This blog is co-authored by Halina Pupin who has since 2006 been advising her clients on ways to achieve effective data analytics based upon her experiences in an earlier successful career with SAP and Rogers Communications.
We constantly need answers to business questions that are dependent upon Human Resource information that typically include:
- What is the correlation between an employee’s performance rating and her success in the organization?
- How do salary adjustments as a feature of the reward program contribute to the performance of the organization? Is the value the same for hourly, salaried, senior management?
- What type of training has the most beneficial impact on business performance?
- What contributed to a new hire’s success and how can those measures be applied to future hires?
HR Analytics is a fast emerging and potentially powerful workplace tool that we are going to hear more about in the coming months. The starting place is to identify the questions being asked about the organization’s people. Capital, R&D, marketing, and sales are all important but without people the company’s plans for these functions go nowhere.
Identifying the concerns establishes the areas for investigation and the data sets needed to identify relationships and causal effects. For example “What is the link between performance rating and career advancement” the performance rating history, length of service, positions attained, education, career history and professional training are all data points that may answer this question. These are all employment record data elements, available within the HR universe but the definition of ‘success’ might mean level of sales, projects completed, budgets controlled or geographies managed involving data from other areas of the business.
Credible conclusions will need to relate to business markers commonly referred to in the business that colleagues will recognize and understand. HR analytics is about the ability of people to deliver results for the benefit of the organization. Identifying the key questions first is critical for a tailored response and unlike most other things going on in HR is not about what the competition is doing.
Analytics in contrast to reporting which is focused on historical data, is about identifying patterns and effects in order to arrive at conclusions and approaches appropriate to the organization.
Historical data tells us that we hired 75 people last year, 65 the year before and 95 the year before that. Analytics tells us how many of those were successful and inform future hiring decisions for improved success. Analytics is about using data to predict potential outcomes.
Sourcing schools attended, the industries worked in, experience gained, its deployment and how long those new hires remained helps the organization improve the quality of future hires by focusing on similar profiles.
The next step when considering analytics in human resource management is to recognize that inquisitive management will want more once the first round of questions have been answered. Having available every possible datum will be impossible and no software without extensive modification will be able to do that. Besides the HR function cannot afford to become overwhelmed managing data. Building a universe of relevant data that has previously raised interest and explored is a reasonable position.
As important as the quality of data (e.g. its source and how it is updated) is how accessible it is. Will special software or other processes be needed to gather and analyze and what training will the HR team require to be able to present data analysis in a form that the business understands and can use.
When moving to analytics it is important to determine responsibility for cross functional analysis. For example, will HR focus only on employee data and if not, how is additional, external data to be accessed? Sales, marketing and accounting information might need to be combined with employment data as well as statistics from outside the company (e.g. number and types of graduates from various institutions or cost of living indices, etc.)
We will be exploring these areas further in future blogs and identifying data analysis techniques that might be useful in your business.