Compensation Cafe

One Reason Pay Is NOT Skyrocketing

For several months many of us have wondered why base pay wasn’t spiking upward out of respect for the historical strong job market. The basics premise of our economy is the strong demand and short supply results in high prices.  This is true for peaches, cars, and people. With unemployment at very low levels and a job market continuing to create jobs at a rate of 210,000 per month, we should have been seeing pay rise far faster than our now sad 3% annual increase. It turns out the trickster was simple math.

On August 21, 2019, it was announced that our jobs reports since April 2018 were inflated by about 40,000 jobs per month. A new adjustment reduced this number to 170,000 jobs. This is about 125% over-reporting of the facts! This is the kind of error that would get any executive or compensation professional fired. It’s a level of error that could result in a maximum, instead of a target, payout on a highly leveraged incentive plan. It’s like paying your new Level 4 Accountant as if she was at Level 6 or 7. I think you get the point. In the real world this probably wouldn’t be an acceptable error.

The error does help explain the ongoing stagnation in base pay. We have posited that the lull was caused by the use of additional short term incentives. This ignores the fact that the majority of jobs have little or no incentive opportunity, We have read that the stagnation was due to low inflation. This ignored the whole “supply and demand” argument in a fundamental way. The fact is, job growth is just normal and what we have seen for most of the past decade. We were duped, but there is a bright side.

Our industry pay data, often maligned by people named me, stood up to this test pretty well. Even as politicians and economists bragged about the historically strong job market, we applied our principles. We looked at the data and applied our compensation philosophies. We adjusted when specific markets or positions moved to be more competitive, but we did not react to the fantastical world of growth that we could not see with our own eyes.

In short, we did our jobs and did them fairly well. We maintained our course because our data and experience told us more than the reports and breathless articles. Short-term incentives have increased, but mainly as a way to control budgets and most specifically support specific motivations. Some industries have seen explosive growth, but most have grown, or shrunk, as expected.

We have work to do but this is a strong confirmation that we have level heads and can apply what we know consistently. I should note that none of this truly defends average annual increases of 3% in a world where the stock market, housing prices, and medical costs are booming and executive pay has grown at about double the rate of the average person. It’s a start, a very good start.

Dan Walter is a CECP, CEP, and Fellow of Global Equity (FGE). He works as Managing Consultant for FutureSense. He is a leading expert on incentive plan and equity compensation issues and has written several industry resources including the only resource dedicated to Performance-Based Equity Compensation. He has co-authored ”Everything You Do In Compensation is Communication”, “The Decision Makers Guide to Equity Compensation”, “Equity Alternatives” and other books. Connect with Dan on LinkedIn. Or, follow him on Twitter at @DanFutureSense.

Wow. That Data is Terrible!

Almost every compensation professional I know can guess the base pay of mid-level accountant or software engineer with near-perfect accuracy. Given regional variations and other factors, most guesses are within 5% of current market data. This consistency and predictability is a foundation of trust when we are pricing jobs that are less familiar or more volatile. If only it was always so easy.

Annual incentive plans add complexity and noise to pay data. Even when you have confidence in the target and actual levels in the survey data it takes real confidence (or ignorance) to make decisions on how the plan pays out in good and bad scenarios. Even with this uncertainty, most compensation professionals do a great job and getting these numbers within a reasonably accurate range.

Long-term incentives are a different animal altogether. In the best cases, the data is noisy or opaque. A good example of this is the pay packages for executives at publicly traded companies. The data seems like it is all there if you know how to dig through it. Once you get into the thick of things a talented executive compensation professional can make sense of it and provide real guidance. When you move from publicly traded to privately held companies things can go haywire.

I recently looked at two industry-leading data sets. They were for the same size companies. For the same industry. For the same region of the U.S. But that’s where the similarities ended. As I mentioned earlier, a good compensation professional can usually guess the base pay amount for a standard position within 5% of the market. Imagine thinking someone should be paid $100,000 and looking into the data to see they should be paid $853,000. It would probably make you question your abilities or the data. The differences in the two sets of equity data I was reviewing ranged from less than 5% to more than 800%!

