CedarCrestone recently pushed out their 2007 Analytics survey. While I’m not sure my own experience matches theirs, there are some interesting trends to at least be noted. You can find their full version of the survey here.
In general, when we’re talking about HR metrics and analytics, I’m perfectly happy with a definition that includes any type of reporting and data regurgitation out of theHR data source(s). Once we get into the concept of data warehouses though, my mind narrows a bit and I start thinking about multidimensional and drill through reporting that you get from ROLAP and MOLAP cubes. I start thinking about ETL’s and enriched data marts. While mostly beside the point, what I’m saying is that when I read the survey, I questioned a great many of their results, but I think it’s all in the semantics.
By far, the most common reporting tool is the simple management reports run from the HRMS, with 62% reporting they do this today (see Figure 2). Simple management reporting has been the first online interaction for managers since we began tracking employee and manager self service eight years ago. Operational and ad hoc reporting follows with 30% usage each. Least used today is multidimensional reporting (18%). For those not familiar with multidimensional reporting, this functionality is needed to drill down into the data to begin to see what might be behind a specific metric—such as turnover—in a given department. ((Cedar Crestone, 2007. “CedarCrestone 2007 Metrics and Analytics Report.))
While a great deal more detailed, what is interesting to me is that through the advent of SaaS and point solution vendors, it seems that HR organizations are getting access to more robust reporting and analytics tools. This is the only way I can think to explain the 18% of organizations who have access to multidimensional reporting. Where a few years ago, you’d get very operational ad hoc report writer with your HRMS solution and hope that your 3rd party outsourcers would not take 3 weeks to produce some standard metrics for you to merge with your HRMS data, today you have the same decent on-demand reporting out of your HRMS and much better analytics solutions prepackaged with the SaaS type services.
What CedarCreston accurately points out however, is that HR is not yet positioned to leverage the great multidimensional reporting that I mentioned above.
But plans seen in 2005 to move to stand-alone workforce analytics applications have not materialized. However, we believe organizations with ERP solutions will ultimately move to the more comprehensive workforce analytics applications available from the major ERP vendors. From a technology perspective, they provide integration, scalable solutions, and convergence with service-oriented architecture (SOA) components. Most importantly, however, ERP providers own and provide best practices for the HR business processes through their HCM applications. They understand that analytics are most meaningful when delivered as part of the daily business processes used by different levels in the organization and they provide best practices for the analytics most appropriate for these processes through their applications. ((Cedar Crestone, 2007. “CedarCrestone 2007 Metrics and Analytics Report.))
The short term holy grail of analytics is really SOA, ERP and a fully functional HR data warehouse. The ability to eventually have a convergence of all the data that is important to your HR organization in a single space is the dream of many of us. And there shouldn’t be anything really holding us back today. Up next, I’ll spend a couple days going over DW and what it all really means (in non-technical terms). I’ll see you then.
2 responses to “CedarCrestone 2007 Analytics Report”
The 18% of survey responders doing multi-dimensional analytics are truly best practice organizations. These are the employers and people that get it! In all likelihood, if you look at the financial and human resource performance of these 18%, I’m sure their respective results would highly outperform the non-18%.
Given the “departmental turnover” example cited in the survey summary, this is a wonderful example of how data mining can think and behave like the business. Without these granular analytics, HR as well as the line would be repeating the same mistake over and over again. I think it was Einstein that said something like doing the same thing over and over again with different expectations is not exactly “genius” behavior. Hence, in this turnover example, data mining can be the shining light for HR to change its hiring practices or processes to better effect change.
In my world, everyone claims to be grappling with controlling healthcare costs in an out-of-control environment. Not to anyone’s surprise, the employers with the lowest healthcare costs are those that are doing data mining at granular levels in order to understand how their respective plans are being used. For example, aggregating medical claims based on hire date and termination date can show how the health plan is being abused by high turnover employees.
Unfortunately, not everyone in the market understands all of the moving parts and those that are in best position to use data mining don’t even know how to assess the opportunities. In spite of the fact that the HR industry has been saying things like “be a value-added business partner” the lack of data mining understanding will prevent them from truly being optimal business partners.
On a more positive note, the 18% of the organizations using multi-dimensional technology are truly innovators and value-added business partners. If any of you 18% are reading this congratulations on a job well done!
Most organizations — not just HR — don’t know how to use the data they have and turn it into operational changes, including the (18%) companies that have multi-dimensional capabilities.
So the first miracle is to get some good reporting. The second is to get it to the day to day workings of the operation as the study noted. It’s not an easy task.
It’s good these studies are done; it is a transparent way of seeing progress.