HR Analytics and Big Data @ Work
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HR Analytics and Big Data @ Work
Articles, research and reports on HR Analytics, Big Data and related topics
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What to Do Before You Fire a Pivotal Employee - HBR

What to Do Before You Fire a Pivotal Employee - HBR | HR Analytics and Big Data @ Work | Scoop.it
Map the impact on your company’s network.
Tony Brugman (Bright & Company)'s insight:

Very interesting article by Belgian HR and Analytics researchers Bart Baesens, Luc Sels and Sophie De Winne, combining social network analysis and analytics.

 

For example to assess the impact of firing a certain employee. "(It) could reveal new serendipitous insights into the relationship between employee and customer drain, which would allow a company to create an unprecedented competitive advantage."

 

In the HBR article they briefly describe

1. how to model an employee/social network through analysis

2. how to further mapping out the employee network

3. how to analyse the employee network using descriptive metrics

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It’s Embarrassing: Every Business Function Measures Quality, Except Recruiting

It’s Embarrassing: Every Business Function Measures Quality, Except Recruiting | HR Analytics and Big Data @ Work | Scoop.it
Recruiting Is Literally the Last Function to Measure It’s Output Quality Quality is such an easy-to-understand thing. It is an improvement in performance a

Via Andrée Laforge
Tony Brugman (Bright & Company)'s insight:

Whoops! (Great analysis as always by ERE's John Sullivan!)

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Andrée Laforge's curator insight, July 28, 2015 8:00 AM

Super article de John Sullivan sur la qualité de l'embauche!

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The Future of Predictive Analytics — the Next Generation of Talent Metrics to Consider (Part 2 of 2) - ERE.net

The Future of Predictive Analytics — the Next Generation of Talent Metrics to Consider (Part 2 of 2) - ERE.net | HR Analytics and Big Data @ Work | Scoop.it
A comprehensive list of future predictive talent metrics
Tony Brugman (Bright & Company)'s insight:

Part 2 of a great collection of predictive talent analytics and metrics by ERE.net's John Sullivan.

 

In part 1 he presented a list of current and near future predictive talent analytics and metrics which every companies should be able to implement now and in the near future. Next, he provides a list of future predictive metrics that should be developed during the third generation.

 

Giving the analytics developments in other industries and fields of management, Sullivan foresees that (when it comes to talent management and HR) "we can expect a wide array of sophisticated public and proprietary predictive metrics to be developed over the next five years". He compiled a list of 25 possible advanced predictive analytics for the world of talent management and HR.

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Dave Ulrich: 'HR Analytics? That's Peeling from the Outside!'

Dave Ulrich: 'HR Analytics? That's Peeling from the Outside!' | HR Analytics and Big Data @ Work | Scoop.it
Dave Ulrich & The RBL Group came up with great new HR Analytics & Metrics insights to answer the question: How to add value through HR metrics & analytics?
Tony Brugman (Bright & Company)'s insight:

Colleagues of iNostix made a full transcription of the RBL Group Slideshare presentation on 'HR Metrics & Analytics: Creating value through measuring and analyzing'.

 

RBL's takeaways to create value:

1. Start with building analytical acumen. This is how you can build your fluency

2. Know what metrics are important for your business

3. Understand which HR practices are important in order to build organisational capabilities

4. Improve your practices by measuring what matters and find meaningful patterns in the data

5. Provide value by measuring data to predict future issues like employee turnover

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Most HR Data Is Bad Data

Most HR Data Is Bad Data | HR Analytics and Big Data @ Work | Scoop.it
Bottom line: when we look at a rating we think it reveals something about the ratee, but it doesn’t, not really. Instead it reveals a lot about the rater.
Tony Brugman (Bright & Company)'s insight:

Critical HBR article discussing why "all of the data we use to decide who should get promoted is bad data". Although the author makes a good point (eg. data based on perceptions of people by other people are not the most reliable), he does not, unfortunately, present any other solutions to overcome these somewhat 'polluted' data.

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Proprietary Metrics — the Next Big Thing in Talent Management - ERE.net

Proprietary Metrics — the Next Big Thing in Talent Management - ERE.net | HR Analytics and Big Data @ Work | Scoop.it

"I am a strong advocate of what I call “parallel benchmarking,” which is borrowing the proven best practices from completely different industries and functions. This article advocates the borrowing and the adaptation to talent management of what are known as “proprietary metrics” from the baseball industry. Proprietary metrics get their name because they cover metrics that are so powerful that they are “owned” and their components are therefore not shared."

Tony Brugman (Bright & Company)'s insight:

John Sullivan introduces Proprietary Metrics, another great Baseball invention coming to HR (Analytics)!

