HR leaders want to know the right metrics to track. Finding the right metrics means identifying where to apply talent analytics and then working backward to determine the metrics that will most help leaders make decisions
CHROs are racing to turn data into meaningful business insights. Though over 75 per cent of leaders still rely on their intuition and not data for key talent decisions, almost every HR executive CEB surveyed plans to increase analytics investments in the next 24 months. The best HR professionals know how to make investments achieve high analytic impact: They use data to maximize the quality of leaders’ talent decisions, inspiring them to take action. Their organizations have better talent outcomes and companies looking to invest in analytics should follow a similar path.
The Path to Analytic Impact
Leading organizations create high analytic impact through a combination of increasing analytic sophistication and improving application of talent analytics to business challenges. Both are necessary, but the order in which HR leaders pursue these capabilities also matters. Most start with analytic sophistication by investing in advanced data tools and methodologies, which is slower and more costly, and additional investments yield minimal gains. However, investments that increase the business application of talent data generate almost three times the analytic impact.
By starting with sophistication, few HR teams are able to translate employee data into insights that line leaders can act on. Barely 15 per cent of leaders have changed a business decision in the past year based on HR analytics, demonstrating the big business application gap between the focus of talent analytics and line leaders’ challenges.
What the Best Companies Do
Progressive HR executives are making three types of investments to accelerate analytic impact. These are the three Cs of business application:
- Criticality: Prioritise analytics based on critical business questions
- Capability: Build capability to apply business judgment to data
- Credibility: For credibility, drive end-user ownership of talent analytics
Focus on Criticality, Capability, and Credibility
1. Prioritise Analytics Based on Critical Business Questions
HR leaders want to know the right metrics to track. Unfortunately, there is no shortcut or a predetermined set of metrics. Finding the right metrics means identifying where to apply talent analytics and then working backward to determine the metrics that will most help leaders make decisions.
Although this approach boosts analytic impact more than tracking metrics effectively, HR does not commonly use it. Less than 20 per cent of leaders believe HR analytics focus on the right business issues. So HR teams need to engage line partners to understand the most critical talent-related business challenges they face and target for analytic support.
Gap Inc. aligns analytics support to key business questions, not available talent data. In 2011, the company made targeted investments in its workforce analytics site that resulted in a 78 per cent increase in site usage in 2012. Gap achieved these impressive results because its team ensured that every investment was aligned to critical business needs and provided a substantial return on investment.
The analytics team uses the collective responses from leaders across the organisation to draw clear boundaries around the scope of its work. Local HR operations teams manage priorities without scalable enterprise impact. Then the team identifies investments for its three-year roadmap by asking, “Do we have the data we need?” and “Are we able to use the data once we have it?”
2. Build the Capability to Apply Business Judgment to Data
The right metrics are only a start. Heads of HR need the right people. So they look to hire data scientists to improve HR’s analytic capabilities. But scarce headcount investments are better spent on business judgment skills first. This investment can nearly double the analytic impact and help staff apply business judgment to data and challenge assumptions, similar to what Telefonica does.
Telefonica changed the mission of talent analytics staff to build its business judgment. The company charges its talent analytics team to inspire, influence and shape business decisions. During the hiring process, analytics candidates must demonstrate strong business judgment skills in two simulated, on-the-job tasks. One uses HR analytics to support business growth, while the other simulates communicating insight to the company CEO.
Once hired, analysts refine these skills through networking internally, sharing best practices and setting clear expectations with line customers. After each project, stakeholders assess analytics support in four dimensions: Actionability, engagement, usage and impact. This feedback helps analysts improve and build their credibility with the line.
3. For Credibility, Drive End-User Ownership of Talent Analytics
Most leaders do not trust data from HR, but they should. The reason only one in five leaders finds HR analytics credible is two-fold: The connections well established in financial data are less obvious in HR data and HR data tends to be less standardised than other data, such as financial results, exposing HR input and results to more interpretation.
HR teams can overcome these challenges by working with managers to interpret talent data and translate the implications of analytics into agreed-on solutions. The best HR professionals avoid giving leaders direct answers to complex talent decisions. Managers need to see the logic behind analytics, and an overly prescriptive approach can divert attention from the challenge at hand and toward defending HR’s analytical rigour and data quality.
Seagate changed its analytics delivery strategy to focus on maximising end-user ownership of talent data. The company’s HR team recognised that its existing strategy failed to synthesise data in a consumable format, clarify decision implications, or support the individuals responsible for taking action. Now, Seagate’s HR team presents talent analytics using filtered data visualisations, leader-driven decision scenarios and implementation support to spur the organisation to action.
Working with a line leader on a complex talent decision, an HRBP will use simple visualisations of talent data from the analytics team to stimulate discussion and diagnosis of a talent challenge. The HRBP filters the most important data for these visualisations for business leaders’ need to diagnose challenges.
At a time when most HR budgets are flat or falling, CHROs must be confident that their analytics investments lead to maximum analytic impact. This approach means investing more in improving business application of talent data and less in analytic sophistication. Use these questions to begin prioritising investments based on the three Cs of business application (Criticality, Capability, and Credibility) to close the business application gap at your organisation:
- Do leaders find talent analytics relevant to their decisions?
- Can my HR team effectively apply judgment to business data?
- Do leaders find HR data credible and easy to consume?
How CHROs can personally drive analytic impact
Heads of HR use talent analytics to communicate the value of HR and advocate on behalf of the function. However, they often delegate much of the strategy setting and operations. Here are proven ways to become more personally involved in building the entire function’s analytic capabilities:
- Set a clear vision and objectives for analytics staff based on business outcomes: Focus analytics staff on providing tangible impact on the business's actions and decisions to boost analytic impact
- Hold HR staff accountable for rigorous data and measurements: Establish accountability for all HR staff, especially HRBPs, to become more effective at analysing data and using this skill in daily HR activities.
- Connect your analytics staff with key contacts: Identify and, more importantly, facilitate relationships between analytics staff and stakeholders across the business.
(Matthew Dudek, Associate Director at CEB, also contributed to this article)