to one another, are ignored
or disregarded. Past performance is frequently seen as a predictor of future performance, even when the conditions
under which the person will operate are significantly different.
By using analytics to identify and collect all the relevant data points for “distance” and “speed”, we were able to
create an algorithm that more accurately predicts the timeframe for readiness.
Read more
Moving beyond “Ready Now” and “Ready Later”

15. Digital Transformation
As a multi-national banking group, there was little choice but to embark on a journey towards the increased growth
and cost savings promised by “going digital”. A world of changing interactivity, increased mobility and insightful
analytics.
But what may appear to be purely technology-driven, has far greater implications for culture, capabilities and talent.
The speed of technological change far outstrips the rate at which people can change.
Developing a talent strategy to manage the digital transformation needed to answer these 5 questions.
Challenge 1: How will the digital strategy change the demands of each person’s current role?
Challenge 2: How well does each individual match their new digital role?
Challenge 3: Should we develop for this role, or redeploy the person to a role with a better fit?
Challenge 4: Where is the best opportunity for deployment?
Challenge 5: How do we connect people with the relevant developmenal resources?
Youlab was used to profile 15,000 individuals in 7 countries and created the analytics to answer these 5 high
value questions.
Read more
A bankers journey to the digital age

16. Predictive Performance Analytics
A mid-sized communications company that had relied on managers to provide informal performance feedback
realized that the quality and consistency of that feedback was significantly different. They wanted to implement a
performance process but were well aware of the generally perceived ineffectiveness of performance processes.
The challenge was to implement a process that was easy to use, but most importantly, could be both backward
(evaluation of past performance) and forward (predictive of future performance) looking.
The company decided that 4 criteria should be used to evaluate the fit of any process or system.
1. Does it clearly communicate expectations? We created a process that prioritized and customized a limited set of
performance dimensions to ensure consistency, but provide flexibility (see what these were in the case study)
2. Does it effectively motivate people to achieve their goals? We designed an incentive process that tapped into
the psychology of reward, was perceived as transparent and fair, and linked to company results.
3. Does it accurately predict success/failure? We used machine learning to predict performance with a correlation
of 0.6 (for comparison, EQ only has a correlation of 0.24 to 0.3 with job performance)
4. Does it improve the quality of the conversation? We provided everyone with the Youlab application that identifies
potential problem areas and provides immediate advice to the individual and the manager.
Read more
Stacking the odds in your favor

17. Loss Analysis
We all know that by the time you are doing an exit interview it’s too late to change someone’s mind, but most
companies do them anyway. However few companies translate that data into a story that changes the way people
are managed and “regrettable loss” is reduced.
With the number of opportunities available to really talented people, and their ease of access to those opportunities,
if you are not proactively connecting them with opportunities in your company, they will leave.
The 3 key questions that need to be answered are:
1. Who are these people?
2. What opportunities are they looking for?
3. Where is the best place for them in your company?
The first step we took to answer these questions was to analyze the termination data over an 8 month period. We
found out that that high performers, between 34 – 36 years of age, who had been with the company for between
4 – 6 years, at junior management level were the most at risk of leaving, specifically around March.
By inviting individuals who met these criteria to join Youlab, a career exploration application, we created a one-to-
one connection between each of them and the HR function who were in the best position to offer growth and
development opportunities.
Each individual has now shared their strength profile and career interests and is been proactively managed.
Was it something I said?

18. Conclusion
We live in a data economy
HR is traditionally data poor
Wealth in this economy is
created through data
Data is not enough
Compelling stories using HR data can
help companies make better people
choices

19. info@peopletreegroup.com
www.peopletreegroup.com
+1 786 245 5258