Until recently, data warehouse and business intelligence deployments have primarily focused on financial and other readily quantifiable subject areas. The emphasis of analysis based on these deployments has been on evaluating the performance of the physical assets and investments of a corporation. Metric-driven questions such as "Which plant has the lowest overhead?" or "Which sales territory is generating the highest revenue?" are easily answered once the main challenges of integrating often disparate data and modeling that data in an efficient form are overcome. However, in today's increasingly service-driven economy, attention is gradually shifting toward assessing and managing the performance of less tangible assets, namely the investment in human capital. Human capital is currently one of the least measured and analyzed investments, which is ironic because the Brookings Institute found that more than 70 percent of a company's costs are labor related. Very few CEOs can state the ROI of their personnel or how their people are affecting - both positively and negatively - business results. However, the human capital investment is essentially the "weak link" in the chain of corporate investments: returns on all other investments are driven by the quality and strength of the investment in human capital. For that reason, employing analytics in human capital management is as critical to achieving continued growth and success as monitoring the financial statements.
Traditionally, the workforce has been thought of in terms of the physical sum of people employed. However, the human capital investment is defined as the total amount and quality of talent, knowledge, expertise, experience and training that these workers possess. As executives and investors begin to view the workforce in these terms instead of as a general cost of business, they are beginning to realize the opportunities that exist for capitalizing on this asset. Resultantly, demand to determine the current value of human capital and achieve workforce optimization is increasing. The drive toward human capital management is exemplified by:
- Investors requesting intellectual capital statements to be included as part of annual reports. The most important reason for this is that human capital can be considered a leading indicator, as it drives the performance of all other assets. Studying trends in the performance, satisfaction and condition of the workforce helps to form expectations of future returns.
- With the current trend toward downsizing, increases in and/or continuation of workforce investments need to be validated. The capability to demonstrate just how this investment impacts business performance can more clearly provide this justification.
- Especially in large and geographically dispersed corporations, executives are realizing that they do not have a firm grasp on the true size and characteristics of their workforce.
- Effectively deploying the workforce so that the right talent, and right amount of talent, is driving toward accomplishing the right objectives is a major challenge.
Most companies today are performing some type of human resources (HR) metrics based on employee counts, such as turnover ratio and average tenure. With HR metrics, usually the collected data is the ending point. Once the data is gathered, the workforce has been summed or counted. However, the main tool in achieving human capital management is workforce analytics. The collected data that is the ending point for HR metrics is the starting point for workforce analytics. Intelligence and experience are applied to the data to interpret its meaning and use it in a more strategic and proactive manner than plug-in metrics. However, the main reason that workforce analytics are more insightful than HR metrics is that analytics incorporate information from all areas of the corporation, not just from HR, as well as information external to the corporation. This enables a 360-degree view of the human capital investment including all factors affecting the workforce as well as all factors the workforce affects.
As an example of applying workforce analytics, assume that a sales region has not been meeting its targets. In addition to looking at financial figures, workforce analytics could also factor in the effect of the human capital investment. After all, it is the sales team personnel that are not generating the sales. Traditionally, a manager would address this problem by seeing if there is enough staff on the team or if there is a high turnover ratio. With analytics, other factors, such as median salary information for that region compared to the salaries of the sales team, can be considered. Or, perhaps a gap exists between the training necessary to sell the products and the training the team has received. Analytics that combine internal and external factors assist with efficient and targeted root-cause analysis.
With this example, it is easy to see that the availability of robust information that supports workforce analytics can drastically reduce the amount of effort required to resolve critical issues. However, the human resource management systems (HRMSs) currently running at most companies are transactional in nature and focused on compliance reporting; they were not designed to perform workforce analytics. Addressing the problem in the example might require time-consuming salary surveys and manual integration of information from separate systems. However, vendors are recognizing the need to accommodate workforce analytics, and the latest HRMS offerings are starting to facilitate this type of analysis. The problem is that while it is easy to incorporate information from customer relationship management (CRM) or general-ledger (G/L) systems into the HRMS if the corporation is running all transactional applications from a single vendor, if each system is a different brand, then the interfaces have to be custom coded - adding time and cost. Additionally, in most cases, external information such as census and benchmark data will still have to be manually incorporated.
For most corporations, the method of providing workforce analytics that will be the least painful and provide the quickest ROI is to develop a human capital data warehouse from which business intelligence tools can perform workforce analytics. The human capital data warehouse, independent of any production system, is best situated to combine information at differing levels of granularity from multiple sources. From a technical perspective, the same data warehousing tenets and methods apply to human capital data as to any other type of data. However, the intangible nature of the data is what makes this initiative more intimidating. Recognizing this challenge, the first task is to develop a comprehensive warehouse plan and design based on a deep requirements gathering effort. After that, the other phases of the warehouse build and analytic deployment have some challenges that are unique to this type of data.
As with any warehouse, the best way to approach the design of the human capital data warehouse is to first think of the desired uses of information. Most likely, one of the first requests an executive would make is identifying the points of excess so that the workforce can be trimmed. While the warehouse will, usually for the first time, facilitate the type of drill-down capability that supports reduction initiatives, it will be important to point out that shortages may also be identified. Another use case would be that during planning and development of strategic initiatives, it would be possible to integrate the impact of head count. Benchmarking will be possible. Data will be available to support balanced scorecards. Additionally, employees will be associated with the finance department structure, instead of solely the HR reporting structure. This means that executives and managers will be able to obtain accurate counts of the people within their sub-organizations to answer questions such as "How many people report to the finance organization?" In large corporations, this question requires manual counts because HR does not track employees by the finance department hierarchy; however, the warehouse will have the ability to track employees in multiple hierarchies.
