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Unlock The Power Of People With Talent Analytics

Unlock the power of people with Talent Analytics

The field of talent (people) analytics involves the use of data to create actionable insights to improve the way we do business. While reports can provide us with data, talent analytics takes a deeper dive, bypassing decision-making based on gut instinct and risk avoidance. Through talent analytics, disparate data points from both people and business combine to create consumable, meaningful insights that impact and ideally, ultimately improve the way we do business. 

Before any analysis, we need to consider two essential issues: the ethical usage of data, and the collection, storage, and democratization of data. 

Ethical Data Collection 

Ethical data collection, storage, and analysis requires consideration of informed consent, transparency, security, privacy, bias, and objectivity. It requires us to delve deep into who might be disadvantaged by the use of a particular algorithm or data set. This process is a prerequisite for building customer trust and value. 

Once ethical data usage is established, it is critical to provide thoughtful structure and access through data engineering. Data engineering is the method behind the systems utilized for the collection, storage, and democratization of data. Data engineering should be an active conversation and concern within your organization so valuable information does not end up lost or inaccessible. 

Identify a Business Problem and Take Action 

Once data is both ethical and accessible, organizations can utilize talent analytics in various ways, allowing examination of everything from hiring decisions to internal processes to customer concerns. After establishing a clear and concise business problem, experts can begin to unlock the power of the organization’s data. 

For example, all organizations should be able to answer questions on internal diversity, equity, and inclusion. To answer these questions, demographics and identifiers can be combined with organizational information such as promotions, departments, titles, hiring, retention, performance, and turnover to create deeper understanding of the organization. 

Beginning analysis may involve the comparison of current organizational demographic statistics with benchmark employment data from the Bureau of Labor and Statistics, providing a useful evaluation of the organization as compared to the current workforce population. This initial analysis can be broken down by unit, department, and team to uncover internal trends. 

The next step should be to collect and create records of this data over time. This allows us to enact and monitor the course of strategies for improvement – once we know how the organization exists today, we can discover and influence where the organization is going. If the organization can document a steady climb in representation after the implementation of a new strategy, this may mean the strategy is working and should be accelerated. In contrast, if the strategy seems to be neutral or even detrimental, the organization has a clear message that something may need to be changed or addressed. 

When this data is collected over a long enough timeline, it allows us to extrapolate from trends and create insights that can influence future actions. For example, with enough data, the organization can analyze promotion and compensation by group and yearly performance trends. Similarly, if the organization analyzes turnover by group and department or manager, we may uncover key insights to retaining talent. 

The information gleaned from talent analytics is invaluable. Talent analytics empowers organizations to plan strategically and effectively, be proactive, and customize employee experiences. It can lead to the creation of incentives, learning and development opportunities, improved retention and talent acquisition, and enhanced company culture. Ultimately, the practice of talent analytics can improve the organization and the bottom line. 

Take the complexity out of people analytics and move from reactive to proactive.  

Brittany Krynicki is a Data Scientist with TalentQuest. She has a background in research and analytics and enjoys making data exciting, understandable, and accessible. Brittany graduated with honors from Berry College and Kennesaw State University, holds certifications in data science through IBM, and is currently pursuing her master’s in data science from the University of Colorado Boulder.

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