Talent Analytics- Big & Growing Bigger  http://www.zenithtalent.com/recruiting-and-staffing-blog/talent-analytics-big-growing-bigger @zenithtalentData: big and growing

Call the 21st century what you will — the connected age, the digital age, the information age — one inescapable reality is that it’s the data age. Nearly all aspects of our private, social and business lives are now somehow tied to data and the value we extract from this information. Vast, complex sets of data and the evolving science of predictive analytics allow us to find new correlations to spot business trends, prevent diseases, combat crime and even hire people. Mining and interpreting data have become instrumental practices in media, marketing, advertising and journalism. In fact, Google recently launched a set of data-driven news tools specifically for journalists.

Using Google’s new journalism resources, we were able to whittle down the main topics in staffing industry discussions and reporting. Interestingly, we discovered through big data that big data ranks among the top three trends. The other two involve employer branding and workplace culture. And there’s a strong correlation there — it’s virtually impossible to attend a recruitment strategies seminar or read the industry news without encountering these topics at the forefront of hiring issues. And when we analyze big data objectively — without trying to prove a theory, support a conclusion or justify an approach — we learn fascinating things. Look, for instance, at the surge in people analytics (blue line) over the past few years compared to employment culture (yellow line) and brand (red line).

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Globally, we also learn that employment culture would seem to be a bigger deal in Canada than in the United States or Britain.

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The top four countries preoccupied with people analytics as a recruitment strategy are the United Kingdom, the United States, Canada and India.

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And the importance of employment brand in staffing? The United Kingdom and Germany top that list.

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At the core of big data is a push to arrive at more confident and accurate decisions. Even based on the cursory analysis above, a global recruiter would instantly have fresh insight to the most effective messaging to use when courting talent: talk about stellar employment brands in Germany or amazing business cultures in Canada.

However, people analytics remain relatively new processes in recruiting. Obstacles persist in gathering data, interpreting the data and using the data. And as hiring managers and HR leaders struggle to grasp the complexities of people analytics, they frequently fall back to relying on “gut instincts.” When that happens, interviewers end up gravitating toward candidates who express shared traits and interests. Yet, the problem is larger than that. As Michael Skapinker observed in the Financial Times, “It is not just our biases that get in the way but that past performance cannot predict results.”

Old habits die hard

The Financial Times reported in June that “top professional services firms” in London, Scotland and the United States had been practicing “widespread discrimination” in their hiring initiatives, rejecting candidates who “did not belong to a select social and educational group.” The firms investigated primarily occupied the legal and accounting fields. Yet even the organizations that took measures to offer internships and mentoring to students from less privileged backgrounds ultimately failed to hire a substantial number of those candidates.

“The problem was that, faced with a final set of potential recruits, too many interviewers opted for people with whom they felt comfortable,” Skapinker concluded.

  • One hiring manager admitted succumbing to what he called the roommate test: “Are they someone you want to share a room with?”
  • Another revealed that he took a particular interest in candidates who said they enjoyed traveling — something that requires money, the U.K. Social Mobility Commission pointed out.
  • A U.S. attorney, in explaining his final hiring determinations, simply said, “Number one people go to number one schools.”
  • And even those who agreed in a higher statistical probability of locating exceptional talent from less renowned schools confessed that sifting through the mountains of data was too difficult and time consuming.

“But, even after dredging familiar waters,” the Financial Times discovered, “many of the firms did not seem happy with what they had found. The UK commission reported that many of the recruiters said the people they thought would be the best lawyers and accountants often did not end up having very successful careers.”

The overwhelming reality? “It is not just our biases, unconscious or not, that get in the way. It is that people who succeed in one job do not necessarily thrive in the next one.”

What happens when algorithms, not instinct, guide decisions?

The Financial Times used the example of Xerox Services’ partnership with Evolv to recruit talent for its call centers. Instead of following habit and trying to get at the heart of what draws top talent to organizations, Evolv examined the factors that made people quit. In understanding the negatives, Evolv wondered, could it then filter out the positives and reduce staff turnover? It did. In fact, attrition at some Xerox call enters dropped by 20 percent, validating the idea that experience and past performance aren’t necessarily strong gauges to predict success.