Some business intelligence tools offer a workforce analytics package. These packages consist of prebuilt reports and analytics that, in most cases, simply need to be pointed to the appropriate data. If one of these products is going to be used, it will be important to thoroughly examine the types and format of inputs that will be required to be built into the warehouse to support the business intelligence application.
When the uses and outputs have been decided, the logical design necessary to support them can be developed.
Figure 1: Analytic Development and Deployment
Sourcing the Information
Once the logical design of the warehouse has been developed, most likely it will become apparent that a good amount of required information is not currently tracked. Therefore, two simultaneous efforts should occur at this point:
- Subject areas for which information is available should be implemented.
- Initiatives should be undertaken to start collecting and storing the unavailable data.
Going ahead first with only the subject areas with available data, rather than undertaking a huge initial effort to source all data and then implement all subject areas together, is an iterative approach to warehouse development that will provide a quicker ROI. The realized benefit of the initially deployed subject areas will garner support to build the processes to track the unavailable data and to continue funding the development of the warehouse to achieve the full logical design.
When the available inputs have been identified, receiving access to the data can be a sensitive and sometimes bureaucratic effort when compared to getting data for other types of warehouses. Obtaining data from the HRMS will be the most difficult. Some usual data sourcing issues are: whether to identify employees by name, whether to provide salary information and whether the effort should or can legally include diversity information.
Corporate politics can cause another challenge in collecting data. The level and position of the business driver of the warehouse can be an issue, or certain sub-organizations may resist being included in the warehouse or may even manage to pull the plug on the entire initiative. There are two ways to combat this situation. The first way is to educate everyone about workforce analytics. Obtaining buy-in is easier when the uses and benefits are communicated. The second, and easier, method is to have an executive that is high enough in the organization to enforce compliance as the business driver. It also makes sense to have a high-level executive driver because the benefit of the warehouse to the corporation is correlated to the percentage of the corporation included in it, and the more senior-level driver will be able to obtain full corporate inclusion.
As mentioned previously, the value in workforce analytics is that it not only includes internal data beyond HR, but external data as well. The amount and type of external data is dependent on what the nature of the deployed analytics will be. Census figures, industry benchmarks, cost of living and regional labor market information is available for purchase in varying degrees of detail. Many companies already have this data organized, so be sure to review the format of the information to make sure it can be easily incorporated into the data model before making a purchase.
After performing the data extraction from the sources, the tasks associated with transforming and merging the data are similar to those in other types of warehouses: cleaning the data, correcting keying errors and dealing with legacy/integration/upgrade/merger data disparities. However, there is one challenge in this step that is specific to human capital data warehouses.
As discussed, a main benefit of the warehouse is to be able to drill through the employees in the organization by the finance department hierarchy instead of the HR hierarchy so that executives can view the workforce in the format in which they manage it. However, while information from systems such as CRM and G/L will be in this format, the employee-specific information coming from HR will be in the HR reporting hierarchy. Occasionally, the match-up will be clear or made possible by translation tables. However, if all systems are not from the same vendor and the corporation is large, there can be very complex and lengthy translation scripts that will need to be custom-coded to perform the HR to finance interface. When there are thousands of departments in multiple business units, coding the match-up of HR to finance data in the warehouse transformation procedures can be time-consuming.
Before undertaking this task, make sure to ask if there is some general rule that has commonly been used to bypass recreating the translation scripts. For example, if the HR department number is seven digits and the finance department is four, positions two through five of the HR number might be the finance number ? with a few exceptions that can be coded into the warehouse transformation procedures. A good amount of research should be performed on this issue ahead of time to determine the level of effort that will be required. Sometimes the reason that comprehensive workforce analytics do not exist at a company is exactly because of the perceived difficulty of information integration.
Capitalizing on the Synergy
With all information related to the workforce residing in the warehouse, business intelligence tools can use it as the source for workforce analytics that will support management of the human capital investment. Analysis may be as simple as creating sets of online analytical processing (OLAP) cubes to enable ad hoc drilling or may include the development of complex charts and key performance indicators. One important fact to realize is that delivering the analytics is only half the effort; the other half is incorporating knowledge and expertise to interpret and exploit them. No tool is capable of making this inference. This requires subject-matter expertise. If this expertise does not exist within the corporation, it will need to be outsourced.
Another use of the human capital data warehouse is to perform offline reorganizations. Most corporations do not have the ability to load a planning or reorganization department hierarchy into production to test how numbers will roll up prior to finalizing the structure. However, the warehouse can support multiple hierarchies simultaneously. This means that reports can be generated from the test hierarchy in the warehouse, and the hierarchy can be perfected without affecting production.
With this comprehensive set of information pertaining to the human capital investment available in the warehouse, once workforce analytics have been deployed, thought should be directed toward expanding the warehouse for other uses such as:
- Integration with a knowledge management system to determine where
- talent deficiencies exist and where talent can be better deployed.
- Succession planning reporting.
- A "human capital physical" where the corporation receives regular and in-depth evaluations from an impartial third party to determine weaknesses and progress toward initiatives.
- Integration with, and/or deployment of, employee satisfaction surveys.
Clearly, deploying analytics to manage the human capital investment presents unique challenges. However, the opportunity to maximize that investment and exponentially increase the returns on other corporate investments makes the effort worth undertaking.
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