)
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:
|
Why it is trending
Typically, if HR uses data, it collects business intelligence on things or events that have already occurred. Through predictive analysis, however, big data can tell HR professionals why something happened and allows them to make some pinpointed forecasts. In fact, it is this predictive nature of data analytics that is changing the role of HR… for the better. "Earlier, when CEOs and CFOs talked, the conversation was based on solid data. HR conversation, however, was merely anecdotal. Now, thanks to data analysis, HR is able to spot trends, make predictions, create a roadmap to succeed and have conversations with other C-suite members of the company based on solid facts," says Shaswat Kumar, partner, Aon Hewitt. In his view, what is critical is the HR's role to ask the relevant questions failing which data analytics would be rendered irrelevant. "The role of HR is important and it has to start with the right questions and then analyse the trends correctly and predict the trends," adds Kumar.
Says B Sivaramakrishnan, managing consultant, Hay Group, India, "By using data analytics in work force management, HR can help companies maximise return on manpower investment. For instance, in manufacturing the impact could be 10-12 per cent of total sales while in services industry, due to its people intensive nature, the impact on ROI could be 70 to 80 per cent." In his view, the recruitment cost in a company is on an average 15 per cent of an employee's cost to the company and typically 12 to 15 per cent of an employee's salary is training cost.
Those are quantifiable results. That apart, predictive analytics can help companies maximise return on employee investment and help them figure out if any increase in recruitment cost increases the chances of finding the right candidate who can fit in with the culture of the company.
Now come back to employee engagement. If numbers are anything to go by, by deploying data analytics companies have been able to reduce attrition by more than 15 percentage points, improve hiring cycle by 30 per cent. According to a Hay Group study, The Business Case for Effective Employee, by investing in engagement and enablement of employees, companies can reduce turnover rates by 54 per cent and increase revenue growth by 4.5 times. In fact, in retail the difference in sales could be 3x or 4x between a person who is engaged and the one who is not.
Talent acquisition and talent management need to dovetail, and recruitment should be tied to business results that the organisation is aiming for. Consider the financial services and insurance sector to understand the significance. For one, predictive analytics has the capacity to keep manning ratios high. "If a bank's branch needs at least 10 people but has only six, it cannot work smoothly. Manning is a critical parameter," says Pankaj Bansal, co-founder and CEO, PeopleStrong. But to make this work, HR has to make the link among the data about the organisation's needs and use that information to implement a predictive analytic strategy.
Doing it right
Those in the business say that once data is accessed, the next step is to understand the engagement points and how it can be leveraged for better results. According to Hirak Kayal, vice-president, Applications Outbound Product Management, Oracle India, there are four engagement points with respect to workforce management. "First, understand the behaviour and body language of the candidate you are evaluating. 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.
