Blog: Making organizations future-ready using analytics

HR Analytics

Making organizations future-ready using analytics

Here’s how companies can identify early signs of disengagement, and gather meaningful data to act proactively on talent needs.
Making organizations future-ready using analytics

While the world is talking about leveraging technology and making decisions based on data, as HR professionals, we need to ask whether we are leveraging technology to the fullest. Can we think of our current processes and systems that can be made future-ready to help our business grow?

There is huge potential in our current HR processes, wherein we can make a shift and start looking at future trends rather than just historical & current data.

In the world of Artificial Intelligence, there is an opportunity to relook at our current data matrix to make it more relevant to our existing business requirements. What if we can predict the exact names of people who are going to leave the organization in the next 3- 6 months? Or be able to identify the percentage of women who can be promoted in the next year, and therefore, how many positions do we need to still hire from outside? And can we identify the triggers of disengagement before they lead people to exit? 

Can our data on performance, compensation, time employees spend in the organization show any trends towards attrition?  And can it help us predict the future? Annual engagement surveys to real-time engagement data, what makes real business sense on the ground, and how can save costs for the organization? 

Data on performance, compensation, the time spent by the employee can inform trends on attrition

There are a few areas that can be made future-ready:

Predictive attrition: For several years, we have been working on creating different kinds of analysis on attrition. Can we try and link our hire to retire strategy on this data along with proactive retention? The question to ask is whether we can leverage past data and predict attrition, not by percentage, but the actual names of people who may be at risk of attrition in the organization?

How to get there

  • Gather all employee data including total experience, roles in the current organization, no. of promotions, tenure, time in current role, years in the last role, compensation data, etc.
  • Put all the employee history data in one place in HRMS or excel

The secret sauce: Look at patterns in the organization, when you analyze this data you will see patterns – For example, people who did not attend any training in the last nine months are likely to exit 70% of the time.  

Now convert these patterns into actual themes and identify the people who fall in these patterns. This will give you a list of people, now keep analyzing this data every month for the next 3-6 months, and you will arrive at the people likely to leave.

What to do with this data: Speak to respective business heads, show them the analysis on why an employee may be at risk of attrition. Ask which of your employees we need to retain and jointly create a retention strategy for them. 

You will see a more valuable conversation with your business and CFO on Talent strategy as you now move from reactive to proactive retention strategy.

Early signs of disengagement: We often ask questions during the exit interview to gauge the disengagement reasons of an individual. What if we can proactively identify the key trends that are part of attrition data like growth, compensation, promotion, learning, culture, work-life balance, location, etc. and ask managers to fill this information every quarter for their teams? Based on the response, one can create a map of people who may be identified as being on high risk, low risk, and medium risk. 

This data can trigger conversations around people at risk, what can be done to change the situation, being prepared for any exit. It can also lead to organization level decisions on culture, creating awareness about polices, providing flexibility, or creating a learning charter for the team. This heat map of people of each team can be a part of your HR quarterly business reviews. 

Talent reviews growth map: Currently, most organizations follow talent reviews for mostly senior/critical roles as the process is mostly manual/ discussion format. The talent review data of the past years can be put in a simple tool wherein people are mapped to the talent review grid like a 9-box model. Enter data around the movement of people over the last few years. 

What kind of developmental interventions do we need to run for our senior leaders that will help them grow?

This data will help create a map of the future forecast of available leaders, how many senior people will be getting ready for future roles every year and what roles we are going to struggle for internal growth/promotions. One can also leverage this data for diversity to understand how many associates are getting ready for the future.

Where can we leverage this data

What decisions /changes can we make in the organization: This can help in making critical decisions on how we are growing our senior leaders.

  • Hiring plan for senior roles
  • What kind of developmental interventions do we need to run for our senior leaders that will help them grow like giving real-life action learning projects, job rotation, 2-year learning journey, coaching, etc.,
  • Diversity roadmap and how can we work on this.

Happiness meter: Do we remember the annual engagement survey that we ask everyone to fill, and then we share results and create action plans for the next year? And again, wait for next year's results? With the latest technology and diverse workforce we have, we need to think about a survey that can be quick and share instant results rather than waiting for one full year. 

Think about three questions that can be a pop-up every week to associates and keep analyzing data cues by location, manager, role, tenure. You will soon gather insights on the pockets that are disengaged and can speak to people to get more insights. This will help in improving the engagement levels rather than annual surveys.


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Topics: HR Analytics

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