Ever wondered how Google manages to draw the best out of its engineers all the time? It does so with the help of a retinue of smart managers. The data mining giant understands that managers have a much greater impact on employees' performance and how they feel about their jobs than any other factor. And how did it develop this insight? Not by chance, nor by accident. Its 2009 initiative, code-named Project Oxygen, cranked out what is referred to in popular media as the "Eight Habits of Highly Effective Managers" - traits that have been incorporated into its various training programmes and have given the company "statistically significant improvement in manager quality for 75 per cent of the worst-performing managers," according to various reports. While Google was smart in applying knowledge gleaned from months of research to better manage its workforce, several other companies are still learning the ropes. But the fact is workforce analytics is hot topic in HR discussions today and a capability in high demand. To use the words of an eQuest whitepaper, for human resources in particular, big data marks a "historic opportunity" to make the "most rigorously evidence-based human-capital decisions ever". Relevant, according to experts, in the three core areas of talent acquisition, learning and development and employee engagement, the challenge now is to derive value from analytics. As Aditya Narayan Mishra, president staffing, Randstad India, puts it, "Analytics helps HR find the blind spots in the company - identify departments that are doing better and what influences success and how it can be replicated elsewhere in the company." Of the three areas, the most important, of course, is the value analytics can add in improving employee engagement and in pinpointing the correlation between engagement and performance, retention/attrition, growth of people in an organisation. "A company can map the correlation between the recruitment cost and customer satisfaction scores, for instance. It can apply modelling with different kinds of weightage and predict things like which departments the new leaders are coming from," says Rajiv Krishnan, partner and leader, people & organisation Practice, EY. But how does all of this work? The answer lies, in part, in the very information that the HR department in an organisation encounters daily.
|MAKE DATA ACTIONABLE|
| To convert diverse and disparate data into tools for business insight, HR teams must: |
Find out if the prospective hire is a cultural fit. The second is workforce modelling. It is a very unstructured and manual process and depends on your organisation's objective. The next step is compensation planning. There is lot of structural analysis possible by using the company's internal data. The challenge here is if you want to benchmark the employee salary vis-a-vis others in the markets. That's when you need predictive analytics." In his view, performance evaluation is the most important engagement point and predictive analytics can help them treat each employee carefully. It can even predict the impact of performance appraisal on the employee - whether it will increase the possibility of her leaving or whether it will help improve her engagement score. And when an organisation is more precise in finding the people with the skills it needs, it can reduce its cost-per-applicant and cost-per-hire. So there are these psychometric assessments used by companies and offered by talent assessment firms such as Wheelbox to understand what skills a candidate requires before the company hires him. According to Bansal of People Strong, even though the predictability of psychometric tests is limited to 70 per cent, it is relevant in finding out the best-suited candidates for a job in the most efficient manner. Let's see what Google's Project Oxygen did right. The statisticians at Google gathered more than 10,000 observations about managers across 100-plus variables collated from various performance reviews, feedback surveys and other reports. Next, they manually started coding the comments to look for patterns. Once they had some working theories, they created a system for interviewing managers to gather more data, and to look for evidence that supported their notions. The final step was to code and synthesise all those results to emerge with 400 pages of interview notes. Spread over a year, the results were then collated, grouped and communicated to various divisions in the company. A host of training modules were initiated under Project Oxygen and at every step, instead of depending on software, the process of reading, rereading and coding all the information was done manually. As P Thiruvengadam, senior director Deloitte, India, explains: "When done in the right way, tracking, analysing and sharing employee performance metrics can be beneficial for both, you and your staff. You should analyse real-time information, boil it down into performance data and empower employees with reports from that data." Avoiding the pitfalls The process of collating data, reading and decoding trends is far easier said than done. The biggest challenge in predictive analytics is having a unified set of data from the same context. Says Kayal of Oracle India: "The challenge is having unified profile information, including accomplishment data of a particular employee, salary, aspirations, interest areas, education, skill set etc. The data element is all scattered. It is sometimes very difficult for an organisation to create a unified profile by accumulating all the data. But creating a unified profile data for a candidate is crucial as this is the foundation on which predictive analytics is done." Unfortunately, companies fail to match their organisational objectives with talent management objectives while executing analytics. "The more the delineation, it is tougher to have a talent management structure and build a leadership pipeline. These problems are seen in succession planning, learning and development too," says Kayal. Despite the odds, companies like Nestle, Unilever, Pepsi, ICICI, for instance, are doing a great job of culling data and using it to study their employees and leveraging people metrics and insights to improve business performance and employee engagement. "Eventually, it comes down to data versus insight. The skills of uncovering 'insight' and being able to communicate this effectively as a 'story' that correctly influences human capital decisions, is of critical importance in the global economy," says Arun Dhaka, country sales director, India and South Asia, Cornerstone OnDemand.