I spoke to experts from both survey providers. Each expressed their absolute confidence in their data collection and analysis processes. Most disturbingly was neither thought that the differences were of great concern. Now, I work on equity compensation programs for all sorts of companies nearly every day and have done so for more than twenty years, but this nonchalance in the face of craziness reminded me how hard this can be for someone who only deals with this data once or twice a year.

In some cases, ignorance is bliss. If you had only ever seen one of the sets of data I reviewed, you may not have any idea how different it was from the other. Heaven forbid you saw both and decided to verify which was right by purchasing a third set. I can almost guarantee that the third set would raise just as many questions. We love to say our industry is a combination of art and science, but I think most of us were thinking of art a bit less like that of Jackson Pollack.

I love to wrap each post up with some type of solution, so here goes… When dealing with equity, especially at private companies, expect the data to be terrible. Get more than one set of data so you can have a reasonable range for the terribleness. Then do your process in reverse. Instead of looking at the market data and determining how to fit people in, look at your people and determine what you are trying to accomplish. Then determine what levels of equity and types of features will help you reach your goal. Finally, take a look at the market data and determine if you are, based on your needs, within the right range of accuracy.

I find this type of work to be the most fun and interesting part of my job, but I respect those who want a more solid foundation. I’d love to hear how you deal with this challenge (or not).

Dan Walter is a CECP, CEP, and Fellow of Global Equity (FGE). He works as Managing Consultant for FutureSense. He is passionately committed to aligning pay with company strategy and culture. Dan is also a leading expert on equity compensation issues and has written several industry resources including the one-of-a-kind Performance-Based Equity Compensation. He has co-authored ”Everything You Do In Compensation is Communication”, “The Decision Makers Guide to Equity Compensation”, “Equity Alternatives” and other books. Connect with Dan on LinkedIn. Or, follow him on Twitter at @DanFutureSense.

Value Driver? No, It's Not a Discount Store!

Do you know what value drivers are? In the world of business strategy, a value driver is an activity or input that drives financial and operational results. They are called value drivers to reflect their role in the value creation that your company wants to achieve: 1) Increasing the share price, thus creating more value for shareholders and ensuring your company funds future operations. 2) Creating more value for customers by customizing your product or service to their needs, thus ensuring sales growth.

Why bring up value drivers now? Because they should play the leading role in your efforts to address HR analytics. Value drivers identify what it takes to make your company successful (compared to what it takes to make the company run). Without the defined value drivers for your company, you run the risk of "garbage in, garbage out" in spite of your hard work on HR analytics.

Odds are the executives in many companies have identified the company's value drivers but haven't thought to share them because they don't understand the other, critical roles that value drivers can play beyond pure finance. So if you ask the right people, I bet you can find out what your company's value drivers are.

Then what? Redesign key HR practices to truly impact the bottom line. For example, track where the value drivers are executed in the company to identify key jobs. The answers shouldn't be the same old list. It should go beyond the most senior titles since you're looking for the select jobs that make a disproportionate contribution to the value that the company is working to create. If you can identify these jobs, you'll know where to most profitably target your work of attracting, recruiting, orienting, developing and rewarding.

In case you're worrying whether any HR jobs would qualify, the strategy powerhouse, McKinsey, specifies three categories of value driving functions: Value creators, value enablers (where HR would sit) and value protectors. And if you want a "deep dive" on the potential role of value drivers in HR work, check out McKinsey's podcast, "Matching Talent to Value."

By the way, value drivers also answer the question, "Where should my objectives come from?" You can use them to build an accurate line-of-sight so you can finally measure how well MBOs are impacting your company's bottom-line. (And wouldn't that would be value creation?)

