MCBASSI & COMPANY

HR Analytics Haiku Contest

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To celebrate National Poetry Month this April, we at McBassi & Company are sponsoring what we believe must be the first-ever HR Analytics Haiku Contest.

For those of you in need of a refresher, a haiku consists of exactly 17 syllables, with the syllables arranged in three lines of 5-7-5.

The pros would, of course, provide a more nuanced definition.  And while we here at McBassi & Company are analytics experts, we are definite amateurs when it comes to haikus.  As evidence, check out the following three examples of HR Analytics haikus, written by none other than McBassi’s (ever-poetic) staff:

Supply meets demand
HR analytics has
Finally arrived

Disparate data
Creatively analyzed
Creates clear insights

Got messy data?
No worries – analytics
Still a good option

Think you can do better than that?  Then this is the contest for you!

Contest Rules
1.  Entries must be received by midnight (EDT) on April 30, and should be sent to info@mcbassi.com with the subject line “Haiku Submission.”  (Multiple entries per contestant are acceptable.

2.  All haikus must be original.

3.  The winner will be chosen by the McBassi staff, whose decision is final.  Disputes and protests will be reviewed at the sole discretion of the staff.

4.  The winner’s haiku becomes the property of McBassi & Company and will be published, with full attribution and kudos to the author, in McBassi’s May newsletter.

Prize for the Winner
In addition to the expected national recognition and fame that will accompany being the first-ever winner of the HR Analytics Haiku Contest, the lucky winner will receive two books:

  • the HRAH (at the risk of stating the obvious, this stands for HR Analytics Handbook)
  • the HUFAZ (which, equally obviously, stands for Haiku U: From Aristotle to Zola, 100 Great Books in 17 Syllables)

So send those responses in — and may the best haiku win!

 

(This post was sent this month via email to our monthly newsletter subscribers.  Click here if you’d like to subscribe.)

Can HR analytics save the world?

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Well, probably not, but it does hold real promise of making the world a better place.

Recently, we’ve been using every free moment of non-client time to pen the HR Analytics Handbook: A Summary of the State of Knowledge, which will be published this month by Reed Business in Amsterdam.

We learned a lot in the process.  And it’s made us stop and think hard about the good (and bad) that could come from the emerging discipline of HR analytics (the application of statistical analysis to people-related data in order to drive better individual or organizational results).

Let’s start with the bad.  So what’s the worst-case scenario if HR analytics really catches on?  One could envision employers somehow using HR analytics to delve ever-more deeply into the private and work behaviors of employees (even knowing every little detail of their medical history and personal habits), and using that information to become more efficient in sorting out the wheat from the chaff.  Almost certainly there would be more than a few bad choices along the way.  Not a pretty picture.

A more probable scenario is that employers will use HR analytics to continue to improve the efficiency of their hiring, succession planning, and  job design processes.  HR analytics would be applied primarily to wring out whatever vestiges of inefficiency may still remain in the workplace.  This could make workplaces less frustrating for the majority of employees.  Not a bad picture, but not terribly inspiring either.

A good outcome would involve employers using insights from HR Analytics to find the “sweet spot”: the intersection between more profitable and more enlightened management and development of people.  That would represent a win-win for employers and employees.

Now that would make the world a better place. That’s what we work on every day.

(This post was sent via email to our monthly newsletter subscribers last week.  Click here if you’d like to subscribe.)

5 steps to creating actionable insights on the people side

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After exploring pitfalls on the path to business intelligence in my last blog post, this week I’m going to look at the other side of the coin: what you need to do to help ensure that you actually are creating actionable intelligence on the people side of your business.

Our new book, the HR Analytics Handbook: A Summary of the State of Knowledge, is a practical guide to help busy HR professionals and executives get quickly up to speed on the fast developing field of HR (or human capital) analytics.   Success in applying analytics to your people data in order to create actionable insights requires that you follow five key steps:

1. Develop smarter measures of the human drivers of performance

This requires measuring the human drivers of results.  As a practicality, this means that HR needs to go beyond traditional notions and measures of employee engagement.  Employee engagement is necessary, but not sufficient, for driving organization business results.  (link to our “Debunking” article)

2. Focus on the “right” key performance indicators

The right key performance indicators (KPIs) are those that are:

  • Critical to a key stakeholder
  • Available on a timely basis
  • Comparably measured across multiple units

3. Use the correct quantitative methods to link these measures to performance

The most appropriate quantitative methods will be determined primarily by the number of data points (“observations”) available for analysis.  The number of data points available is, in turn, determined by the appropriate “unit of analysis” for the KPI under consideration (individual, team, business unit).   When the number of units of analysis is small, use simple statistical techniques.  When the number of units of analysis is large, you’ll be able to use more complex statistical techniques.

4. Present your analysis in a simple, easy-to-understand format

Do your best to summarize the results of your complex analysis in a small number (even one, if possible) of simple graphics.  (For an example, see Figure 1 in this paper about the drivers of plant success.)

5. Share stories that back up your quantitative analysis

Most folks (even quant types) remember stories.  So weave together your analytics with stories, and key stakeholders to tell the stories for you.  Combining analysis with stories is a winning combination!

5 pitfalls to avoid on the path to business intelligence

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My co-authors and I just finished writing the HR Analytics Handbook: A Summary of the State of Knowledge (to be published by Reed Business next month).

In the course of this writing project, we spent substantial time pondering what really gets in the way of HR professionals as they embark on a path of analytics—of creating actionable business intelligence, rather than simply collecting more HR facts and figures.

We identified 5 key pitfalls that should be avoided in beginning the process of applying analytics within your organization:

 1.       Measuring what is easy versus what is important

HR departments often have enormous amounts of data on the HR function itself, spending considerable time and effort on HR scorecards that focus on the department’s own efficiency.  But this has little to do with the human drivers of business results.

 2.       Confusing benchmarking with HR analytics

Benchmarking is never going to identify the unique human drivers of your organization’s performance.

3.       Accepting clever marketing parading as science

This is a particularly common mistake when it comes to understanding employee engagement, where our analysis reveals that the drivers of employee engagement across different organizations are consistently more different than they are similar (even among businesses within the same industry).

4.       Focusing on ROI  as the “holy grail”

Beyond telling you whether to continue or stop making an investment, ROI tells you nothing about how to improve the business impact of your investments.

5.       Allowing perfect to be the enemy of good

Sometimes perfect information simply is not available, but HR professionals should not use this as an excuse; they should seek to learn from the evidence that does exist.  For example, we know that correlation does not prove causation.  But if you only had information on the human correlates of your company’s business results, it would be foolish to ignore it.  Even correlations often provide valuable learning about key associations within your organization – even if they don’t prove causation.