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I know it’s pathetic, but one of my wishes during Thanksgiving dinner … other than to avoid major indigestion, plus of course good health to all those I care about … was to learn what great things the HR Tech market’s largest players were doing in the predictive HCM arena. This would clearly make my recently launched research effort pretty darn interesting.
As this is clearly one of the more nascent HR Tech areas, I really don’t expect to hear about an abundance of mature, robust predictive capabilities just yet. I do expect, however, that many of the large HCM / HRMS solution providers we invited to participate in the research will have a reasonably clear and compelling product strategy and execution plans around their product’s predictive capabilities. Also, in my effort to take a read on this emerging capability area (the research’s main objective), I’m hoping to hear about HR Tech customer experiences related to leveraging these powerful capabilities.
From the HR Tech Practitioner Trenches
When I dabbled on the HR Tech practitioner side (around 20 years of dabbling), my corporate HR colleagues and I sometimes sat around brainstorming about how to possibly predict such things as:
- Which on-boarding aspects, if changed, could contribute to accelerating time-to-productivity
- What are reliable indicators of “very high upside” in a candidate or employee’s profile
- Will a job candidate, employee (considered for a new team or department) or a corporate acquisition target be a good fit from a culture perspective
- Will changing an employee’s job, manager or team have a positive or negative impact on performance, retention, engagement, etc.
- Will changing comp and/or benefits plans to reduce costs adversely impact the company in other ways
These “skull sessions” often ended with the same seemingly rhetorical question (at the time): “Can we ever expect HR Tech capabilities to help us out here?”
Bottom Line Regarding the Research Just Launched
Whether I’m getting ahead of myself by hoping the above questions will ultimately be supported by HCM systems remains to be seen. But I remain hopeful that I will be hearing from some HR Tech vendors that such predictive opportunities are not only on their radar, but they’re close to rolling these and other impressive capabilities out over the next 12-18 months.
A former colleague had a penchant for using phrases that stuck with me... One of them was - “I have questions for all your answers.” It took me years of working with Charles Edward “Skip” Odell to learn that his middle name was Edward, thereby explaining why the letters “CEO” on his cuffed shirts were not just aspirational.
During those same years, the HR technology domain was very much growing up, and the topic of predictive capabilities wasn’t generating many questions or answers in most customer or solution vendor circles.
While HR technology solutions have clearly matured in many ways (e.g., engaging user experiences leading to broader usage outside HR Departments, mobile computing’s dominance and increasing cognitive capabilities), the use of science within HCM platforms is arguably still at the adolescent stage. Lots of promise, seemingly random growth spurts, daunting challenges and some really pleasant surprises along the way.
Pulling Back the Curtain on Predictive HCM Analytics Capabilities
What are some of the pleasant surprises? Well for starters, literally -- as these were in-fact the first predictive capabilities introduced in the HR tech arena -- more customers are now using tools that highlight employee retention risks, or future star performers among job candidates. Both of these predictive capabilities, and most others, are of course generally based on validated algorithms adapted to the customer’s business context and data relationships; and either the customer’s data scientists or the system itself (via machine learning) does the adapting and periodic re-calibrating.
But as Skip astutely pointed out, interesting answers often beget more good questions. So relative to predicting retention risk or future star employees -- or any other situation or outcome that is attracting predictive HR tech capabilities, here is a small sampling of questions that arise:
- What are some of the most impactful and innovative examples of predictive analytics available to HR technology customers today, and which are being widely leveraged?
- How long does it typically take for a particular HCM system’s predictive capabilities to start becoming evident, valuable and/or reliable?
- Do the predictive capabilities within enterprise HCM software apply to most organizations using them, or is the predictive value sometimes more robust in certain industries or types of organizations?
- What are some of the operational factors that might enhance or impede the business value to be derived when deploying predictive HR technology?
- When should the guidance and insights delivered by predictive HCM tools be acted upon – including on the basis of prescriptive analytics; i.e., when the system prescribes specific and generally reliable actions to take?
- What are the key trade-offs (e.g., benefits and risks) inherent in predictive engines that adapt themselves through machine learning … vs. engines (=algorithms) that rely more on customers to adapt them?
- Finally, how will these capabilities evolve over the next few years, and will most customer organizations be ready and equipped to take advantage of these advances?
Announcing Groundbreaking Research
HfS Research will be pulling back the curtain on the above questions and many other interesting nuances related to leveraging these emerging HR technology capabilities. We’re very excited to announce our first-of-its-kind research and Blueprint Report entitled “Predictive Analytics in HCM Systems.” Publication is set for March 2017, and we expect many of the major HCM vendors – both HRMS and Talent Management Suite vendors – to participate.
A Point of View (POV) “Time-to-Predictive Value in HCM Solutions” is also being published this week and is available (complimentary) with a registration if you are not already a member of the HfS research and knowledge-sharing community.
There are some amazing capabilities being brought to market that clearly demonstrate the arrival of science in HCM systems. For customer organizations wanting to take advantage of the increasing scope of these predictive capabilities, and for solution providers wanting to continue differentiating through related product innovations, the research we’ve just initiated should be quite valuable.