Margaret O'Hanlon, CCP brings deep expertise to discussions on employee pay, performance management, career development and communications at the Café. Her firm, re:Think Consulting, provides market pay information and designs base salary structures, incentive plans, career paths and their implementation plans. Earlier, she was a Principal at Willis Towers Watson. A former Board member for the Bay Area Compensation Association (BACA), Margaret coauthored the popular eBook, Everything You Do (in Compensation) Is Communications, a toolkit that all practitioners can find at https://gumroad.com/l/everythingiscommunication.

Compensation Singularities

Editor's Note: Today's post comes to us courtesy of guest contributor Joe Thompson.

Have you heard of the Compensation Singularity Rule?

I’ll confess that I had not heard of it until quite recently when one of my clients made me aware of the concept.  Frankly, I’m surprised that I hadn’t learned of it sooner because it appears to be an enormously versatile rule that can tremendously streamline the market-pricing process.

The client – one of those Washington, DC inside-the-Beltway agencies – was greatly concerned that their latest salary survey analysis was obviously wrong and needed to be fixed immediately.  It seems that the agency had just lost a senior cybersecurity executive to a private sector company which had reportedly offered the individual a salary of $600K.  (That was according to Fred, who had heard it from Nancy, who had overhead Susan talking about it … but, I’ll put the provenance aside.)  As a result, the client knew, empirically, and unequivocally, that the going market rate for all cybersecurity professionals was measured in the multi-hundreds of thousands of dollars per annum range, significantly above the ranges indicated in the salary surveys.  It turns out the client had applied the Compensation Singularity Rule.

The Compensation Singularity Rule (CSR) states, in part:

A singular compensation data point is vastly more reliable and is far more effective for determining true market rates than hundreds of thousands of compensation survey data points, provided that the singular data point is sufficiently large compared to the median survey rate.  A singular compensation data point that is near, or below, the median survey rate (some sources use P75 vice P50) must be considered an outlier, clearly spurious data. 

A major part of the versatility of the Compensation Singularity Rule lies in the fact that only the magnitude of the singular data point needs to be considered; one need not be concerned with job location, level, industry, duties, or other tedious aspects of job matching in order to apply the CSR.  The Rule can also be used to conclusively demonstrate that compensation is the sole determinant of what makes a job offer competitive (though that is a subject for a different post).

Now, I’ll admit that I had to look the rule up.  WorldatWork hadn’t started teaching it when I took my last course with them, but I’m sure that they’ll have to start soon, based on the frequency at which the Rule seems to be used and cited.  Apparently, it is already widely taught in some of the most prestigious and venerated institutes of higher education including YouTube, Instagram, and WhatsApp.

As wizened compensation professionals I’m sure that you’ve realized that I am being tongue-in-cheek; you have all likely faced this phenomenon in the past.  It seems that no matter how many reputable surveys you have used, how many curated data sets you have drawn upon, or how meticulous your analysis, the Compensation Singularity Rule will always garner the attention of employees and executives.  Compensation singularities seem to have a sort of cognitive gravity well.

Unfortunately, rational discussions about job/industry matching, the nature of right-skewed salary distributions, sample sizes, statistically significant data sets, etc., don’t seem to help in these cases.  Pointing out the obvious – that if the going market rate was truly at the singularity rate, every other employee would have already left for those salaries, too – can end the immediate compensation hyperventilation, but seems to leave people with the nagging suspicion that you have practiced some form of logic legerdemain.   You can free the person from the gravity well, but the effects still linger.

So, what techniques have you found to most effectively address compensation singularities?

Joe Thompson, CCP, CCMP, is currently employed as a Human Capital Strategic Consultant at Booz Allen Hamilton where he assists clients with a wide range of human capital challenges.  He has delivered human capital solutions across the talent management lifecycle including recruitment, job analysis, hiring, compensation and incentives, workforce planning, HR policy, attrition, performance management, change management, cyber human capital, and human capital program design, implementation, and evaluation.  Joe has worked in the area of compensation for 10 years and, prior to Booz Allen, he has worked in the U.S. Intelligence Community and in the U.S. Navy.

