How are successful HR leaders approaching the use of analytics to optimize their workforce and create real business value? Perla Sierra, the Sr. Director of Data Analytics Information Management at Assurant tells us more. What have been the 3 biggest challenges your team faced when you were investing in, driving adoption or attempting to optimize business outcomes from People Analytics?
Before embarking on a true analytical journey, the quality of the data must be assessed, consistent metrics must be agreed to, and privacy safeguards must be implemented. While data is usually never perfect, we must strive to obtain the highest possible level of data integrity and when there are data gaps, then the analytics practitioner must understand the data opportunities and take it into account in any analytical exercise.
Even though data about people has been available for a long time, the transition of people analytics from a hunch and feel to a more structured approach has been one of the biggest challenges. The challenge around using data-driven decision-making approach towards people is that it is important to compare employees and performance using an even playing field and this is very difficult to do. To do this, we must always adjust for context. Adjusting for context involves consideration of factors which may not be present in the data or if it is it may not be obvious.
As an example, when comparing performance for similar or same jobs, other factors outside of the data should be considered such as environmental factors (i.e. working conditions, manager, etc.).
Making data-driven decisions within the People Analytics space may fuel tensions because people prefer human judgment over algorithmic judgment. The truth is data-driven decision-making removes bias if the playing field is even and of course if the data is accurate. Making decisions based on data will undoubtedly increase the analytical maturity of the organization and serve to engrain a more unbiased approach to decision making while also strengthening the organization’s culture. The availability of data offers new innovations and new insights.
What would your top 2-3 most practical tips to HR leaders who want to successfully build, scale, and optimize their people analytics function? What should they prioritize?
Availability of quality data, data security, and data privacy should be at the forefront of implementing a people analytics function. Without these 3 key elements, it will be more difficult to be successful in building and scaling a successful people analytics function. Sometimes not all the data may be perfect or accurate. In this case, the organization should consider a phased approach that could leverage the data that is accurate or at least good enough to start the analytics journey rather than waiting for all the data to be accurate.
Unfortunately, a phased approach will take longer and may cost more in terms on incremental costs, however, you should consider the intangible opportunity costs that would be missed if you simply choose to not move forward until you have the entire scope of the data. This is a case of you must get comfortable with the uncomfortable and push through with “good enough” data. I would consider the best practice to create data councils and data stewards which could drive data standardization and quality and push for data governance.
What would be the 2-3 key milestones in an organization’s People Analytics Maturity journey?
Let’s start with the end in mind. The end goal is for the business to embed people analytics in business decisions. How can we best accomplish this endeavor? The data must be reliable, secure, safeguarded, consistent, however, it does not have to be perfect. The HR partner must identify people problems that impact the business that could be identified earlier in the process or even eliminated altogether ideally. The HR Partner and the Business Partners must work collaboratively to look for opportunities to derive insights and provide the actionable insights to the right audience at the right time and ultimately whenever possible provide prescriptive and predictive analytics. The HR team must work with business leaders, frontline managers, customers, vendors, and others to identify opportunities, spread awareness, form deep partnerships, and advance organizational maturity.
Some examples of how HR can increase its own analytical maturity while also increasing that of the business would be by providing the following: monitoring of employee retention, training, internal mobility, skill set assessment, correlating hiring assessment to performance metrics, identification of future resource needs.
What approach has worked for you when it comes to building a data-driven culture in your HR organization?
The most effective technique I have come across when building a data-driven culture is to create business partnerships with different internal businesses, departments, analytical practitioners, and even external sources. It is the diversity of the people and experiences that everyone brings to the table that really offers the most in terms of creative problem solving and innovation. Everyone sees an opportunity through a different lens and this integration creates strong collaborative models that benefit the organization. By adopting a culture of diversification and integrating thinking partners that can offer constructive arguments, daring to disagree, and being prepared to change your mind you are truly moving the organization to the highest level of not only collaboration, buy-in, and acceptance but you are increasing analytical maturity across the entire organization. Openness is the beginning of something truly great. As the collaboration and communication continues, the players strive to complete each other rather than compete and then this is when the trust starts to come into the data-driven culture and here is when things really start to feel right and growth surges.
What are the 2-3 big trends in the people analytics and workforce performance space that you are personally tracking as we head into 2020 and beyond?
Just like consumers, employees would also like tailored solutions for them instead of a one size fits all approach.
Machine learning techniques provide the organization with the ability to tailor personalized experiences for employees in all areas from talent acquisition to internal mobility, learning, team building, and other areas to increase employee engagement. Machine learning may be used to not only personalize experiences but also offer optimal team integration, and internal mobility opportunities with optimal positive outcomes based on data available through personality tests, social networks, surveys, or other means to create an explosive HR transformation that will increase employee engagement and reduce turnover. Of course, the ideas could also be directed towards contractors which would also offer a big return on the investment.
Employees and contractors would like the same personalized experiences that are provided to customers and this should not surprise us as customers, employees, and contractors are all people. I would expect to see an increased focus on correlating employee engagement with productivity levels and business results.
Want to hear more from Perla and other Executives from AIG, Taboola, Webster Bank, Marriott, Panasonic, and GE, among others? Meet and hear them speak at The 21st HR Metrics and Analytics Summit in Orlando, FL. Register with a discount here
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