 

Interestingly, metrics can be used as a competitive advantage:

"Proprietary metrics in both baseball and talent management by definition are unique and valuable, so the data used, the methods for collecting the data, and the components in the metric formula are all treated as valuable secrets. This exclusive or limited use allows executives using the proprietary metrics to make better talent decisions than their competitors."

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Merging engagement survey outcomes with business data

Merging engagement survey outcomes with business data | HR Analytics and Big Data @ Work | Scoop.it
HR can expand its potential as a strategic partner given a more intelligent approach connecting HR and business data with the results of the engagement study. By linking the broader-scope engagemen...
Tony Brugman (Bright & Company)'s insight:

Great example of how to visualize engagement survey results effectively and with a clear story, while linking them to business data. (Courtesy of iNostix)

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16 HR Metrics Smart HR Departments Track

16 HR Metrics Smart HR Departments Track | HR Analytics and Big Data @ Work | Scoop.it

Via HR Trend Institute
Tony Brugman (Bright & Company)'s insight:

Great article by Globoforce! Darcy Jacobsen listed 16 HR Metrics that could bring more insight and life to your HR Dashboard. The list consists of Recruitment Metrics, Retention Metrics, Compensation/Benefits Metrics, Culture and Diversity Metrics, Staffing/Performance/Productivity Metrics, Development Metrics, and Health, Safety & Sustainability Metrics. 

 

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Your New Secretary: An Algorithm

Your New Secretary: An Algorithm | HR Analytics and Big Data @ Work | Scoop.it
Data scientists are beginning to peer into work relationships, trying to identify patterns that can improve how we collaborate with peers, manage sales relationships, or see how we stack up against colleagues.
Tony Brugman (Bright & Company)'s insight:

Cool examples of People Analytics! Read the WSJ item on 'Your New Secretary: An Algorithm'. How data and smart software are helping businesses and employees to improve their work life and work relationships.

 

Also, watch the Video: "Can an algorithm improve your work life? Evelyn Rusli explains why the next frontier for data is improving your work relationships."

http://live.wsj.com/video/your-new-secretary-an-algorithm/529D42D9-DD6F-4B0F-BD6B-39B51B3CB0F5.html#!529D42D9-DD6F-4B0F-BD6B-39B51B3CB0F5

 

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Develop a Recruiter Scorecard … Because Champions Demand That You Keep Score

Develop a Recruiter Scorecard … Because Champions Demand That You Keep Score | HR Analytics and Big Data @ Work | Scoop.it

"Champions insist that you keep score. If you understand that concept, you shouldn’t be surprised that one of the best ways to separate champion recruiters from weak ones is to bring up the topic of assessing individual recruiter performance."


Via Andrée Laforge
Tony Brugman (Bright & Company)'s insight:

ERE.net Part 1 of John Sullivan's excellent post on keeping a scorecard for recruiters to increase accountability (and in the end their performance). Sullivan gives three scorecard examples:

- A focus on the percentage of improvement in performance

- A year-long assessment of a recruiter’s progress

- A performance comparison to the best and the best ever

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11 Guiding Principles to Maximize HR Analytics Effectiveness

11 Guiding Principles to Maximize HR Analytics Effectiveness | HR Analytics and Big Data @ Work | Scoop.it
HR Analytics are certainly not a new topic in our business, however they have become very popular recently. We have done this type of work for organizations of all sizes for nearly twenty years—so ...
Tony Brugman (Bright & Company)'s insight:

Great read on iNostix' HR Intelligence Blog. A solid set of must-know guidelines and common mistakes when embarking onto the field of HR Analytics.

 

For example:
- "Organizations make investments in people without any data or with the wrong data"
- "Once a connection/linkage is made with the data, accountability is unavoidable (and that’s a good thing)"
- "Actual business impact must be shown—making predictions are not enough"

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Analytics used to predict who will leave a job!

Analytics used to predict who will leave a job! | HR Analytics and Big Data @ Work | Scoop.it
WSJ - March 14, 2013, 3:36 PM ET Joel Schectman Book: HP Piloted Program to Predict Which Workers Would Quit Hewlett Packard Co. tested a predictive scoring system that attempted to grade the likel...

Via Tanuj Poddar, Ramesh Babu C S, HR Trend Institute
Tony Brugman (Bright & Company)'s insight:

Interesting news. According to the new book Predictive Analytics, by Eric Siegel, HP piloted a scoring system to predict which workers would quit.

Siegel: "The analytics model looked at factors such as salaries, promotions and job rotations, and scored the likelihood that particular employees would leave."

 

More importantly, data scientists at HP estimated a full implementation of the system has a potential of $300 million in savings. "The current extent of the program is not known." To be continued...?!