Image: https://archive.briankoberlein.com/2015/01/22/pointed-debate/index.html

Cafe Classic: Reimagining Rewards and the Value of Work

Editor's Note:  This Classic appeared a few years ago when the wave of analytics was first hitting us, but its invitation to think the unthinkable remains important today as we work to extract true value from our fast-developing array of tools and data sets. What formerly unthinkable things are you making happen in your job?

There's a wave racing roward us, all of us in HR and particularly those in the more "quantitative" field of employee rewards.  It is a wave of metrics and analytics, and its approach is raising questions (and anxiety) about its impact on what we do in designing and managing programs for rewarding work.

In his recent HRExaminer article Thinking the Unthinkable, John Sumser dishes up a nice summary.

Practitioners are about to be showered with an abundance of data and metrics. For the most part, the first round will be limited by their imaginations. Sadly, that’s a really big limiter.

HR and its processes are going to be automated in the immediate future. The administrative crap is more or less finished (which explains the lack of innovation in most HR software companes). Now, the more thoughtful parts of the business will come under the dome of digitization.

The first wave will be data visualization tools that expose the massive inefficiencies in our processes.

He also takes aim at the degree to which our current state of analysis focuses mostly, often exclusively, on averages and raises a tantalizing question about the way in which we value work.

Anyone who has been following our growing statistical literacy understands that averages hide as much as they disclose. For the state of the art to involve the recounting of averages is a sad commentary on how far behind our profession has fallen.

Rather than a sea of averages, we need to be looking at the value creation. A position (say agile programmer) that generates five times the value of a retail clerk ought to be a bargain at four times the cost and three times the schedule. And, the outliers that drive the average may well be the best (or worst) spent money in the budget.

Historically, our methods for valuing work have focused on the jobs people hold and have essentially followed one of two fundamental directions:

External or market based evaluation, where the value of the job is directly linked to its "going rate" in the relevant labor market, and

Internal evaluation, where the value of a job is established through internal comparisons which focus the compensable factors or dimensions (such as education requirements, problem solving, etc.) of the job.

More recently, skill/competency based work valuation methods have turned the focus from jobs to the workers themselves.  These people-based approaches place value on workers according to the skills and/or competencies the possess and are able to demonstrate.

All of these, however, involve a method of estimating the potential for creating value.  In the new era of more sophisticated analytics and tools, as we use visualization to better understand the work done in our organizations, what will we learn about the ways people and the skills they possess create value?  How will we be able to tie those lessons to our reward programs, so that we can move beyond traditional "pay for performance" and link work more closely and directly to value creation?

In case that isn't enough to get your head spinning, I also want to point you to a video John shared: "Media for Thinking the Unthinkable" by MIT's Bret Victor, embedded below (email subscribers may have to click through to watch).  At 45 minutes, it may be long for office multi-tasking, but worth your while to absorb offline.  Bret's central point: there are things that our human senses can't quite grasp, but we have proven adept over time in building tools that adapt these things to our capabilities (think microscopes and telescopes).  In the same way, we are now learning to build tools (better data visualization tools among them) that adapt many previously "unthinkable thoughts" to the ways our minds work.

Media for Thinking the Unthinkable from Bret Victor on Vimeo.

Don't think this applies to HR and rewards?  Think again - and watch out for that wave!

Ann Bares is the Founder and Editor of Compensation Café,  Author of Compensation Force and Managing Partner of Altura Consulting Group LLC, where she provides compensation consulting and survey administration services to a wide range of client organizations.  She earned her M.B.A. at Northwestern University’s Kellogg School and enjoys reading in her spare time. Follow her on Twitter at @annbares.

