One of the major stumbling blocks when getting started with HR analytics is not building the business or proving...
the value of HR analytics, but knowing how to use analytics. Following the rise of talent management and cloud HR technology, HR analytics capabilities are becoming the new focus for chief human resources officers (CHROs) and HR technologists. The growth of big data and analytics capabilities means that powerful, real-time analytics software is now widely available.
As organizations invested in analytics and data warehouses for sales and financial data, HR has often been left wanting. However, that is changing. The main players in cloud HR software have analytics capabilities built into their platforms so that HR departments can get access to analytics where previously they could not. As more and more organizations move to the cloud, development of analytics capabilities will grow. But understanding and knowing how to interpret analytics is a skill set not common in HR, and it will require training.
Develop an HR analytics strategy
Before using analytics, organizations should develop an HR analytics strategy. This strategy should focus on taking a deep look at what should be measured and why it should be measured. Are you keen to understand the correlation between training investment and overall performance in your organization? What about tracking attrition by business area? Is headcount reporting a major headache that needs deep drill-down measures?
With so many possibilities, it can be difficult to understand and recognize the biggest added value. By defining your initial pain points and key areas of interest, you can build a focused strategy. A phased approach of introducing new analytical capabilities over a three- to five-year period will enable you to grow a successful analytics capability internally.
Embedded analytics or best-of-breed?
When acquiring HR analytics capabilities, there are two options: HR software with embedded analytics or a dedicated HR analytics platform. As mentioned earlier, many cloud-based HR systems have embedded analytics. However, many customers may not be moving some or all of their on-premises HR system to the cloud and therefore may look toward a standalone product. In most cases, embedded analytics are superior to a dedicated system because, more often than not, they are designed to process the data of that system. Loading data into a dedicated system can be troublesome if some form of extract, transform, load (ETL) process is required. That said, a standalone product might be the only option available to a customer and could still add value, even after the ETL effort.
The biggest advantages of dedicated HR analytics are prepackaged metrics and a focus only on analytics. Dedicated analytics get 100% attention from their product team, while embedded analytics are likely to be only one area of focus across a busy team. Dedicated analytics are often feature-rich in analytical and modeling capabilities, as well as predictive analytics and benchmarking. Cloud products have the added benefit of being able to provide aggregated benchmarks using data from all of their customer's tenants. Some products provide hundreds or even thousands of metrics that can be used out of the box.
More often than not, a separate data warehouse is not needed for either solution.
Correlation does not mean causation
The most common mistake made when working with analytics is to presume that a correlation in the data is direct proof that something is true. This is why understanding analytics -- and the correct way to interpret them -- is so important. In a recent diginomica.com piece on HR analytics, analyst Brian Sommer of TechVentive gave a solid example regarding retention analytics:
Just because an arithmetic model found a 92% correlation between some factors and retention doesn't mean that everyone with those factors will actually stay with the employer a long time. Likewise, someone who does stay a long time might not have any of those factors. In and of themselves, these correlations might be interesting, but they are not predictors or guarantors of future performance.
Anecdotally, we all know people who changed jobs simply because their spouse/significant other got a career opportunity in another city. Few algorithms check for that right now as their data sources don't have access to a worker's spouse's future employment information. These correlations are not perfect, do not factor in all variables and can't really predict future acts. But too many business people immediately jump to the conclusion that a high correlation equates to causation. That’s a common mistake.
Understanding your data, including the factors that work for and against your analytical outcomes, is critical to ensuring the analytics are meaningful and actionable.
Is your data ready for analytics capabilities?
Let's assume that you understand what you want to measure and you know how to interpret it. The next and possibly most critical aspect of preparing to use HR analytics is whether your data is ready for analytics. There is no more relevant use of the phrase garbage in, garbage out than in an analytics context. For your analytics to be relevant, meaningful and actionable, the source must be accurate. Not only that, data must be formatted in a consistent manner across the organization in order to perform apples-to-apples comparisons. One of the biggest challenges of global organizations or those that have undergone a number of acquisitions is that numerous segments of employee and HR master data may be formatted differently than other segments. Aligning your global data standards and performing data cleansing is a difficult and time-consuming exercise that should be completed before any analytical activities begin.
Another challenge in preparing your data is obtaining and formatting external data, such as U.S. Federal job data or Radford Job Codes. This data often comes in one format and will need to be aligned with your existing data if you don't maintain it already within the source system of HR master data.
There is no disputing the fact that HR analytics are powerful and add value, but using analytics still requires some adaptation of the HR organization to ensure accurate and meaningful interpretation of analytics. HR systems featuring embedded analytics are high value, but dedicated HR analytics products offer rich functionality and often provide prepackaged metrics. Despite the value of HR analytics, getting up and running can take some effort in data preparation, which should not be underestimated.
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