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Unlocking Big Data: A Strategy for Workforce Analytics

Learn how to convert workforce data into meaningful insights to grow your company.
Tony Brugman (Bright & Company)'s insight:

Nice dialogue with Mercer's HR experts Brian Kelly and Wendy Hirsch by AMA. As usual the talk covers the general insights about why companies and especially HR should take HR metrics and HR analytics seriously. I like the example told by one of the experts, to emphasize the importance of following the adequate steps, when engaging into HR Analytics:

 

"We learned that the company started out, like many other companies before them, by first figuring out its data landscape—gathering what it has  data on and asking if the data are complete and relevant."

 

"One month Jim asked his analytics specialist, who compiles and sends the monthly reports, to hold off sending the report to gage reaction.  Do you want to know what they heard back…nothing! That's right, not a thing."

 

"In other words, start with the impact you want, that is, the question you want to answer. Then choose the metrics you’ll use to assess the impact. Then collect the data you’ll use to generate your metrics, report your findings, and assess whether you had the impact you desired."

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For Entrepreneur360 'Data Champions,' Success Is Steady, Plotted and Planned

For Entrepreneur360 'Data Champions,' Success Is Steady, Plotted and Planned | HR Analytics and Big Data @ Work | Scoop.it
These companies push forward with proven strategies to drive results.
Tony Brugman (Bright & Company)'s insight:

What does set the best data-driven entrepreneurial companies apart from its less performing peers? According to thorough analysis by Entrepreneur Magazine the characteristics of these 'Data Champions' are:

 

>> They often emphasize a reliance on performance data, sharing it across the company to help employees at all levels make decisions.


>> They are much more likely than their peers to offer employees higher pay and a share of profit.

 

>> They pay close attention to planning and managing growth, looking to build a sustainable functioning business.

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Why Not Turning HR Metrics Into Predictive HR Analytics?

Why Not Turning HR Metrics Into Predictive HR Analytics? | HR Analytics and Big Data @ Work | Scoop.it
For a recent project iNostix has developed a shortlist of Talent Metrics and turned them into a corresponding list of Predictive HR Analytics to learn from.
Tony Brugman (Bright & Company)'s insight:

Nice overview from HR Analytics experts of iNostix. Clear-cut and simple!


"Believe it or not but for any descriptive HR Metric (looking at the past) several predictive questions (looking at the future) can be developed. It’s not that difficult, it’s a matter of a different perspective."

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The Future Of Predictive Analytics – The Next Generation of Talent Metrics to Consider (Part 1 of 2) - ERE.net

The Future Of Predictive Analytics – The Next Generation of Talent Metrics to Consider (Part 1 of 2) - ERE.net | HR Analytics and Big Data @ Work | Scoop.it
A comprehensive list of current and future predictive talent metrics
Tony Brugman (Bright & Company)'s insight:

Great stuff by John Sullivan, giving us a (respectively) top 3 and top 18 list of what he calls first and second generation of predictive analytics/metrics. Excellent resource!

 

Disclaimer and advise by Sullivan: "Although these second-generation predictive analytics are more difficult to develop, it’s hard to argue against the potential business impacts that these predictive metrics will eventually provide."

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Why HR reporting can be very misleading!

Why HR reporting can be very misleading! | HR Analytics and Big Data @ Work | Scoop.it
HRIS-based reporting is often misleading without anyone knowing it. Like it or not, regression analysis is absolutely necessary to correctly assess things.

Via David Green
Tony Brugman (Bright & Company)'s insight:

Good piece from our colleagues at iNostix on the dangers of HR data reporting, written by Jeroen Delmotte. He warns that...

- HR reporting does not tell you everything,

- Regression analysis is absolutely necessary,

- (and therefore without proper analysis) Descriptive HR reporting can be very misleading!

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HR Ranks at the Bottom — Reasons to Adopt Metrics and Predictive Analytics

HR Ranks at the Bottom — Reasons to Adopt Metrics and Predictive Analytics | HR Analytics and Big Data @ Work | Scoop.it

John S"When you survey the most frequent users of analytics and metrics in the corporate world, not surprisingly you find that HR ranks at the very bottom. Compared to finance, which is ranked No. 1, HR compares poorly with only half of its functions being classified as advanced users and three times more HR functions are classified as non-users."


Via David Green
Tony Brugman (Bright & Company)'s insight:

John Sullivan from ERE.net captured, collected and summed up a neat list of 28 reasons why firms should shift to "a data-based model" or even better to a model based on predictive analaytics. Nice read!

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CIPD - You say tomato, I say tomahto

CIPD - You say tomato, I say tomahto | HR Analytics and Big Data @ Work | Scoop.it

"The term Human Capital Management (HCM) arouses strong feelings, amongst HR professionals at least! In one corner it represents an attempt to categorise people as mere widgets in a machine in order to understand how to minimise their costs. In the other corner (and I have to hold my hand up here) HCM is a means of trying to understand the value of the contribution that people make to organisational performance."