Conference Topics To Meet Every Need - Part 2

Editor's Editor's Note: Today's post comes to us courtesy of guest contributor Chris Dobyns, as Part 2 of a two post series where he shares his impressions and takeaways - and particularly his top picks for breakout sessions - from the WorldatWork 2019 Total Rewards Conference and Exhibition, recently held in Orlando, Florida. Did you miss Part 1?  

The WorldatWork 2019 Total Rewards Conference and Exhibition wrapped up last week, hosted at the Rosen Shingle Creek facility in Orlando. The conference attracted approximately 2,000 attendees, comprised of speakers, practitioners, academicians, consultants and exhibitors.

The Breakdown On The Breakouts

The breakout sessions that I attended were all uniformly above average, or better. There were a couple of sessions, however, where the workshop title, the actual content – or both - deviated from the title on the official schedule or the original session summary enough that attendees were left wondering if they were actually sitting in the right room.

This “variance” is probably due to either a misinterpretation of the expected content by attendees or, more likely, that “things” (content, speakers, perspectives, etc.) changed in the six months between the conference and when the original workshop proposal was submitted and accepted. These things just can and do happen.  

The Top Three 

Of the approximately 85 individual breakout presentations offered this year, I made a concerted effort to attend one session in every available timeslot. Even so, that only allowed me to assess (and “rate”) about one-tenth of the actual presentations. Given that small sample size, these are my top picks for 2019. 

1.  My 1^st place pick was, The Lens of the Future: Transforming Total Rewards into a First-Class Service, presented by Antonis Christidis from Mercer, Inc. I was initially worried about this presentation, as one co-presenter had been pulled away at the last minute for work-related reasons, leaving Antonis to carry the whole show. Ultimately this session was successful through some artful story-telling, a novel concept and the use of an illustrative and highly-relatable graphic. 

• This workshop reflected Mercer’s speculation on the future use of Organizational Network Analysis (ONA) as the basis for better valuing (and paying) for skills based on the sustained strength and frequency of formal and informal asymmetric relationships that exist in an organization
• This would be in clear contrast to the prevailing practice of paying for jobs relative to a person’s placement in a vertical hierarchy.
• Less clear was how to account for the “quality” of the network relationships and how to factor in any aspect of individual performance. Regardless, I thought this was one of the most novel and well-presented concepts I saw at the conference this year.

2.   Next up was the hugely fun Pay Equity, Privilege, and Unconscious Bias, presented by Bryan Briscoe from Interface, Inc. This session was part of the first-time Power Half Hour presentations, to demonstrate that some conference content lends itself better to a half-hour format.

• Apparently benefiting from a mix of urban legend, “crowdsourcing” and je ne sais quoi, this session drew a huge audience well north of 500 people.
• This was a super presentation that struck just the right balance of respect for the importance and seriousness of the topic, juxtaposed by a series of humorous interludes that provided the perfect balance of comic relief. 

3.   My third pick was Comp Nerds Play with Data: Meaningful (and Fun!) Analysis for the Rewards Professional, presented by the now familiar team of Bryan Briscoe and Paul Reiman from Commvault.  

• This session began with some insights into what differentiates Nerds from Geeks . . . and which category is more “marriageable” (it’s the former) – which purportedly includes Paul and Bryan.
• This presentation was an informative and fun mix of all-too-common statistical and data visualization missteps and how to avoid them.
• The funniest example of the danger of attributing correlation to causation was how increased ice cream sales during the year is directly correlated with an increase in the number of shark attacks. Just don’t ask.
• The funny/edgy quote comparing artificial intelligence to teenage sex made a repeat appearance – and was even requoted by presenters in two subsequent workshops at the conference. Imitation. The sincerest form of flattery.

Double Your Fun

Doing justice to attending all the relevant workshops at the conference realistically requires a 2-person team. Understandably, organization budgets do not always allow this luxury. Whether just one or many, conference attendance provides an excellent opportunity to see and hear about leading edge practices and to make valuable industry contacts that can expand your professional network. Maybe equally important, the conference provides a chance to refresh, reenergize and have some fun, because there’s literally something to meet every need at these conferences. 

