Ever get in your car and suddenly Google Maps announces the ETA to your destination, except you never input a destination in the first place? That sort of predictive capability is a paragon of client-side analytics, and a convenient analogy for any data-driven effort within a company. But how often do we apply this methodology to our own employees?

From an HR standpoint, we traditionally value personal relationships and risk management as general best practices. But, since every company represents a unique organic environment, we should consider dropping the blanket problem-solving for something a little more idiosyncratic. The better we understand an individual employee’s behavioral patterns within the context of the employee pool at-large, the more readily we can diagnose problems in their latency, as well as leverage strengths to grow company-wide proficiency.

Call it people analytics, talent analytics, or HR analytics—the point is that the age of leading with your gut is now being replaced by much more informed decision-making. First, you have to be able to harness data regarding your employees’ engagement levels and combined workforce skills beside your company’s overall retention rates and leadership capabilities. Next, you have to be able to find the story (or stories) within it. Lastly, you have to put your newfound knowledge into action.

“Demystifying People Analytics”

In a 4-part article on Human Resources Network’s (HRN) blog, David Green provides an excellent roadmap for implementing people analytics within your company. Green addresses how to build a team into your infrastructure, what skills this team needs to embody, how they approach storytelling with their data, and big name examples of how people analytics has transformed a company’s approach to the employee lifecycle.

How does people analytics fit into your current infrastructure?

Much like a previous article (http://tekmountain.com/is-your-hr-so-two-thousand-and-late/) where we discussed HR model overhauls, Green’s first article explores a spectrum into which your people analytics team may fit—from centralized to democratized to outsourced:

Center of Excellence

  • a team unit within HR that doesn’t report directly to the Chief Human Resources Officer (CHRO)
  • similar structure to that of CoEs for talent acquisition, talent management, etc.
  • risks lack of influence outside of HR because of not reporting to CHRO, who would be main advocate and influencer across the company

Team within HR

  • reports directly to CHRO
  • CHRO helps to define key challenges to be addressed
  • actionable data stories are more readily communicated throughout the company

Team outside of HR

  • Green advises against sitting team within finance or IT; suggests sitting team under COO, while still receiving some guidance from CHRO
  • allows team to have access to both HR and non-HR data
  • risks upheaval of company’s HR model without commensurate input from HR itself

Enterprise-wide

  • assign multiple teams, each focusing on a different aspect of people analytics
  • may very well be the “natural evolution” of any people analytics effort within a company, regardless of its original structuring
  • risks lacking cohesion and breadth of insight with the effort being spread across multiple departments

Outsourcing/Partnering

  • allows for faster info-gathering and analysis, as well as pursuing a larger number of initiatives can help the process reach its maturest level more quickly

What are the must-have skills for your people analytics team?

According to Green’s second article, the cumulative abilities of your team must be wide-reaching, but the more diversity of skills for each team member, the better:

  • “Strong leadership”
  • “Business knowledge and acumen”
  • “HR domain expertise”
  • “Consulting skills”
  • “Statistical analysis”
  • “Data science and management”
  • “Programming and database design”
  • “Visualization”
  • “Storytelling”
  • “Change management”

How does your team create the most actionable story from your data?

With three questions, Green’s third article gives a very straightforward outline to creating your data storytelling:

  • What do you want your audience to know?
    • Don’t strand your audience in all your complicated methodology. Define the business problem, discuss your insights, then provide possible plans of action.
  • How do you want your audience to feel?
    • Emotion has been scientifically linked to memory-making. Sometimes, innovation in itself isn’t the most compelling topic. But the social implications of new technologies affect everybody. 
  • What do you want your audience to do?
    • This part may seem a little more esoteric. Because you know your audience the best, you have to ask yourself the most effective form of communication to influence their behavior.

What are some great examples of people analytics projects?

It’s always good to ground all the abstract talk with some real-world proof. Green’s fourth article provides examples of major companies that have addressed different stations on the employee lifecycle:

  • Cisco – Workforce planning / personalized career development
    • Talent Cloud – in-house software that allows employees to create their own personalized work profiles, including their skills and competencies, as well as whether or not these abilities are being most utilized by their current roles. This helps managers to identify new opportunities for each employee.
  • LinkedIn – Talent acquisition / Scaling the organization
    • At one point, LinkedIn was growing so quickly that its talent acquisition team couldn’t give any accurate forecasts about hiring rates and required resources. After an analytics team was brought in, were “able to predicted hires within 5% of actuals.”
  • Integrated Service Solutions (ISS) – Engagement linked to business performance
    • ISS sought to improve customer satisfaction by first raising employee engagement rates. These key drivers were identified: “motivation, service quality, knowledge and responsiveness of service staff.” The company then overhauled its training programs for both employees and supervisors.
  • IBM and Nielsen – Retention of critical talent
    • IBM’s Proactive Retention Program first predicts the employees most likely to leave, then identifies the best course of action to reduce this risk. Nielsen’s people analytics team “calculated that a single percentage point of attrition represented a $5 million-dollar cost.” The company then boosted management efforts where needed.
  • Shell – Compliance / Cybersecurity
    • When it came to employees causing phishing incidents or virus downloads, Shell identified a link between tenure, skill, and job roles. The company then created a targeted cybersecurity training video to these particular demographics.

People analytics is all about implementation

As an entrepreneurial and innovation center, we at tekMountain understand that all the online advice in the world still won’t get the job done. Any company needs boots on the ground, working in tandem with that company’s leaders to create an efficient and lasting plan of action. As HRtech is the bread-and-butter of our parent company CastleBranch, we’ve got the experience and insight in-house to help implement innovative concepts like people analytics into companies of any size.

We’ve also recently been exploring how certain aspects of people analytics can be applied to an industry-wide workforce. As medtech is one of our three core innovation focuses, we’re very interested in how to help curb the current nationwide nursing shortage, fueled in part by a 33% turnover rate of nurses in the first three years of their career. This problem is by no means simplistic, but strides need to be made in the placement process of nursing graduates. Via complex, personalized e-portfolios, nursing students can begin to build real-world career expectations throughout their education. The more we’re able to customize the individual nursing experience from schooling all the way through the entire career lifecycle, the more likely we’ll be able to lower the high turnover rate, while also better meeting geographic demands in this profession.

 

Data is just data, until you do something with it.

 

This blog was produced by the tekMountain Team of Sean AhlumAmanda SipesBill DiNome, and with lead writer Zach Cioffi.

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