While there is much buzz about the use of data and analytics in HR, most organisations have yet to establish predictive workforce analytics programs or figure out how such efforts could benefit their businesses.

A recent report, which looked at the best practices of five companies currently using predictive workforce analytics to enhance talent acquisition and management, found that there are generally six hallmarks of successful predictive workforce analytics.

The companies, Cargill, Gap, IBM, Johnson Controls and SAS, use predictive workforce analytics for such efforts as selecting job candidates who have the most potential to develop into high performers; identifying when critical employees are at risk of leaving the organisation; and using social media to get a real-time understanding of employee engagement.

The report, which was conducted by APQC in conjunction with Talent Analytics, Corp, said the six best practices associated with predictive workforce analytics are:

1. Purpose: Articulate a vision for why your organisation is adopting predictive workforce analytics as a business approach.

Early-adopter organisations did not speak about making a large business case before getting started with predictive workforce analytics.

However, each did talk about having clearly articulated a vision for why their organisation was adopting predictive workforce analytics as a business approach.

Essentially, each had answered the broad, long-range question: Why predictive workforce analytics at our organisation? Upon these broad, long-range visions, the early-adopter organisations craft short-term predictive workforce analytics plans.

Typically, these are yearly plans to conduct discrete predictive workforce analytics projects that have a strong probability of success.

2. Resources: Secure the specific resources necessary to carry out your organisation’s short-term predictive workforce analytics plan.

The early-adopter organisations did not mention making large financial outlays to get started with predictive workforce analytics. For the most part, interviewees did not talk about supporting predictive workforce analytics with significant investments in technology or new staff.

“Pick small, containable projects that enable you to apply predictive analytics to specific HR issues”

However, they did underscore the importance of securing the specific resources needed to carry out their well-defined predictive workforce analytics plans. Each articulated and then located critical predictive workforce analytics skills.

Next, they assembled these skills into formal, dedicated predictive workforce analytics groups.

3. Problems: Select predictive workforce analytics projects in response to true business challenges that your organisation faces.

The early-adopter organisations do not conduct predictive workforce analytics because they see other organisations doing this work.

Instead, their predictive workforce analytics projects arise out of true business problems.

Moreover, their analytics projects are only predictive when predictive is the most appropriate method for answering the specific business problem at hand.

4. Data: Don’t wait for perfect data before getting started with predictive workforce analytics.

Assemble and validate data according to the requirements of your short-term predictive workforce analytics plan.

The early-adopter organisations have a common long-term goal to establish clean, organisationally consistent, and centrally-stored workforce data.

Some of the early-adopter organisations started work on this goal years back and have made significant progress. Others are still in the beginning stages of data integration.

One commonality among them all is the decision not to wait for perfect data before getting started with predictive workforce analytics. Instead, the early-adopter organisations assemble and validate workforce data on a per-project basis.

“It’s no longer a question of ‘if’ predictive analytics will be utilised for workforce challenges, the question is, when”

5. Education: Educate end users about the basics of predictive workforce analytics.

The early-adopter organisations devote significant time to educating end-users about predictive workforce analytics.

During projects, they present incremental results and solicit user feedback. Post project, they extensively socialise findings sharing consumable amounts of information, often in the form of a story.

At all times, they provide varying levels of predictive workforce analytics education to HR and other areas their organisations.

6. Outcomes: Measure and share the outcomes of your organisation’s workforce analytics efforts.

All of the early-adopter organisations measure the results of their predictive workforce analytics projects. The stories and visuals they create with data promote action which they closely track as a key outcome of analytics success.

Any positive outcomes that arise, they deliberately publicise in order to build the business case for continued investment in predictive workforce analytics.

“A lot of companies haven’t gotten started with predictive workforce analytics because it seems like such a daunting task,” said Elissa Tucker, human capital management research program manager for APQC.

“What we found in this research is that there is no reason to wait until everything is perfectly aligned or your data is complete and fully scrubbed.

“Rather, pick small, containable projects that enable you to apply predictive analytics to specific HR issues.”

Tucker also said the foundational practices for success in predictive workforce analytics engagements can provide similar benefits for other analytics projects.

Organisations are embracing predictive analytics as an approach to solve complex workforce challenges, according to Tucker, who said business ROI will continue to add to the momentum.

“It’s no longer a question of ‘if’ predictive analytics will be utilised for workforce challenges, the question is, when,” she said.

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