Demystifying People Analytics: Part II

In the previous article, we discussed how and why People Analytics is likely to emerge as a focus area in times to come.

Now, most people who work in the analytics space are familiar with Gartner’s Data Analytics maturity models.

Data analytics has been divided into four categories as defined, by tools, techniques, and approach: (1) descriptive analytics, (2) diagnostic analytics, (3) predictive analytics, (4) prescriptive analytics      

However, in practice, these categories co‐exist and complement each other. The adoption of data analytics in an enterprise is not linear. These would vary in order, intensity, and application, depending on business priorities and context.

Hence, it may be prudent to look at the below model when an organization is exploring Analytics, as a way to resolve problems.

The organizations which have seen tangible business outcomes have focused on the problem they are trying to address rather than the kind of analytics they are using. Building an Analytics team without having a clear definition of problem areas is going to backfire. The team will sit in a silo, crunching numbers that no one understands and cares about.

For instance, in the case of turnover data, almost every organization runs some analysis on it. For some organizations, it is critical information that directly impacts business. But, for others, it is a tick-in-the-box activity. And the insights do not lead to any meaningful change or outcome.

In the current times, some of the direct applications of analytics could be:

• Creating an optimal hybrid workforce model and leave-system to match the demands of current times
• Identifying patterns of overtime, attendance, and leaves to maximize workforce productivity and reduce payroll leakages that also add value to employee well-being and engagement. A simple but effective exercise could be tracking online meetings that an employee is a part of and running a test on the fatigue these meetings cause.
• Identifying the time, it takes for a new hire to be productive and hence an ideal point of intervention if the organization doesn’t see the results, instead of letting the situation simmer till the next performance appraisal
• Measuring the impact of employee engagement initiatives on employee productivity could also be a great way to find out what works rather than relying on hope. The same goes for Employee engagement surveys, quite a few organizations have already shifted to pulse surveys that keep feeding an immense amount of data into the system. This data needs to be analyzed to provide real-time meaningful solutions otherwise employees will lose interest and the whole initiative will lose steam.
• In one of the IT organizations where I worked, we analyzed each project and forecasted the skill-sets required for the next year. We created a plan to build those skills, by looking at the existing capability data. This was a simple analytical exercise however, in hindsight, it would have been a good idea to use predictive analytics in this case. It would have made a lot more sense to understand market trends, availability (internal and external), and the most cost-effective solution to fulfill each of the forecasted demands on time.

As per 2017 Deloitte Global Human Capital Trends, analytics is used to address a wide range of business challenges: Recruitment remains the No. 1 area of focus, followed by performance measurement, compensation, workforce planning, and retention. The report also states that only an abysmal number of respondents believed that they had usable data and the readiness to move into advanced analytics.

As was discussed in the previous article, the possibilities are immense however, the focus should be on those questions that remain unsolved, and a fair assessment on the suitability of analytics to be used in uncovering insights.

In the next article, we will take a look at some of the analytics-led HR initiatives that organizations like Google, Chevron, and Cisco have launched to improve performance and provide meaningful decision support to business leaders.

References:

https://www.tlnt.com/what-an-analytics-maturity-curve-should-actually-look-like/

https://www2.deloitte.com/us/en/insights/focus/human-capital-trends/2017/people-analytics-in-hr.html