We live in a Data Economy

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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

Creating “Wealth” in a data economy relies on a sustainable source of

Complete Data

Current Data

Accurate Data

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

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

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

Being data “rich” is HR’s BIGGEST CHALLENGE

-32

*Based on 7086 response from global HR practitioners

Systems have evolved Processes haven’t

When all you have is a hammer, everything looks

like a nail

Data is not enough

It needs to be compelling

When meaningful data is used to tell a compelling story,

we make better choices.

Compelling HR Stories we’ve helped our clients tell

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”

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

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

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?

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

info@peopletreegroup.com www.peopletreegroup.com

+1 786 245 5258