Technology Selection Model for Talent Analytics

Technology Selection Model for Talent Analytics
Technology Selection Model for Talent Analytics

As Talent Analytics is catching up pace, I have seen and interacted with many of the HR folks, who struggle with the understanding of technological maturity and have a tough time with technology decisions. What is our current technology maturity? Where should we fetch the data, to come up with useful metrics? Which all reports need to be automated? Can we have a stand-alone analytics system? Where does data reside and what all systems do we have to integrate?

On the Talent Analytics journey, these questions are inevitable. As I face these questions in my daily job, I have tried to come up with a model which can help me in technology decisions.


At the bottom-left (0-0) grid, we have basic metrics & reporting. These metrics can be ad-hoc/ periodic.

Eg: Average time to fill, average workforce tenure, offer status report, senior junior ratio and employee pyramid

Here the mantra is: Try to standardize the reports, clean-up the data and use excel sheets to come up with the same.

At the bottom-right (0-1), we have reporting extensions. Here we use the cleaned up reports and derived metrics, to provide cause & effect/descriptive analytics.

Eg: Linking the workforce planning with internal rotations, linking succession planning & leadership promotions, correlation of rewards vis-à-vis employee performance

Here the mantra is: To use the cleaned up reports and use tools available in excel/ any statistical tool like R/Minitab/Tableu to arrive at descriptive analytics

At the top-left (1-0) grid, we have stand-alone analytics. In this case, to avoid the sophistication of integrating with multiple systems, we can have a stand-alone system generating insights.

Eg: We can feed the data dump of performance management data to a system that can help us with relevant insights on performance management like appraiser classification effectiveness, span of control, effectiveness of performance conversations and so on

Here the interesting info is that it is easy to build such a system and we get insights as and when we require. The limitation would be the amount and vastness of insights generated will not be as immense as an integrated analytics system.

At the top-right, we have an integrated analytics system, which would be built using multiple data sources. As you can guess, the time required to build such a system will be high and the efforts required will be exponential, compared to a stand-alone analytics system. The efforts will include identification of data sources, standardization & synchronization of data, dealing with unstructured data, to name a few. This type of system can surprise us with insights that we might not have been able to manage through standalone reports/analytics. Also, this system can be enhanced with predictive models & other use-cases.

Eg: Predictive Workforce Planning, Behavioural Modelling to create/amend policies

While this can help you with some idea on technology decisions, few points to note here would be:

  • Take bold steps with automation wherever possible (working in a technology company can be an added benefit)
  • For basic metrics/reporting extensions, excel plus statistical tools can help you in deriving the linkages & correlations
  • Go for Integrated analytics wherever the scope for analytics is extensive, as the time and efforts spent on this is huge
  • Standalone analytics is the best bet in case you are just starting the talent analytics journey, considering the investment involved
Dharshana Ramachandran

Dharshana Ramachandran

Talent Analytics Specialist at Tata Consultancy Services
Dharshana Ramachandran is currently working as Specialist in Analytics for Corporate Talent Management team. In this role, she works in creation of talent analytics frameworks to align with the business objectives and to derive meaningful insights from people data and business performance. She has worked in various roles like Sales, HR Business Partner and Diversity Specialist. She is s computer engineer and a triple gold medallist in MBA. She has specialized in Talent Analytics from XLRI Jamshedpur and Wharton Business School. She is a marathon runner, an avid fitness enthusiast and would like to be known to be as a data driven HR person.
Dharshana Ramachandran
@australian - 2 years ago
Dharshana Ramachandran
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