Tony Brugman (Bright & Company)'s insight:

A critical review by CIPD on Human Capital management and measurement:

 

"While some people argue that there is little difference between the concept of 'human capital' and that of 'human resources', the key distinction is that the focus of human capital is on the value of employees, and how this may be measured, rather than on the HR function itself."

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The Difference Between Workforce Analytics & Big Data

The Difference Between Workforce Analytics & Big Data | HR Analytics and Big Data @ Work | Scoop.it
Big data and workforce analytics are often misunderstood. Learn how they're different and important in building workplace strateiges.
Tony Brugman (Bright & Company)'s insight:

While giving an analogy about her pregnancy, the author makes a good point on HR analytics: "HR metrics and formulas are pieces to the puzzle in helping you make the best decisions and find correlations between the analytics and metrics you are using." You have to look at the big(ger) picture in order to make the best decision for your work, team etc.

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Top 10 HR Metrics Tracked in Workforce Planning - i4cp

Top 10 HR Metrics  Tracked in Workforce Planning - i4cp | HR Analytics and Big Data @ Work | Scoop.it
RT @hrm_today: Top 10 #HR metrics tracked in workforce planning - via i4cp: http://t.co/6nWVDau2A0

Via Andrée Laforge
Tony Brugman (Bright & Company)'s insight:

Interesting findings from new i4cp study on usage of HR metrics in workforce planning. Read more on the study here: http://www.i4cp.com/productivity-blog/2013/07/18/new-i4cp-research-identifies-critical-workforce-planning-metrics

 

Quote from the related article:

"The research revealed that, for example, high-performance organizations are almost 3X more likely than low-performers (53% vs. 18%) to measure new-hire attrition rates. High turnover rates for new employees can signal problems with recruitment, onboarding, training or even employer branding."

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HR needs metrics and analytics to retain star performers following EU plans to widen bonus caps

HR needs metrics and analytics to retain star performers following EU plans to widen bonus caps | HR Analytics and Big Data @ Work | Scoop.it

The EU recently published its plans to widen bonus caps for banks, but despite the hype that this will lead to loss of staff and competitive advantage, banks have already begun to make positive preparations for these changes.


Via Andrée Laforge
Tony Brugman (Bright & Company)'s insight:

Interesting take on moving away from bonuses and need of HR Analytics in HR Magazine. The writer of the article says it "requires a complex way of measuring performance - metrics that for many companies don't currently exist. (..) Without HR analytics, measurement becomes a subjective "finger in the air" exercise based on networking and relationships within the company rather than on performance."

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Top Performers Produce 4x More Output and Higher Quality Referrals

Top Performers Produce 4x More Output and Higher Quality Referrals | HR Analytics and Big Data @ Work | Scoop.it

"Articles from academics don’t always provide practical lessons, but there have been two recent ones that everyone in talent management should pay attention to."


Via Andrée Laforge
Tony Brugman (Bright & Company)'s insight:

Excellent article on ERE.net (written by John Sullivan) discussing two studies  that provide valuable and practical lessons for HR and Talent Management. The first study “revealed that the top 5 percent of the workforce at the researched firms produced 26 percent of the firm’s total output.” This high ROI rate has consequences for HR: “Top performers need to be prioritized.”

 

The second study fits with the former one. This study shows that “a referral from a top performer who is hired will produce nearly three times more profit impact (+90%) for the firm compared to the referred worker from a below average performer.” Some consequences for HR: Measure quality of hire and identify high-quality referrers.

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Will Moneyball Analytics Kill Loyalty and Leadership?

Will Moneyball Analytics Kill Loyalty and Leadership? | HR Analytics and Big Data @ Work | Scoop.it
Past achievements mean nothing if you don't have future potential.
Tony Brugman (Bright & Company)'s insight:

Provoking article and ditto discussion on HBR. The author, Michael Schrage (MIT Sloan), discusses whether employees (from pro athletes to business marketers) gain their importance through results from the past or based on future value. According to the writer, the increasing use of 'Moneyball'-inspired analytics and metrics will also increase the emphasis on the latter.

 

"You are what the analytics predict you will be. That changes the social — as well as the professional — contract."

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Big Data: Scorecards and Dashboards | Talent Management Blog

Big Data: Scorecards and Dashboards | Talent Management Blog | HR Analytics and Big Data @ Work | Scoop.it

"Last week we discussed data from anecdotes and storytelling. This was the beginning of the human capital analytics continuum. This week I want to talk about scorecards and dashboards."

Tony Brugman (Bright & Company)'s insight:

Column by HR Analytics expert Gene Pease on Scorecards and Dashboards. Pretty basic stuff, but nevertheless a good read for learning those basics. Also it gives more color to what Pease calls the ‘Analytical Continuum’.

 

At the end, he warns users to overly rely on these instruments: “Dashboards are a good place to start. However, I caution against making decisions solely based on dashboard data. In many cases, this data is correlated, but that does not always imply causality.”

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