HR Is Shifting To A Data-Driven, High Business Impact Approach, So Don’t Be Left Behind

Why You Need To Shift To Data-driven HR Model

The largest shift in the history of HR is underway and you simply cannot afford to be left behind. This shift is from a historical reliance on intuitive or “off the top of my head” decision-making among HR professionals and supervisors/managers to a more businesslike data-driven approach. Let me be clear, I’m not talking about the mere use of “metrics”, but instead, a comprehensive approach where data is continually used to dramatically improve every aspect of recruiting, retention, development, innovation and workforce productivity. The shift will mean that HR leaders will no longer be able to utter the now commonly used phrases “off the top of my head, “I believe” and “in my experience” and instead, these phrases will be replaced with more credible businesslike responses like “I know”, “the data proves” and “this trendline shows precisely what we can expect”. So if you are an experienced HR professional that frequently answers with “I think”, be aware that you may soon be replaced with an algorithm!
This soon to be dominant approach can also be called “data-supported”, “data-based” or “evidence-based” HR decision-making, because data or evidence support are the foundation of all major HR decisions that result in direct business impacts. The goal is not to slightly change HR, but instead to strategically and measurably improve the people management results that are derived from the largest corporate variable expenditure (labor costs). Under the new approach, HR is not merely expected to be “aligned with corporate goals” but instead, to directly and quantitatively impact corporate goals. Under this new methodology, HR leaders will be expected to continually improve the dollar value of innovation and the productivity of the workforce until your firm leads the industry in a side-by-side comparison.  Moving beyond “a business partner” and into a “business leader” role absolutely requires everyone in HR to begin using “the language of business” (numbers and dollars) and to show how great people management practices measurably increase corporate revenue.

HR Gets No Vote On The Matter, You Must Become Forward-Looking


Unfortunately, HR leaders don’t get “a vote” on this shift, because it is being driven by corporate executives and a faster moving more competitive business world. This shift to become more systematic and businesslike follows the path that has already been successfully utilized by other overhead functions like supply chain, finance and CRM. The biggest shift under the data-driven approach will be a move away from “backward looking” metrics and toward “forward-looking data” and predictive analytics. A focus on the future will mean that HR’s current reliance on 100% historical metrics will soon end. To be replaced with predictive analytics which will forecast trends and alert us about upcoming talent problems and opportunities, while there is time to do something about them.

HR Currently Ranks At The Bottom in Analytics And Data-driven Decision-Making

We don’t have a positive image when it comes to metrics. When you survey the most frequent users of analytics and metrics in the corporate world, not surprisingly you find that HR ranks at the very bottom. Compared to Finance, which is ranked #1, HR compares poorly, with only half of its sub-functions being classified as “advanced users” and “three times more HR sub-functions are classified as non-users”. HR shouldn’t be surprised to learn that “the executive team” came in #2 as strong users because they (along with finance) are at the forefront of demanding more data-driven decision-making and analytics from HR. The remaining business functions, operations, R&D, marketing and sales all had a higher percentage of advanced metrics users than HR (source: AMA/i4cp study). The new reality is that corporate executives that will no longer tolerate HR as the last remaining function that is not data-driven.

An Example To Illustrate The Difference With Data-driven Decision-Making

Perhaps an example will illustrate the dramatic difference between the current approach and the emerging data-driven approach. Employee retention is traditionally one of the “softest” areas within talent management. HR and retention leaders are generally satisfied with only reporting “the overall turnover percentage during last year”. However under a data-driven approach, more revealing information will be provided. To start with, for retention purposes, the highest impact jobs and employees will be identified using data. Next the data will be individualized to reveal “why” each of these high-impact individuals stay, what might cause them to leave and what actions would cause them to stay and remain productive. Using a data-driven “heat map”, high-impact teams and individuals that were “at risk” would be identified using a predictive analytic and then assigned a “flight risk” percentage indicating the level of potential retention problems. And finally, an algorithm would be developed to quantify the potential dollar loss if the individual left and a supplemental cost would be added if they went to a competitor.
Individual managers would be held financially accountable for the turnover costs of any “regrettable” and preventable turnover within their team. HR would be assigned the role of identifying the most effective proactive “actions or levers” for preventing key turnover. Each month, HR would report the dollar cost of key regrettable/preventable turnover, the success rate of accurately identifying those with a high flight risk and the most effective tools for reducing or preventing turnover among those that are considering leaving. If the scenario described above appears to you to be “a pipe dream”, welcome to the new world of data-driven retention. Powerhouse firms like Google and Apple, firms that excel at innovation, speed, adaptiveness and exceptional stock valuations have already successfully shown the business impacts of this type of a data-driven approach. You should also be aware that once your firm’s executives find out that this approach is possible, your job may quickly be in jeopardy if you aren’t already making the shift.

Additional Examples Of Data-driven Talent Management Capabilities

Perhaps some additional examples can help you grasp the expanded capabilities of data-driven decision-making in Talent Management

Predictive hiring

Data would allow you to continually update the changing “hiring criteria” factors that accurately predict the future “on the job performance” and retention of new hires. Data will also allow you to accurately determine which selection tools (the number of interviews, interview questions, types of interviews, grades, reference check scores etc.) accurately predict or are a waste of time.

Projecting their “career trajectory”

Data will allow you to predict how long employees (and new hires) are likely to stay and how high in the organization they are likely to reach for succession purposes.

Performance management trend lines

HR will be expected to use data to demonstrate when and if performance management efforts will convert weak performers into above average performers. Algorithms will also predict when the productivity and innovation rates of individual employees are likely to begin leveling off.

The impact of workplace features

Data will be used to demonstrate which employee perks and office design features directly increase employee innovation, collaboration and productivity.

Improving learning

In a fast-changing world where the single most important competency is rapid self-directed learning, data will reveal the most effective approaches for increasing individual and corporate learning speed and effectiveness.

Compensation and benefits

Rather than relying on salary surveys or intuition, the new approach will use data to identify which types and amounts of rewards/benefits have the highest measurable impact on employee productivity, retention and external recruiting.

In the next installment of this blog I’ll give you my top 10 reasons why you need to immediately begin making the shift towards  data-driven decision-making.

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