Dear Email, a Love Letter

Dear Email,

You have gotten a bad rap. You get destroyed and end a political career.  You get tweeted in an effort to be transparent but instead potentially put a “there” in a “there’s no there, there” narrative.  You can drown some in notifications or serve as a diary for others.  While many hope your death is imminent, I remain devoted.  How do I love thee?  Let me count the ways.

  1. You always know when.  You have a handy-dandy date and time stamp that helps shed light on what the drafter was thinking at that precise moment in time.  This stamp is used to create the all-important timeline of events.
  2. You’re easy. With just a few clicks and pounds of my keyboard, you are put in a file that I can search and retrieve later when I need you again.
  3. You’re findable. Even when you are used to document something – as sent only to the drafter – you appear in both an inbox and a sent file folder.  This means you exist at least twice.  When an email is sent or forwarded to numerous people, you exist in even more file folders.  Even when you’re deleted, you go to a deleted file where someone has to take yet another step to truly delete you.  This means it is really hard to completely lose you and completely destroy you.  If I can figure out who got you, I can most likely find you using fancy forensics.
  4. You’re nearly everywhere. Fifty-four percent of the world has at least one email account. (I have three.)  Think about that.  Half of the planet has email.  This means that most understand and use email regularly.  We email our accountants, doctors, lawyers, and friends seeking advice and support.
  5. You’re important. Sometimes, you’re are silly.  Sometimes, you’re dumb.  A lot of the time, you’re amazing evidence.  Just like the stuff people say, the stuff that makes it into email is stunning.  This includes that time that someone quoted Sir Mix-A-Lot in an email to a co-worker, remarking that his “Anaconda don’t want none…”  Uff da, indeed.  (Note, great song, poor context.)
  6. You’re the best. When done right – without opinion or superfluous adjectives – you can save a case.  People believe you, and sometimes, they believe you more than they believe live testimony.

For all of these reasons, I just can’t quit you, email.  You remain one of my top recommendations for documenting performance, discipline, outlandish behavior, awkward conversations, and whatever else befalls HR departments.  I just hope you are done right and don’t need a lot of explaining.

Love, Kate

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Email Dance

Performance Data Based Analytics

How can you do big HR data analytics when you eliminate performance rating?

A well-known people analytics person posted this question on LinkedIn a week ago.  The discussion that followed was fascinating.  The question took my breath away for a couple of reasons: (1) performance ratings are one of the most bias-ridden data points on any employee, so it makes me queasy to think such data would be relied upon heavily in an analytic, and (2) so much more data exists that can provide better insights.

Let’s take the biased data first.  While I am no Marcus Buckingham devotee, his piece for Harvard Business Review back in February 2015 encapsulates the idea that HR data – particularly performance data – is bad data.  By in large, managers rate employee performance on how they would perform the job, not on the actual performance of the individual employee.  If, as Mr. Buckingham points out, 61% of a rating is really a rating of the rater, then ratings aren’t that useful in making people decisions.  (In a post for another time, performance management doesn’t even need ratings.)  This bad data makes for bad analytic results.

This is furthered by oodles of studies that show unconscious bias is baked into performance data.  There is no escaping it.  The unconscious bias of managers – which is not to shame managers, we all have bias – is active in performance reviews and data.  If women and minorities are rated more harshly because of the bias, then an analytic tool that relies on performance data also contains the bias in those harsher critiques and could perpetuate or exacerbate discrimination.  This can occur even when we remove demographic data through data proxies.  Who wants biased data in their analytic tool?

Next, the idea that HR analytics must rely on performance data misses the plethora of other data that we could use to make people decisions.  Here are but a few examples:

  • Great leaders have connections throughout companies. We can find out who has developed a network within a company by reviewing email connections, social media connections, and other network analysis.
  • Internal threats sometimes start with emailing themselves information on personal email accounts. Monitoring access (authorized and unauthorized) along with other retention analysis can help identify who could be stealing our trade secrets or confidential information to take to a competitor.
  • We can better predict how to scale hiring and what skill sets are needed based upon productivity and sales projections.
  • Things like weather, productivity, date, and time can all be factors in safety incidents. If we analyzed these items, we could develop a work schedule that reduces worker injuries.

These examples show employers could do better without touching the bad and biased performance data.  If we didn’t include biased and bad data, would it be the end of people analytic tools?  I emphatically answer “no.”  We could do even better without it.


Image by Markus Spiske available at