Data-driven talent transformation

As rightly said by Jelf Zeanah, “The role of data analytics in an organization is to provide a greater level of specificity to the discussion.” 

These days' data is ubiquitous within the organization. Every system has a spreadsheet for working on and analyzing something. Organizations prefer their decisions to be based on and backed by data. The last three years have been a whirlwind for organizations. The workforce has gone through high churn, and the ways of working have changed drastically. 

These dynamics and the much-discussed phase of the Great Resignation have made HR look at the historical data and seek insights on doing things differently. 

The data available is either Structured or Unstructured.

Structured Data: Structured data consists of hiring, attrition, diversity, and quantifiable data. It is information about an employee translated into a spreadsheet. 

Unstructured data: Unstructured data is qualitative data consisting of textual documents. e.g., employee performance evaluations, mental health surveys, company reviews, or third-party websites. 

According to Deloitte, 91% of companies use essential data analysis tools such as spreadsheets to manage, track, and analyze employee engagement cost per hire and turnover rate metrics. (Venturebeat.com, July2022). However, it is imperative to invest in scalable Artificial Intelligence based tools to leverage data and make it scalable. Data has a pivotal role in human capital analytics, also known as human resources analytics or talent analytics, which is the application of sophisticated data mining and business analytics techniques to human resources data. There is never a lack of data within the organization. However, several organizations are in a rush to leverage data for fear of missing out on some predictive analytics tools and thinking about their inability to take critical talent decisions imperative to business. 

The crux of the matter is that HR should be able to answer a question about what they need to achieve from the human capital analytics continuum. The Human Capital Analytics Continuum consists of: 

• Anecdote(a narrative added while presenting data to make it impactful and create a story for the alternate to the decisions / solutions)
• Scorecard or Dashboard(representation of the progress against the plan)
• Benchmark(being robust with market intelligence and know the best practices to reproduce those best practices considering the organization’s eco system; in other words, a yardstick with an intent to continuously improve)
• Correlation (study of relationship between two factors and variables)
• Isolation and Causation – (study of the impact of an occurrence due to another factor / variable, to identifyif there exists a cause and effect relationship)
• Optimization – (envisage the future outcomes through various data analysis techniques contributing towards the process of taking right decisions within the organization)

There are vital elements to be considered while leveraging data in talent development and learning initiatives. 

1. Alignment with Organizational & Business Goals 

Data analysts must consider asking themselves the following questions before leveraging data:

What is the corporate goal? What are the critical roles? What are the new skills to be developed? What are the current skill gaps and how do we bridge them? 

The learning and talent interventions must impact the organization's business outcome. For example, an organization with a growth trajectory looks at achieving specific business numbers. The talent and learning interventions should increase the talent bench strength internally, strengthen the leadership pipeline and build succession planning to accelerate business outcomes. 

2. Establish a Precise Measure of Success

As there is a clear line of sight on purpose, process, and pay off on learning and talent interventions, HR should leverage data optimally in two formats:

     a) Defining Impacting Business Vectors –

Determining the business vectors to be measured during the learning intervention is crucial. E.g., An intervention for supervisors may have a business vector ranging from productivity, attrition of the team, the number of promotions in the group, and the incentives earned by the team. Likewise, talent interventions can have clearly defined measures of success of building x% of bench strength of internal leaders and mobility across the function. 

     b) Utilization of Human Capital Analytics Continuum –

The Human Capital Analytics Continuum can be leveraged as a dashboard and scorecards to measure the plan vs. the actual deployment. It is an effective tool to track inputs and build a governance mechanism to measure the predefined business vectors continuously. 

3. Designing and Curating Business Backward Learning andTalent Solutions

After aligning with the business objectives and defining success parameters, the next step is designing solutions. HR should align training and learning objectives to all the tasks contributing to achieving business results, called performance mapping. It is a proven way to align training curricula to organizational goals systematically. The solutions can be dovetailed with the jobs to be done by the cohorts and the key competencies. The skill taxonomy and experience maps should be considered for critical roles and creating their developmental plan and customized developmental journey. 

4. Data Insights for Continuous Improvement

A combination of quantitative and qualitative data can provide exciting nuances on people metrics impacting business. For example, measuring lead indicators like employee engagement score, performance review, recognitions, development plans for each direct report, promotion rates etc., are early evidence of the quantifiable business results to come. 

The business results include the key performance indicators, turnover rates, productivity, customer loyalty, sales volume, cost reduction or avoidance etc. 

In the human capital continuum, this is the stage where organizations can measure correlation or causation to learn the impact of interventions. For example, the leadership journey led to 10% of participants taking up the next level of role, which means positive movement in the participants who have attended the intervention.

5. Optimization

One of the critical facets is predictive analysis, where historical data can be a great source of information if analyzed and leveraged well. Predictive analysis is a set of methods, tools, and processes to help organizations analyze large data sets to forecast future events. For example, organizations use predictive analysis in strategic workforce planning, varying from determining headcount to identifying and defining critical roles, identifying leadership potential through traits, and taking many more strategic people decisions impacting business.

Thus, data-driven talent transformation can lead to a paradigm shift in how talent and learning partners contribute and add value to the talent and learning aspects of the organization.

Leveraging data in people's decisions can enable organizations to become more agile and thrive in the days to come.