The connection that people analytics data with the prosperity of the organization is crucial and yet it needs to have further development. Consulting firms in the U.S. and Europe lament the slow progress.
According to HBR Analytics among top executives, 15% said they use “predictive analytics based on HR data and data from other sources within or outside the organization,” while 48% predicted they would be doing so in two years. Harvard Business Review Analytics Study.
The reality seems less impressive, as a global IBM survey of more than 1,700 CEOs found that 71% identified human capital as a key source of competitive advantage, yet a global study by Tata Consultancy Services showed that only 5% of big-data investments were in human resources.
We will go through the best practices, mindset and key steps needed when it comes to leveraging the power of People Analytics into the real-world of business.
According to the HBR Study in People Analytics, when it comes to presenting Human Capital Analytics to business partners the aspiring data scientists is advised to follow the LAMP framework.
What are the vital connections? That is the question that should be addressed primarily. The logic element of any measurement system provides the "story" behind the connections between the numbers and the effects and outcomes
In the field of human resources, there are many logical frameworks:
All are useful, but they are not sufficient to connect decisions about investments in HR programs to strategic outcomes. In contrast, some authors have proposed a "service-value-profit" framework for the customer-facing process.
This framework calls attention to the connections between HR and management practices, which, in turn, affect employee attitudes, engagement, and turnover; which, in turn, affect the experiences of customers. This, in turn, affects customer-buying behavior, which, in turn, affects sales, which, in turn, affects profits.
Analytics is about drawing the right conclusions from data. It includes statistics and research design, and then goes beyond them to include skill in identifying and articulating key issues.
Even a very rigorous logic with good measures can flounder if the analysis is incorrect. For example, some theories suggest that employees with positive attitudes convey those attitudes to customers who, in turn, have more positive experiences and purchase more.
Suppose that a super market chain has data suggesting that customer attitudes and purchases are higher in locations with better employee attitudes. Does that mean that improving employee attitudes will improve customer attitudes? Many organizations have invested significant resources in programs to improve frontline-employee attitudes based precisely on this sort of evidence of association (correlation).
The measures part of the LAMP model has received the greatest attention in HR.Much time and attention is paid to enhancing the quality of HR measures, based on criteria such as timeliness, completeness, reliability, and consistency. These are certainly important standards, but lacking a con, they can be pursued well beyond their optimum levels or they can be applied to areas where they have little consequence.
Create accurate and verified numbers and indices calculated from data systems to serve as input to the analytics, to avoid having “garbage in” compromise even with appropriate and sophisticated analysis.
Today's turnover-reporting systems can calculate turnover rates for virtually any employee group and business unit. Armed with such systems, managers "slice and dice" the data in a wide variety of ways (ethnicity, skills, performance, and so on), each manager pursuing his or her own pet theory about turnover and why it matters. Are those theories any good? If not, better measures won't help. That's why the logic element of the LAMP model must support good measurement.
The final element of the LAMP framework process. Measurement affects decisions and behaviors, and those occur within a complex web of social structures, knowledge frameworks, and organizational cultural norms. Therefore, effective measurement systems must fit within a change-management process that reflects the principles of learning and knowledge transfer. HR measures and the logic that supports them are part of an influence process.
Use the right communication channels, timing, and techniques to motivate decision makers to act on data insights, for example:
It is important for HR analytics to move away from the “bubble world” and move closer to the real world. The right time, right con, and right value potential must be realized to enable businesses to truly benefit from HR analytics.
We can then expect businesses to pay more attention and dedicate more resources to the field of talent analytics and set up a logic-based accurate future for HR.