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Why emulate HR "Best Practices" when you can customize

The HR space has traditionally been driven by Industry Best Practices. The likelihood of People Practices or Initiatives getting approved by the Management also increases manifold if one can show instances of the same being practiced by the market leaders or the competitors.

But how fruitful is such an approach? Does it always achieve the desired results? In the quest to adhere to best practices, can a more effective Initiative/Intervention be left out?

HR Best Practices

Let’s take an example to understand this. If a patient goes to a doctor with a health issue, does the doctor look at what treatment other doctors have prescribed for similar health problems? No, because every patient is different. Every patient’s background, lifestyle, medical history etc. is different. The doctor also has to take into consideration the patient’s comfort level, his/her allergies (if any) and look at which treatment options may suit the specific patient. The gist being that a treatment or medication that suited a particular patient may not necessarily suit or be as effective for another patient.

Similarly every company is different. The organization’s vision and mission, the culture, the employees’ profile, organizational capability and skillset, the strategic priorities etc. differ from company to company. Superimposed onto this is the fact that within the same company itself, different employees have different aspirations and requirements and one cannot afford to rely on a ‘one-size-fits-all’ approach to people practices.

What is the alternative approach?

The idea would be to study how different sets of employees behave and what kind of interventions and initiatives tend to work for these different sets of employees given a particular organization’s context.

For this, we need to begin by studying our employees. Every data-point being captured about employees may prove to be a useful. Be it the traditional attributes like educational background, age, which tier college an employee comes from, employees’ placement in organization structure, etc. The other important attributes to look at would be:

  • Depth and breadth of work exposure,
  • Performance levels
  • Target achievements
  • Roles undertaken
  • Past career progressions
  • Employee skillset
  • Training & development
  • Client feedback
  • Rewards and recognition

Once we analyze all these data statistically, we can find ways of categorizing or segmenting employees, thereby identifying different set of employees, such that employees in one set are intrinsically similar and ones in different segments are intrinsically different form each other.

But isn’t employee categorization already happening? –Yes, categorization at very macro level is currently being done based on demographics, performance etc. such as Hi-Potential Employees(Hi-Po’s), Gen X, Gen Y, Campus Hires, Hi-Po Women Leaders etc. But do all Hi-Po’s behave the same way or are the requirements similar for all the Gen Y population?

Data-based Categorization can go many levels deeper to identify employee segments within each macro level employee categorization. For eg: with-in Hi-Po’s or Gen Y’s, we would be able to identify distinct employee groups/segments, with employees within each group sharing specific characteristics and aspirations.

Once distinct employee groups are identified, we can then go about studying what kind of interventions and initiatives tend to produce desired results for each of the employee groups. If we are able to find clear patterns of success or failure of certain initiatives with respect to a certain employees group, would it not give us a good fact based evidence for what may work and what would not? To get our confidence levels up, we would need to test and validate the results.

Once we have identified what works for specific employee groups, our work would not end here. We would need to check the effectiveness of our solution on an ongoing basis to ensure that our analysis done earlier still holds true. A good data-based statistical solution should perform for a good amount of time, unless the organization undergoes some major change(s) which makes the current organizational context different from the one in which the solution was built.

Furthermore if we see the effectiveness and impact of the solution dwindling over time, then it would be time for us to remodel, thereby accommodating the evolution undergone by the organization and its employees.

HR Analytics

So are Industry Practices redundant?

No not really. I think knowledge of what is happening around us will never become redundant. It is like constantly updating our general awareness. Novel initiatives undertaken by others may help us in idea generation and open up our thought process. It can guide us towards newer hypothesis that can be tested to see if they or any of their modified versions would work in our organization. And if nothing else, it may just drive us to do better in our own organization. But we should not try and adopt a people practice just because it has worked well for another organization.

I am trying to explore moving away from the tradition of studying others to studying oneself. And the key would be to “Look within, instead of searching outside”, which is becoming more and more achievable now, given the availability of deeper and richer employee data and the growing analytical capabilities.

I would love to hear your views on this.

 

This blog is written by Arasi Nagappan, Analytics Consultant at BRIDGEi2i

About BRIDGEi2i: BRIDGEi2i provides Business Analytics Solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. Our analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems and make data driven decisions across pan-enterprise processes to create sustainable business impact. To know more visit www.bridgei2i.com

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The views and opinions expressed in this article are those of the author and do not necessarily reflect the official position or viewpoint of BRIDGEi2i.

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