Predictive analytics in human capital management continues its slow but inexorable march out of the sizzle phase and into the steak -- or for my vegan friends quinoa -- phase. As this phenomenon is occurring, a few topics are getting considerable air time.
- How are predictive engines adapted and applied to the unique business context of every organization – and by whom!
- What types of predictive capabilities in HCM solutions (largely algorithms coupled with machine learning and human testing) have the most relevance and value to a particular HR/HCM agenda?
- Will the predictive analytics guide in solving business problems? … and the all-important …
- How much do data scientists earn and can HR afford them?
Forecasting the winners… more to come (winners and research)
An HCM or Talent Management offering that lacks a compelling predictive analytics strategy and capability set, and is competing outside of smaller companies, is akin to the proverbial “dog that won’t hunt.” (Yes I’ve fully acclimated to living in the South). Although from the buyer perspective, trying to unpack a vendor’s people analytics strategy, or just distinguish it from other capabilities out there that sound awfully similar, might keep some dogs hunting for a while. I’ve maintained for years that the HR tech market needs much more clarity around how solutions are different and why the difference really matters, in a language that typical HR professionals relate to. The absence of this makes the landscape more cluttered and more confusing for buyers.
I’ll be covering key operational and technology dependencies that affect the leveraging of people analytics in my upcoming HfS Blueprint Guide entitled “Predictive Analytics in HR Technology.” This will be published in early March 2017, but way before that, my related HfS POV is coming out in the next week or so. Among other things, it will offer-up a new industry metric called “Time to Predictive Value.” For now, here’s a preview.
Assessing a solution’s predictive analytics capabilities – checkmark or not
Here are three lenses to apply when evaluating whether an HR tech product’s predictive analytics will achieve desired outcomes; and by product, I mean HRMS platform, Talent Management Suite or HR Point Solution:
Time-to-Predictive Value (“TtPV”) is my stab (POV forthcoming!) at creating a meaningful guidepost to help judge one aspect of a product’s capabilities in this realm. It will hopefully bring some much needed clarity to a domain where, for example, “retention or flight risk” -– not a very meaningful metric in isolation, as most metrics aren’t –- often gets a vendor a quarter or half-way toward qualifying for a predictive analytics checkmark.
There are various operational dependencies for leveraging predictive analytics in HCM (or within any business discipline), such as having a large enough relevant data set, sufficient analytics and data science competencies and staff, pursuing closed-loop validations with well defined scenarios, applying appropriate calibrations for different data (e.g., job and organizational) contexts either performed by people or machines (via machine learning), etc. These dependencies and conditions typically take time to be addressed –- from weeks to months or longer. Buyers should have a sense of when they will actually see the predictive value manifesting itself, as that influences ROI and is also a major input to my lens #2.
Degree of Predictive Analytics Business Impact: There’s a wide range of potential business impact and value to be derived from these capabilities in HCM. Two factors that seem to correlate with impact beyond TtPV:
- Whether the best actions or decisions are being guided by the predictive information. In other words, is the analytic prescriptive as well as predictive? (A reason why retention risk in isolation probably has less value than what is often hyped.)
- Is the business problem being solved/avoided, or opportunity created, going to deliver noticeable competitive advantage? Examples include knowing the most important predictors of job success in a critical role, or what factors materially drive or impede employee productivity or customer retention, or is the organization truly ready to succeed on a strategic initiative?
Finally, Innovativeness (yes, it’s a word) of the predictive analytics capabilities: The more innovative a set of these capabilities are, particularly if they lead to practical and measurable business value delivered in relatively short order, the more it inspires other creative ways for solution providers to help solve HCM business problems. Data correlations and cause-and-effect relationships that are very intuitive to discern or simply the product of good common sense (e.g., freezing salary increases or cutting back on company-paid benefits will likely result in a spike in employee turnover) earn very low marks on the innovativeness scale.
In contrast, when Walmart years ago determined that putting diapers on sale will often lead to increased beer sales (somewhat logical, but only AFTER the non-intuitive relationship was discovered), now that’s a winner.
People analytics is hot, and predictive capabilities is a major reason why. But in order for customers to derive business value commensurate with what they are paying for the surrounding solution, they must look beyond the sizzle and assess the quality of the steak in meaningful and business-relevant ways.
Steve Goldberg (click for bio) is Research Vice President, HR Technology and Workforce Strategies at HfS Research
Back when enterprise time began and God was handing out the technology dollars, why was the Chief HR Officer always seemingly at the back of the queue? Why did so many of our beloved enterprises become plagued with the clunkiest, funkiest legacy systems we never could have dreamed up in our worst nightmares? Especially when you consider the data critically and sensitivity of one's employees - their profiles, their health records, their compensation, their performance etc...
So it's no surprise that the advent of the SaaS based HR suite has been embraced like manna from Infosys heaven. Suddenly, our HR-technology plagued enterprises can hatch a plan to rip out the cancerous legacy and slam in something that's standardized, has hire-to-retire process that are sort of adequate, and doesn't require that cobol transformation project each time you try to push through an exception payment. So what better timing than for HfS to bring aboard Steve Goldberg - a true veteran of the HR tech world - to lead our thinking in the space and is freshly returned to his desk from the HR Technology show (read his blogs here).
