By Steve Trautman September 12, 2017

Don't Settle for Inadequate Talent Risk Data

6 Characteristics of Highly Effective People Metrics

Posted by Steve Trautman on Sep 12, 2017 1:00:00 PM

When it comes to managing talent risk, your company likely has one or more of the following types of information: an org chart, head count, retirement risks, attrition rates, and employee satisfaction or engagement scores. But if you rely heavily on technical staff with unique skills and knowledge, you will want additional information to assess and mitigate your risk. The data should have the following characteristics:

 

Specificity

  • Zoom in to the individual contributor. You should know the names of the people who are at risk and why.
  • Agree on labels for their expertise. Everyone needs to be talking about the same risks.
  • Clarify how many workers are involved. You need to be able to talk about capacity as part of your risk profile.

Relativity

  • Show the sphere of influence for each chosen expert. Are they setting the technical standard for their team, their site, their region, or the world?
  • Show how more than one expert around the world may be setting different standards and how they relate to each other. Otherwise, there is no hope for consistency.
  • Align on language to describe the work so that role clarity, redundancies and inefficiencies can be unearthed and mitigated.

Chunk-ability

  • Deconstruct the blocks of knowledge into technical domains or silos that take a month to a year to learn so the expert can be clearly chosen and the silos can be discussed from the executive level to the front lines.
  • List the skills and knowledge in consistent blocks of work (such as tasks that are teachable in about one hour) so they can be easily measured, staffed, and scheduled.
  • Break the skills and tasks into steps and answers to questions that get at the wisdom, tacit knowledge, and secret sauce of the chosen experts.

Accessibility

  • Gather the talent risk data in hours or days for a given expertise. It can't take too long or be too difficult for busy technical experts to participate.
  • Scale the solution from 1 person to 1,000 people or more.
  • Update the data regularly by spreading the workload between managers, experts, and others.
  • Count the number of technical domains, experts, and capacity of workers per domain.
  • Tally the cost of a mistake in each silo so that risk and priority are based on real impact.
  • Test the speed with which employees can learn a new skill or knowledge set and be prepared to go to work.
  • Monitor risk reduction over time.

Predictability

  • Keep the data current so it accounts for changes in team members and knowledge domains in relation to business goals.
  • Standardize units of measure so that comparisons can be made between teams and predictions can be set for risk reduction timetables.

Usability

  • Align with leaders at all levels around risk and priority for mitigating the risks.
  • Use the data to inform every business decision and execution plan
  • Conduct scenario planning using the data before a big reorganization.
  • Level workloads and assign resources to ensure a ready workforce three to thirty-six months out.

Topics: talent risk data

Steve Trautman

Steve Trautman

Steve Trautman is corporate America’s leading talent risk management and knowledge transfer expert. With two decades of application inside blue chips and Fortune 1000s, his pioneering work in the field of talent risk management and related knowledge transfer tools are now the nationally-recognized gold standard. His clients have included Boeing, Costco, Goodyear, Aetna, Farmers Life Insurance, Bank of America, Microsoft, and Qualcomm, among others.

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