Everyone probably has a different perspective.  What’s yours?

Chris Dobyns, CCP, CBP is currently employed as a Human Capital Strategic Consultant for the Office of Human Resource Strategy and Program Design for one of the largest U.S. intelligence agencies. The Office of Human Resource Strategy and Program Design is responsible for organizational effectiveness, personnel assessment, compensation and incentives, occupational structure, recognition and rewards, HR policy, human capital program design, implementation, evaluation and assessment and internal consulting. Chris has worked in the area of compensation for more than 35 years, and has been employed in various compensation-related positions by a number of large, private sector companies including, Sears, Roebuck, Arizona Public Service and Westinghouse Savannah River Company.

Original "Conference Themes" image adapted from WorldatWork by Chris Dobyns.

Conference Topics To Meet Every Need

Editor's Note: Today's post comes to us courtesy of guest contributor Chris Dobyns, as Part 1 of a two-post series, where he shares his impressions and takeaways - and particularly his top picks for breakout sessions - from the WorldatWork 2019 Total Rewards Conference and Exhibition, recently held in Orlando, Florida.

The WorldatWork 2019 Total Rewards Conference and Exhibition wrapped up last week, hosted at the Rosen Shingle Creek facility in Orlando. The conference attracted approximately 2,000 attendees, comprised of speakers, practitioners, academicians and exhibitors. Attendees came to get the latest news and innovations in the area of compensation, benefits and work-life practices.  This year’s workshops were aligned around the four key themes of Learn, Solve, Disrupt and Envision.

The conference kicked off with the standard events, beginning on Monday AM with a remarkably good plated-breakfast (it is the most important meal of the day). This was followed by a conference welcome and remarks from association President and CEO, Scott Cawood. Scott emphasized the need for both improved awareness and preparation for the transition to a digital economy, given the expected exponential growth of technology in the next 3-5 years. Organizations were encouraged to continue to fine-tune (or perhaps just begin) their digital strategy development, particularly with an eye on the future implications for the total rewards space.

 You Are Enough

Keynote speaker, Chris “Bash” Bashinelli, followed and advocated conference attendees to endeavor to walk in the shoes of a global citizen, which is exactly what he has done as a “20-something year old” in his global travels for PBS – both literally and figuratively over the last several years. He encouraged the audience to Stop, Listen, Learn and look to make connections with others to understand their perspective. Then, do your best  . . . and that will be accepted.

2019 Workshop Focus

Individual conference workshops were once again centered around the topical categories of: Benefits, Compensation, Executive Compensation, Sales Compensation, Total Rewards and Workforce Experience.

There weren’t too many surprises this year regarding the topical focus of individual workshop sessions. As expected, some clear themes and trends emerged around the topics of gender pay inequity, unconscious bias, data analytics/visualization in support of improved understanding of pay, organizational value and maintaining competitiveness – to retain top talent.

Technological advances provided hints at future inroads and areas ripe for automation, but no innovations met the definition to be categorized as “artificial intelligence” yet.  There were some sessions that reflected nascent machine learning or robotic process automation, either in current use or actively being contemplated.

Terminology Shifts, Twists and Turns

The broader term “well-being” appears to have supplanted the term “wellness”, in generally describing employee health and state-of-mind. And in acceptance of what is apparently now considered the “new normal”, the former terminology work-life balance has all but been replaced by what is probably the more accurate phrase, work-life integration. This trend tacitly acknowledges the continued pervasiveness of technology and the difficulty for employees to realistically separate work from their regular life activities. 

Lastly, there was a noticeable emphasis on the escalating importance of financial counseling for employees, principally to help them prepare for retirement and better withstand financial hardship events. The 500 lb. gorilla present in at least two workshop sessions was the continuing concern about the $1.5 trillion in student loan debt, and how that is effecting employees practically and psychologically – particularly employees in entry-level positions.