The data-based process “actually opens up doors for people who would never have gotten to interview based on their CV,” Xerox told the Financial Times. And Google’s Laszlo Bock believes the same principles can translate to superior recruitment for senior positions, as well. As Bock recently wrote, it’s accomplished “by looking at large sets of data and inferring relationships, similarities, and predictors of success and failure.”

So instead of hunting for candidates at a certain experience level in a specific skill set, such as accounting or engineering, you let data inform your search by identifying talent with the “ability to learn quantitative methods combined with a zeal for catching and correcting the smallest of errors, persuade with data, and thrive in social settings.”

It sounds fantastic, and it does work. MSPs and VMS providers jumped on the people analytics bandwagon some time ago. The challenge is that many hiring managers, HR departments and recruiters have yet to develop and launch a comprehensive, strategic people analytics program. Fortunately, thought leaders such as Alexis Fink, director of talent intelligence and analytic at Intel, are showing staffing professionals how to transform their organizations through the best practices of talent analytics. And for MSPs struggling to convince their clients of the big data benefits, some of these strategies may help pave the way.

People analytics 101

Before embarking down the path to utilizing big data, Fink reminds us that we need to prepare for a mindset shift. This is first step MSPs should consider when approaching clients. People analytics are not reactive — when used properly, they provide illumination rather than support. That means we should approach big data with curiosity and impartiality — not as a vehicle to prove something we already believe, or that others believe. In the end, the results of a careful analysis might not be those for which we had hoped, yet they will point us in the best direction.

Know the objectives and what could be different or changed because of the results.

  • What are we trying to achieve?
  • What information do we ideally need to make a decisive choice or course correct our current direction?
  • What is the real business problem we’re trying to tackle?

By identifying the answers to these questions, we can work backward to uncover the data our clients need.

Build thoughtful samples

  • Drive thinking that’s big, broad and beyond single a department or division. Consider how the data affect the organization and its talent as a whole.
  • Defend against confirmation biases that can arise from like perspectives or people who think the way we do. Approach the analysis as one of the researchers on “MythBusters.” Attempt to disprove accepted norms. Be receptive to risks, failures and unexpected outcomes — all of these situations are critical learning experiences that will improve the process.
  • Use good data: reliable, valid, clean and complete. The data should be objective, not based on a specific business group, category of talent, company division, or hiring manager.
  • Design comparisons across groups and over time.

Enlist partners early

Even the most thoughtful and expertly performed analysis can fail if stakeholders are not informed and included in the process. We should strive to bring others along with us on this journey of discovery, and solicit their input. The decision makers will be more likely to participate, review the research, understand its value and implement the recommended changes. Otherwise, the entire effort can be jeopardized. Without prior knowledge and inclusion, other stakeholders in the process may feel as though they’re being told how to do their jobs, especially if they think things are going well at the moment they’re handed a substantive report outlining all the things they need to change.

Despite best intentions, recipients in this scenario will feel blindsided. And when that happens, crucial plans languish on a shelf unimplemented and collecting dust, which amounts to wasted opportunities, squandered time and lost costs.

Build the right team

Designing the right team is imperative and should take place before any data collection or analysis occur. Although MSPs have mountains of useful data in their VMS and enterprise resource systems — not to mention in-house analytics teams — the effort must be more expansive and collaborative to succeed. The best teams, Fink asserts, include a broad swath of representatives. In an outsourced workforce program, that would incorporate professionals from the client organization, the MSP, the VMS and staffing partner firms. These subject matters experts will be required to address the Whys, the Whats and the Hows of the project.

  • Why: hiring managers, operational leaders and executives to provide the business expertise.
  • What: staffing partners, procurement leaders and HR officers to provide expertise on the talent.
  • How: Data analytics specialists from the MSP, client organization or technology provider (e.g., VMS) who understand the information, how to gather it and how to interpret it into meaningful results that decision makers can act upon.

Open minds and open eyes

The benefits delivered by people analytics are unparalleled. And while the process might seem alien and overwhelming now, in the not-too-distant future big data can open our eyes to a world of exceptional talent and new generations of innovators we didn’t see before.