Investing in human resources (HR) analytics is as essential for business performance as it is for employee performance. According to Gartner, 70% of organizations are increasing investments in talent analytics, but only 21% of HR leaders believe their organizations are effective at using talent data to inform business decisions.1  In fact, only 12% feel like they’re getting results.2

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70%

of organization are increasing investment in talent analytics

21%

of HR leaders believe their organizations are effective at using talent data to inform business decisions

 

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HR teams often rush to adopt new technology solutions, such as human capital management (HCM) systems, but they’re doing so without a clear strategy for what objectives they want to achieve – and what data they need to get there.

For example, a consumer-packaged goods company may want to expand into digital offerings and services, yet it has no strategy for how it will build the skills and find the talent to do so. HR leaders would need data—from within their company and also externally from the broader industry—to identify skills gaps in their workforce and understand the types of skills they would need to develop in order to become a leader in digital products and services. But many leaders are struggling because they don’t know where to start. They either don’t know where they have gaps in workforce data and need to collect more, or they aren’t able to access and process the data they do have. And many organizations today struggle with both of these issues. 

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Understanding where to start 

To make intelligent, data-driven decisions about their workforce, organizations must start by creating an employee data strategy that aligns with the business strategy. This allows the business to look at which talent efforts it wants to focus on (e.g., recruiting, upskilling, engagement, productivity, retention). Generating insights from those areas can then inform decision making across every aspect of their talent organization.  

Once you align your business and talent objectives, you can work back from there to identify what data should be collected, how frequently it should be updated, where it should be stored and who should have access. For instance, if an organization wants to predict employee attrition, it needs to look at both leading and lagging indicators at the employee level from the past 24 months (e.g., patterns around employee behavior, sentiment, leave, training, performance, feedback and even supervisor data). This allows the business to use downstream analytics to identify the key markers that will impact attrition.

Key milestones on the path to implementation:  

Build the foundation  

Before jumping to design an employee data strategy or adopting a new technology, HR leaders must understand their workforce dynamics and organizational goals. You can do this by using AI- or survey-based assessments to capture skill preferences across your workforce, or even measure behaviors, practices, and mindsets to better understand your workforce culture. By anticipating business needs and future challenges to solve, HR leaders can begin to determine what types of data they need to capture months in advance, and then they can think more holistically about data collection, arrangement, storage and retrieval methods.  

Generate insights  

The quality of data insights also depends on the data maturity of the organization. But even companies with a lesser degree of data maturity can create actionable insights by choosing where to focus with their talent organization. For instance, companies with low data maturity could follow a lean data model that focuses on one specific talent objective, evaluate how existing data can provide insights, use existing HCM systems and eventually learn from the process to evolve it into a comprehensive long-term strategy that supports multiple business and talent objectives.  

Prove value 

The C-suite must understand the value of people analytics before they support 
key investments in the technology. Therefore, HR leaders must clearly demonstrate
and communicate the far-reaching impact through tangible metrics and business outcomes.
For example, showing how investments in AI-powered skilling capabilities can drive 30%
workforce cost savings and a 17% productivity increase. Doing so provides measurable 
evidence of how people analytics are helping achieve broader business goals and initiatives,
such as overall cost savings and growth.

Manage data ethically  

Data collection is essential, but the General Data Protection Regulation (GDPR) and other
local laws have impact on the types of employee data that can or cannot be collected.
For instance, personally identifiable information should not be collected. But if information
such as location is essential to gathering insights, there are ways to work around that, such as
coding or randomizing certain geographic areas to avoid using identifiable markers. Businesses
need leaders who understand the data governance laws of each country in which the organization operates. Proper governance also requires testing data for indirect markers or outliers that may skew results. 

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The rewards 

Building data maturity and scaling a business-wide employee data strategy will take time, but this approach will deliver new ways of working for your people and your organization.  

From spotty to strategic data collection  

When workforce data collection is bound by the intent of your broader business goals, data is better utilized and insights generated are more complete. Let’s say the business goal is to improve employee retention. The standard data packages that HCM systems offer can only provide so much. The business may want to cast a broader net to collect data from sites such as Glassdoor or Indeed that offer deeper insight into employee sentiment and feedback.   

From fragmented data silos to a single source of truth 

By having a strategy that connects all data sources to one common cloud-based foundation, it can paint a more holistic picture about not only a single employee, but also your entire talent organization. These linkages are critical. For example, when you marry data around employee productivity, engagement and unplanned leave patterns with organizational safety and alert trends in the construction industry, you may be able to see early indicators of employee burnout or non-ergonomic work environments in real time. Armed with these complete insights, leaders can then make more proactive, informed decisions about roles, rewards, safety and more.  

From unregulated to responsible data practices 

Establishing a clear approach to data design enables organizations to operate responsibly and ethically with their employees’ data. It will also help to earn the trust of employees who have the assurance that their data is being used properly. Consider diversity and inclusion initiatives. Organizations can use artificial intelligence to uncover hidden patterns and identify potential areas for biases. For instance, HR leaders might identify a bias towards women after seeing patterns in lack of training or job opportunities when compare to male counterparts. The ability to properly, and responsibly, govern these processes will help create an environment where all of your people, and your business, can thrive. 

Every business has goals, but how is it using data to achieve them? A talent data strategy provides the foundation for better, data-driven decision making and it ensures that employee data is used to benefit both people and the business, overall. 

See more on Workforce Insights

Footnotes: 
[1] https://www.gartner.com/en/human-resources/insights/talent-analytics 
[2] https://www.gartner.com/en/human-resources/trends/improve-hr-data-quality 

 

Tanushree Guha

Managing Director – Applied Intelligence, Solutions.AI for Talent & Skilling Lead

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