Implementing Reliable HCM Data Successfully Into Your Business

Human capital data is a necessity in the business world today. Whether managers are being asked to evaluate their employees or human resources (HR) is looking for data to help leaders make long term decisions — reliable data can provide employers and workers with figures that are essential to removing negative factors and achieving positive outcomes.

In Deloitte’s 2018 Global Human Capital Trends survey, 84 percent of respondents saw people analytics as either important or very important. These results make human capital analytics the second highest-ranked trend in terms of significance. This is predicted to grow as analyzing, forecasting and improving the workforce through collected data becomes instrumental in the everyday operations of human capital management.

One issue that employers may grapple with when collecting data is determining whether this data can be considered “reliable data” or “suspect data”. Historically, data has not been collected in a reliable manner. Often systems and processes are set up once and training becomes a word of mouth or show your replacement process. This creates suspectable data which allows the ones interpreting the data to apply his or her opinion or reasons to make exceptions. It is critical to develop good processes and good training habits to maximize they systems that will be using the data.

According to Marcus Buckingham, a leader in the world on Human Capital Management, there are three words that we must always keep in mind when collecting or making decisions based on data: reliability, variation, and validity.  With this you must have consistency and trust your processes to collect the data. 

If you’re asking yourself whether data you receive is reliable, you’re questioning two aspects of the data: is everyone collecting the data in the same rigor and does it really measures what it says it will? Meaning, if we were to ask an employee to rate their own performance in the workplace, does it reliably calculate that employee’s performance? If this question is presented, employers and HR will be hard pressed to find employees who will answer that they’ve performed “poorly”. Therefore, this data becomes suspect data.  

Once leaders are able to determine if the data collected will be reliable, employers need to make sure that there is variation in the tools used to collect this data, one that will be able to measure answers to the full extent. Are the definitions clear and is there consistency to support any HR objectives. A manager believes that 40 percent of their employees belong in the exceed expectations category but HR says that managers are only allowed to rank 20 percent of their employees at an exceeds expectations level. If the definitions are too broad, then those managers are being told to give unreliable data. They are now obligated to randomly choose individuals to be ranked in that 20 percent, downgrading the other employees into the meets expectations category instead of where they actually belong. With this limitation, the data collected becomes unreliable due to lack of variation.

After ascertaining that the data you’ve collected is reliable and the collection procedure provides enough variation in the answering process, Buckingham states that you have to then determine the validity of this data, or whether the information you’re collecting really matters. Does the data you’ve collected, the performance review of employees, make a difference in the positive progression of your business? In this case, it very well could. HR can collect the data to determine what employees ranked lowest in the employee performance categories and make retention decisions based on the figures.

Making decisions based on the data collected, so long as it’s valid and reliable, can make a huge difference in the future of your business. Companies can hope to use this human capital data to address issues like measuring employee performance, but also issues that correspond with recruiting, compensation, retention, and so many more. The success of Human Capital Management depends in part on determining hiring needs and looking into workforce trends to enable future successes.

In order to predict these trends, 69 percent of organizations are building integrated systems to analyze worker-related data. Currently, much of HR data is collected through competency ratings like the one described above. While they can give an over-arching synopsis of an employee’s performance, they tend to be unreliable, considering 61 percent of the rating usually reflects the person doing the rating as opposed to the person being rated.

Employee opinion surveys are great sources of data since they are rating a person’s own experience and purpose. With enough variation in the definitions that an employee can choose to answer, ranging from “Strongly Disagree” to “Strongly Agree”, we can better predict future performance of employees. We are not asking the employee directly how they believe they perform, rather how their job reflects back on them. The better performers tend to answer “Strongly Agree” when asked questions like “In my team, I’m surrounded by people who share my values” while the low-ranking performer will answer “Strongly Disagree”.

This is just one of the many ways to collect reliable data that can help improve your company’s performance. And while there are many, make sure the data collected is reliable, has a large enough variation to get reliable answers, and that collecting this data can actually make a difference in your company’s future. We are being led into an era where human capital data is becoming more prominent in major decision-making processes. Don’t your company be led astray by bad data.

RCM Technology’s HCM group provides consulting services in optimizing your data reliability, variation, and validity allowing you to maximize your HR analytics strategies.

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