What Makes a Successful People Analytics Professional?

To help understand why some People Analytics professionals are more successful than others I undertook a worldwide request for insight. 

I have long held the opinion that 3 basic core competencies were prevalent in successful People Analytics professionals; but to generate a complete profile, I wanted accompanying information on their professional backgrounds, career aspirations, and the organizations who gave them their first People Analytics role.

The core competencies referred to are:

• Close familiarity with the organizations needs and culture
• Strong people skills
• An open mind

Ultimately my request would suggest the importance of 2 additional factors:

• Familiarity with data and HR (context)
• An identified focus (definition of success)

Respondent Profile

Most respondents were currently working within HR when they assumed their role, though their specific task at the time is unknown. HR was and continues to be the organizational home for most People Analytic roles.  Almost half indicated their first People Analytics role emerged gradually from a previous role rather than being specifically created. It was an even split between the first role being a Team of One or not.  Two thirds had no specific career path in mind and the same portion feel their careers’ next step will remain within HR.  However almost 100% envision People Analytics (PA) being part of their future career, in or out of HR.

The greatest self-reported strengths attributable to receiving the People Analytics role were familiarity with data and HR, with technology and math skills also being significant.

Lessons Learned

If we use SUCCESS and EMBRACING RESULTS separately for scoring, there are 3 areas where lessons can be learned in building our profile:

1. Employee background
2. Availability of People Analytics resources
3. Identification of a specific business problem

These lessons are inter-related, but they raise two new questions that are not fully answerable from these results.  We can discuss these in our recommendations, but the questions are:

• Can core competencies overcome deficiencies in the ideal profile?
• Can a People Analytics role that fails to influence an organization be considered a success? 

Employee Background

No link could be identified between a specific background attribute and success.  However, there is a definite link between their background and having their results being embraced. Those respondents who did not have results embraced heavily attributed data familiarity as a strength but had no reported HR strength. Perhaps this was a contextual issue pointing to a weakness in understanding what is important to the company, the correct perspective on HR data, or poor people skills (core competencies).

Availability of People Analytic Resources

Resource availability seemed to have no impact on success.  Slightly more than half of successful respondents were given specific tools, but 40% of successful respondents were provided no team, budget, tools, or other resources. This seems to be another area suggesting the need for core competencies.  An open mind may allow the focus to remain on the problem to be solved instead of viewing it from the perspective of an available solution to be used.  People skills can empower a professional to leverage resources from other areas of the organization.

Identification of a Specific Business Problem

Unsuccessful roles usually lacked an identified business problem to address. Stated another way, there was no stated focus. It is my sense that defining focus is the biggest improvement opportunity for both organizations new to People Analytics as well as those who have been practicing for a while.

We have already drawn a link between an employee’s background and results not being embraced.

Almost none of those situations had a specific business problem to address, and neither were they considered successful.

In addition to pre-identifying business problems, many organizations do find value in exploring data to uncover unknown areas for improvement (focus) and following the insights provided.  Predictive modeling is a common example of this in People Analytics. In these circumstances business value is found in both historic metrics such as turnover as well as predictive metrics such as attrition risk. 

Conclusions

If we construct a candidate profile of a successful People Analytics professional whose work was embraced, they would be working within HR and have a well-rounded familiarity with HR, data, technology, and math.  Their employer provides a clear definition of success by defining a problem on which to focus. Core competencies they possess allow them to overcome the dearth of any resource need as well as the ability to deftly convey their insights back to their organization in an effective, appreciable manner.  It is important to note that these core competencies could possibly exist within a single individual or be spread amongst a team.

In initiatives that were not embraced, there are several identifiable trouble spots to address.  The most visible is the lack of focus/defined business problem.  It is not uncommon to expect data to tell you where to focus, but perhaps this is a distinct skillset beyond the stated core competencies.  Another concern is highlighted by unembraced initiatives involving People Analytic professionals who reported strength in data familiarity but no strength in HR.  Core competencies may provide the people skills to appropriately share insights.  However, the nuance of people data and the HR process seems to be lacking in this subset. This possibly points to the need for some HR functional context or guidance on conveying their message.

To summarize, ingredients for a successful People Analytics professional producing results that will be embraced by the organization seem to be:

• • Close familiarity with the organizations needs and culture
  • Strong people skills
  • An open mind

2) Familiarity with data and HR (context)

3) An identified focus (definition of success)

Recommendations

The lack of core competencies in an individual does not necessarily doom a People Analytics initiative, or that individual’s participation in it.  This situation can be overcome by using formal or informal teams to ensure each skill set is available. 

It is also advisable to ensure proper context is in place. This involves more than simply examining how the defined business problem is impacting the organization.  The People Analytics professional(s) involved may not have a full awareness of the nuances and breadth of the HR function itself.  Perhaps an “HR 101” course could be used to explain the relevance of Recruiting, Learning, Total Rewards, Performance, etc.  and why those employee processes and data are unique. An alternative to this could be ensuring an HR expert closely reviews all results before they are shared with the business.

Perhaps the most significant recommendation is having a definition of success: an identified business problem was a strong component of successful initiatives.  There is also a place for exploring your data to find areas of improvement.  Caution should be used, and this is where strong people skills will come into play; without a defined focus, the People Analytics professional will have found a problem that was previously unidentified.  Calling attention to it and providing suggestions on its resolution can be interpreted as criticizing an organizational leader and telling them how to do their job.

The two questions raised but unanswerable by the provided insights were:

• Can core competencies overcome deficiencies in the ideal profile?
• Can a People Analytics role that fails to influence an organization be considered a success? 

Core competencies are true skills and reflect an ability to get things done. This ability powers People Analytics professionals to find resource alternatives, ideal communication techniques, and relevant focus topics.  It is my opinion these competencies do a tremendous job of overcoming any inherent shortcomings in a defined role.

We must not settle for simply being right but also strive to be effective. People Analytics cannot be successful when results are unembraced by the organization. The goal of any decision support role is to empower better decision making and provide our data-consumers with relevant insights in a meaningful way.  Effective People Analytic professionals base their insights on trustworthy data and irrefutable metrics. This is especially relevant with the burgeoning use of artificial intelligence and predictive modeling. People Analytic professionals would do well to remain skeptical of any predictive model that is not fully transparent, cannot be explained, and is verifiably void of hidden bias. 

Insight Purpose & Process

My insight request occurred as a survey shared among social media and industry websites so as broad an audience as possible could be captured. Participants responded from all global regions and the intent was to create a snapshot in time reflecting circumstances when they undertook their first People Analytics role.  These circumstances were then compared with both their success in that role and whether their organization embraced their results.

The quest was not driven by simple curiosity but a desire to help identify a replicable profile.  My work In the People Analytics technology space involves helping my customers succeed in their role and build a practiced embraced by their organization.  This resulting profile will be shared with my customers and used to identify areas where I can help them improve. 

Where are you in your People Analytics Career or Journey?

One Model can provide guidance around all the above profile ingredients, and create a path for you to establish yourself as a People Analytics leader as you move forward.

• Step 1: One Model can help you define your organization's critical metrics and understand how to present them to various layers of decision makers.
• Step 2: Our team of data engineers can solve your problem of HR data portability and quickly integrate all relevant customer data sources into one platform.
• Step 3: Our Machine Learning/Artificial Intelligence platform will equip you with a suite of easy-to-use predictive pipelines and data extensions that allow your organization to build, understand, and predict workforce behaviors.

If you would like further information on this study or to learn more about One Model, please reach out to me at: Joe Grohovsky | joe.grohovsky@onemodel.co

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