Welcome Steve! Can you share a little about your background and why you have chosen research and strategy as your career path?
Sure Phil. I've basically operated on all sides of HR Technology, so a real diversity of experiences. This includes HRIS and Talent Management practitioners in the U.S. and Europe,
Another takeaway from the HR Tech Conference
One of the age-old knocks on the HR profession is that it attracted those who prided themselves on “being good with people.” I was never really sure what this meant when I selected HR / HR Systems as a career path way back when, but it seemed better than being good with hazardous waste. This notion was eventually borne out by the fact that my shortest corporate HR stint was with a Waste Management industry leader.
So how does this relate to the recent HR Tech Conference? Well, beyond what was discussed in my last post about smaller players doing their share to drive product innovation, another realization hit me: Dozens of newer HR technologies are not just “good with people,” but “really smart about people.” This means knowing personal if not unique drivers, how to engage and motivate, and leveraging that context for the benefit of both the organization and its individuals. Employing different talent management and employee engagement approaches for different talent pools (e.g., early career vs. later career or more experienced employees, high potentials, high-value candidates, change-resistors, etc.) makes very good sense.
Notwithstanding having my 13th HR Technology Conference participation cut short by needing to return home to Florida to deal with a hurricane, one major observation stood out for me. It was also a fairly pleasant surprise, something that doesn’t come easy after attending so many of these events—as enriching as they usually are.
In fact, the hurricane actually contributed to the observation. How? Well, in having to unfortunately cancel briefings with major HR Tech vendors to leave early on Thursday, I had to rely more on quick-hitting discovery sessions in the exhibit hall, generally with lesser known vendors. They are typically not as schedule-constrained at the conference.
So, here it is: I found it just as easy to see meaningful HR Tech innovations in the booths of “little guys” and emerging players as I did in their much larger and more established counterparts. I’ll define ”meaningful product innovations” as practical, obviously value-creating (vs. largely “wow factor”) advances where the system’s intelligence is leveraged without a lot of heavy lifting or major change adoption needed by the customer organization … dependencies often under-estimated by vendors and customers.
This is my inaugural blog post for HfS Research, an analyst firm I’m very pleased to now be part of.
I joined HfS because of the deeply held belief that HCM solution vendors could be bringing more clarity to the buying decision and even drive more compelling business outcomes for customers, and that a certain type of analyst firm could help pave the way.
I also joined HfS because, like hockey players go where the puck will be vs. where it is, HfS struck me as a firm that is not only going where the puck will be, but arguably laying down the ice for a new arena. And in the spirit of “I’ll try almost anything twice,” I had an opportunity in 2011 to work with someone I (and legions of others) greatly admire, Josh Bersin, and I also covered the HR Tech landscape then.
Attending the annual HR Tech Conference, as I’ve done 12 of the last 15 years, is like going to a family reunion for me, only a bit less gossip and lamenting about getting old (given tech sector demographics). Re-nourishing the relationships cultivated over the years is frankly as important to me as the intel gathering done at the conference, although the latter makes for a much easier cost justification.
I started going when I served as PeopleSoft’s HCM Product Strategy head, and would have gone when I was an HRIS practitioner from the mid-80s to late 90s but no equivalent conference existed in my view. This one rules the roost.
My esteemed colleagues at HfS, Phil Fersht (founder and CEO) and Barbra McGann (Chief Research Officer), asked me to do a pre-event post on what I’d like to see, and then a post-event post on how much of my wish list was fulfilled – AND BY WHICH HCM VENDORS IN PARTICULAR.
My list follows, and I strongly encourage appropriate vendor contacts to reach out to me at [email protected] so you can brief me in Chicago on the extent to which your offerings align with any of the items mentioned here:
- HR-user configurability of the solution, even not-very-technical HR users.
- Prescriptive analytics (i.e., analytics that also guide the user in addressing or solving a problem vs. just reporting the news).
- Examples of cognitive computing that demonstrate real machine learning such as pattern recognition and appropriate actions automatically initiated at either the micro (employee) or macro (workforce) level
- Product innovations that can drive significant business results for customers without major operational dependencies (e.g., change management, process changes, competency re-alignments, etc.), or innovations that will be central to solving customer business problems or pains that are likely to become more acute over the next 5 years. Examples of the latter might relate to the impending mass exit of baby boomers from the workforce, more reliance on freelancers, etc.
- Technology that mirrors the way end-users think and solve problems, often in idiosyncratic ways.
- Evidence of how a vendor’s customer success model is helping customers achieve measurable user adoption and business value targets.
- … and in general, more acknowledgement that no matter how great the solution is, technology by itself is no more than perhaps 40-50% of the answer to solving business challenges in the HCM domain.
I’m genuinely excited about once again navigating the HR Tech vendor and solution landscape at the annual HR Tech event, culling and calling out nuggets that buyers will find valuable; and very keen to do so on behalf of HfS Research.