Anecdotal Audience Observations

A couple of noteworthy trends appeared to be on the rise in terms of the audience demographics, although these observations are admittedly about as unscientific as they get.  Based on attendance at now four of the last five conferences, the gender demographic would appear to reflect that women comprise the majority of the attendees. The accuracy of this observation and if women also comprise a majority of the demographic in the total rewards profession remains unclear.    

A slightly more scientific observation was the discernible number of first-time attendees at the conference. In both the General Session as well as a heavily-attended breakout, by an informal census close to 40 percent of the attendees self-identified as being the first-time attendees to the Total Rewards Conference this year. No additional conclusions or inferences are supported, but both observations are perhaps interesting artifacts. 

The Top Workshop Picks for 2019

In a repeat of last year’s conference summary, Part 2 of this article will focus on individual workshops and the handful of topics and their respective presenter [teams], and what really separated these sessions from the rest of the pack.  Stay tuned for the next continuation of the series – and the big “reveal”. 

Everyone probably has a different perspective.  What’s yours?

Chris Dobyns, CCP, CBP is currently employed as a Human Capital Strategic Consultant for the Office of Human Resource Strategy and Program Design for one of the largest U.S. intelligence agencies. The Office of Human Resource Strategy and Program Design is responsible for organizational effectiveness, personnel assessment, compensation and incentives, occupational structure, recognition and rewards, HR policy, human capital program design, implementation, evaluation and assessment and internal consulting. Chris has worked in the area of compensation for more than 35 years, and has been employed in various compensation-related positions by a number of large, private sector companies including, Sears, Roebuck, Arizona Public Service and Westinghouse Savannah River Company.

Original "Conference Themes" image adapted from WorldatWork by Chris Dobyns.

Algorithms and Formulas Are Us

We are moving rapidly into an increasingly “data-driven” age of Human Resources and Compensation. This data is translated into information. Some of it is quantitative, some of it is visual, all of it is probably biased. Understanding the origin and reasons for potential biases will give the most successful companies an edge that may be impossible to beat.

In the end, HR and Compensation are about winning. Winning the battle for talent. Winning the battle for performance. Winning the battle for consistency. Winning the battle for a pleasant and rewarding place to work. Having an advantage in those battles is what we strive for when we create unique programs, pay differently than our peers and build a culture that inspires. The age of data geeks is upon us and we need to be able to use that to our advantage.

The gold standard of data algorithms may be those used by search engines like Google. Here’s the rub, every piece of data is selected because someone wrote a program that indicated it should be selected. Every data outlier rejected didn’t fit a set of guidelines that was first created by a person. Stanford recently did a study in 2018 that showed a distinct pattern of bias in image-based search results. The results would probably not be categorized as discriminatory, but they also cannot be considered totally accurate.

When the result of an algorithm is a group of photos that appear on your screen, bias may not be that harmful. When an algorithm drives pay decisions or more, we should understand how and why the algorithm works the way it does. A perfect example of this is survey data. The first step in a survey is deciding what data to collect. People make those decisions.

Data that is hard to gather is often made a low priority. For example, we seldom see the details behind incentive plan terms, conditions and features. When data is messy, it may be avoided because the final report is simply too hard. For example, the lack of reward and recognition program details in most pay surveys. Data that is hard to understand may be summarized at a level where it lacks any impact. A good example of this is equity compensation data.

Finally, if the collected data just seems weird, it is often minimized or ignored entirely. Some of the most unique and effective pay programs never seem to appear in survey data, even when we know those companies are participants. We don’t love surprises. We like things to match our expectations. We want smooth transitions. We will modify data, job leveling, structures and more in order to fit the data into our models.

