After exploring pitfalls on the path to business intelligence in my last blog post, this week I’m going to look at the other side of the coin: what you need to do to help ensure that you actually are creating actionable intelligence on the people side of your business.
Our new book, the HR Analytics Handbook: A Summary of the State of Knowledge, is a practical guide to help busy HR professionals and executives get quickly up to speed on the fast developing field of HR (or human capital) analytics. Success in applying analytics to your people data in order to create actionable insights requires that you follow five key steps:
1. Develop smarter measures of the human drivers of performance
This requires measuring the human drivers of results. As a practicality, this means that HR needs to go beyond traditional notions and measures of employee engagement. Employee engagement is necessary, but not sufficient, for driving organization business results. (link to our “Debunking” article)
2. Focus on the “right” key performance indicators
The right key performance indicators (KPIs) are those that are:
- Critical to a key stakeholder
- Available on a timely basis
- Comparably measured across multiple units
3. Use the correct quantitative methods to link these measures to performance
The most appropriate quantitative methods will be determined primarily by the number of data points (“observations”) available for analysis. The number of data points available is, in turn, determined by the appropriate “unit of analysis” for the KPI under consideration (individual, team, business unit). When the number of units of analysis is small, use simple statistical techniques. When the number of units of analysis is large, you’ll be able to use more complex statistical techniques.
4. Present your analysis in a simple, easy-to-understand format
Do your best to summarize the results of your complex analysis in a small number (even one, if possible) of simple graphics. (For an example, see Figure 1 in this paper about the drivers of plant success.)
5. Share stories that back up your quantitative analysis
Most folks (even quant types) remember stories. So weave together your analytics with stories, and key stakeholders to tell the stories for you. Combining analysis with stories is a winning combination!
My co-authors and I just finished writing the HR Analytics Handbook: A Summary of the State of Knowledge (to be published by Reed Business next month).
In the course of this writing project, we spent substantial time pondering what really gets in the way of HR professionals as they embark on a path of analytics—of creating actionable business intelligence, rather than simply collecting more HR facts and figures.
We identified 5 key pitfalls that should be avoided in beginning the process of applying analytics within your organization:
1. Measuring what is easy versus what is important
HR departments often have enormous amounts of data on the HR function itself, spending considerable time and effort on HR scorecards that focus on the department’s own efficiency. But this has little to do with the human drivers of business results.
2. Confusing benchmarking with HR analytics
Benchmarking is never going to identify the unique human drivers of your organization’s performance.
3. Accepting clever marketing parading as science
This is a particularly common mistake when it comes to understanding employee engagement, where our analysis reveals that the drivers of employee engagement across different organizations are consistently more different than they are similar (even among businesses within the same industry).
4. Focusing on ROI as the “holy grail”
Beyond telling you whether to continue or stop making an investment, ROI tells you nothing about how to improve the business impact of your investments.
5. Allowing perfect to be the enemy of good
Sometimes perfect information simply is not available, but HR professionals should not use this as an excuse; they should seek to learn from the evidence that does exist. For example, we know that correlation does not prove causation. But if you only had information on the human correlates of your company’s business results, it would be foolish to ignore it. Even correlations often provide valuable learning about key associations within your organization – even if they don’t prove causation.