The pitch made by the data pros is that the data will fit an objective model and ignore our biases. But, it’s tough to ignore the biases of the data pros, especially if we don’t know what those biases may be. When data always meets your expectations, it may be due to the fact that your expectations are driving the data, instead of the other way around. In a data-driven age, the art of HR and Compensation is even more important than ever before. Your expertise, insight, and experience may be the secret sauce that gives your company the edge against peers who simply take the data at face value and execute accordingly.

Dan Walter is a CECP and CEP and works as Managing Consultant for FutureSense. He is passionately committed to aligning pay with company strategy and culture. Dan is also a leading expert on equity compensation issues and has written several industry resources including the one-of-a-kind Performance-Based Equity Compensation. He has co-authored ”Everything You Do In Compensation is Communication”, “The Decision Makers Guide to Equity Compensation”, “Equity Alternatives” and other books. Connect with Dan on LinkedIn. Or, follow him on Twitter at @DanFutureSense.

Cafe Classic: All Decisions Are B.A.D. Decisions

Editor's Note: Even in the age of AI, ML and even TMI, SWAGs based on B.A.D. are still necessary.  Jim Brennan shares some Classic pointers!

All decisions are B.A.D. decisions, relying on the Best Available Data known at the time.

Even compensation experts, supposedly the most analytical of HR types, have difficulties with data.  Makes sense to me, because all human decisions are B.A.D. decisions, made according to the “best available data” when action was required.   

When there is no good data, you must rely on the Best Available Data.  The resulting acronym (B.A.D.) is quite meaningful as well as memorable.  The process of making decisions without access to complete and perfect information occurs every day.  The best professional survey analyses involving “counts”, for example, report their reliability statistics.  And guess what they define?  The estimated rate of error contained in their statistical results.  Standard error is a vital measure because it predicts how much of a miss is likely when relying on the normative results derived from a particular observation sample.  It is much like the spread in the shot pattern you get from pellets dispersed by a shotgun; or from sand thrown by hand, where wet sand will be more densely clustered than loose sand. 

Every measurement, even the best, comes with a certain risk of error.  Tight standards of precision offer more hope of being “right” and strenghten the ability to make inferences from studies, but that is not always possible or useful.  The real world is filled with bad information, inadequate facts and missing data points.  And the opposite situation occurs, too, where you are flooded with contradictory information.  In this Internet world, it seems like anyone can find support for almost any contention somewhere.  Too Much Information (TMI) has become a catchphrase acronym. 

Relevance counts, as well.  Despite access to all the information in the world, you can never be sure that one particular answer will prove to be valid in your particular unique situation. 

Sooner or later, you may reach a point where you can only speculate before acting without perfect confidence.  Even in the numbers-oriented world of compensation management, Scientific Wild-Ass Guesses (SWAGs) still are required sometimes.  Operating in circumstances where useful facts may be missing  is a normal part of the total rewards profession.  The more often you enter uncharted waters, the more frequently you will find an absence of robust benchmark studies with relevant useful facts that can guide your choices, suggest options or demonstrate context.  Then, it is time to get B.A.D.!

The best approach when dealing with ambiguity generally is to define your assumptions, explain the risk level and give it your best shot.  For instance, if no one complains over an admittedly arbitrary guess about what you think a specific job should be paid, you are probably offering to pay too high and they don't want you to find out because it makes life easier for them.  When hiring managers grumble but are able to comply easily and operate adequately, then the approximation is probably just about right. 

If any SWAG based on the Best Available Data works out, then your B.A.D. decision will be the best possible outcome.

E. James (Jim) Brennan is an independent compensation advisor with extensive total rewards experience in most industries.  After corporate HR posts and consulting CEO roles, he was Senior Associate of pay surveyor ERI before returning to consulting in 2015. A prolific writer (author of the Performance Management Workbook), speaker and frequent expert witness in reasonable executive compensation court cases, Jim also serves on the Advisory Board of the Compensation and Benefits Review.