What data should you use to make informed talent management decisions?

As the CEO of a startup, I focus on financial performance, employee turnover, leadership development, industry trends, diversity metrics, and customer feedback for informed talent management decisions.

Informed talent management decisions require a holistic view of the workforce, including Skills and Competency Data aligned with Job Descriptions, Skill inventories, and Succession Planning. Demographic information, retention rates, and Performance Metrics like KPIs, Performance reviews, and 360-degree feedback inform performance evaluation. Additionally, training, leadership potential, Employee engagement surveys, and Exit interviews enhance decisions. To stay competitive, benchmark externally with industry trends for market insights and strategic talent management."

People have biases - and yes, this includes you and me. Although there is always something that data is not able to show (context, relationships, capacity to improve peers, you name it), data about objective metrics should always be used when possible. Sometimes your best performer objectively is just someone who doesn't sell himself well (or the other, you may have a team member with bad performances but good self-promoting skills). Data will help you see cases such as these, and you'll be empowered with more tools to be fair.

I've always found it helpful to know your community and community events as well as how they impact your buisness. Weather also can play a huge role. The biggest think to pay attention to is salesbvs last year, sales vs last week or even holiday sales vs this year.

Having the best people in the right position is key to any organization. There are many people looking for jobs nowadays, but not all of them have the talent or are compromised enough to share company values. Unfair practices create a hostile environment where talented employees do not see their efforts rewarded. In the end, numbers do not lie. With all the tools available, performance can be measured. Promoting people with the best results is a fair way to demonstrate it. Taking care of your talented employees creates loyalty and respect. Some employees leave their current jobs because they find better opportunities, but some of them leave because they do not feel appreciated. Offering better benefits makes employees stay for a long time.

In my experience, data can be misleading if made the only variable basis which talent management decisions are taken. For instance, take the example of data for an employee working on 60 tickets a day with an error percentage of 10% (making an error every 7th ticket) compared to another employee working on 10 tickets per day with an error percentage of 10% (1 error every 10 tickets). In this example, the person giving the higher productivity according to the standard definitions, would bring in much more customer escalations than the other employee giving less productivity. In the end, data should be one of the factors for talent management and not the sole criteria.

Data is crucial for making informed talent management decisions because it provides objective insights into an organization's workforce. Data empowers organizations to optimize their talent pool, enhance employee engagement, and ultimately achieve their strategic goals by ensuring the right people are in the right roles at the right time.

One of the things I have found helpful during a project I was working on which required me to manage talent and employee engagement. I analysed data derived from exit interviews and implemented strategies to address shortfalls. I could achieve improved employee engagement and contributed to increased productivity.

I focus on the following: Performance Metrics: Monitor KPIs and productivity for insights into top performers and areas to improve. Employee Feedback: Gather feedback to gauge engagement and well-being. Skills and Competencies: Analyze workforce skills to bridge gaps and guide development. Turnover and Retention: Track data to identify trends and address root causes. Recruitment Data: Optimize talent acquisition with insights into sources of hire, time-to-fill, and cost-per-hire. Diversity and Inclusion: Foster diversity and inclusion using relevant metrics. Market Trends: Stay competitive by staying informed about labor market trends and salary data. Succession Planning: Identify high-potential talent and plan for key role transitions.

One of the things I have found helpful during a project I was working on which required me to manage talent and employee engagement. I analysed data derived from exit interviews and implemented strategies to address shortfalls. I could achieve improved employee engagement and contributed to increased productivity.

Organizations should collect a diverse range of data pertaining to their employees. This data includes performance metrics, such as individual and team performance reviews, productivity and output measurements, and skill assessments. Demographic data can help identify diversity and inclusion gaps. Training and development records can inform decisions about skill gaps and potential areas for growth. Data on turnover rates and exit interviews can provide insights into the reasons for employee departures. Combining these diverse data sources allows organizations to make informed talent management decisions that enhance workforce productivity, engagement, and long-term success.

Managing talent effectively begins with comprehending data. Analyzing data is more than just a skill. Find the measurements that unlock data's power. Evaluate factors like employee satisfaction scores, performance data, and attrition rates. Making wise decisions requires these insights. The goal of data analysis is to identify patterns and trends, not just numbers. Put into practice various techniques like cohort comparisons, trend analysis, and predictive modeling. These methods can offer priceless insights on personnel management initiatives. Once you've analyzed your data, don't stop there. Measure the impact of your talent management strategies. Continuously refine your approach based on data-driven findings.

Analyzing data to make informed talent management decisions involves a systematic process of collecting, processing, and interpreting relevant information about an organization's workforce. It begins by identifying key performance metrics such as employee retention rates, performance reviews, skill assessments, and employee satisfaction surveys. By leveraging data, organizations can make more informed decisions about hiring, promotion, training, and other talent management strategies, ultimately leading to a more effective and competitive workforce.

Hi All, Just adding some details which may add value to the question asked, • PMS data of employee, • Pre and Post employment Surveys • Select process details, • Employee Survey, Feedbacks, Engagements • Employee Welfare data • Team performance, Employee Collaboration data • KPIs • Labor market data – Globally • Employee development program data • Cost benefit Analysis • Retention Analysis • Exit Interviews Analysis

All data related to competencies and performance makes sense only with the context. Ensure you don't make conclusions and decisions based exclusively on dashboards and numbers. Use the data analysis only to highlight the areas where you need to look closer, ask questions, collect facts and learn the context. There are a few reasons for that: - The collected data is discrete ("Knows", "Doesn't know", "Achieved X points", etc.). Factual data is more often not discrete. What do we count as "He/she knows"? Do test results indeed reflect the real knowledge? How do we measure Experience? etc.; - The data analysis assumes a person works by strict, clear, straightforward instructions, like robots. But this is not the case either.

sing data to make informed talent management decisions involves collecting and analyzing relevant information to better understand and optimize an organization's workforce. This process typically starts with gathering data on employee performance, skills, and engagement, as well as external data like market trends and competitor benchmarks. Data-driven talent management helps organizations make more precise, objective, and strategic decisions, ultimately leading to a more effective and competitive workforce.

In my experience, Talent Management should have the below criteria for decision making, mentioned in order of preference from high to low: 1. Data & Analytics — Looked at with the right context 2. Professional Attitude - Basis one-2-one meetings and feedback from the team/partners/stakeholders about the individual to determine potential, improvement areas, and willingness to listen and work on feedback’s given and improvements observed as a result. 3. Miscellaneous: Can be linked to external factors like family issues, workplace environment, etc. To be used to put the data available and attitude determination in context.

Talent management is not just about people but it also talks a lot about the processes of the organization since it influences people directly. In order to take the data the best place is the internal repository where data about retention, exit interviews, productivity etc remain stored. That data can then be analysed and made into decisions for any changes . In addition, taking direct feedback with the employees, leaders and in direct stakeholders will also help to address any issues and make the changes.

You also need to consider some key aspects: 1. Data Quality: Ensure data is accurate and up-to-date for reliable insights. 2. Ethical Use: Comply with privacy regulations and ethical standards when handling employee data. 3. Technology: Invest in data management tools and systems to streamline data processing. 4. Continuous Learning: Foster a culture of learning based on data insights. 5. Data Security: Protect sensitive data with robust security measures. 6. Cross-Functional Collaboration: Collaborate across departments for seamless data flow. 7. Balancing Data Types: Combine quantitative and qualitative data for a holistic view. 8. Benchmarking: Compare data with industry standards to gauge performance.

To make informed talent management decisions, use data such as performance metrics, employee feedback, skills and competencies assessments, succession planning data, retention rates, recruitment effectiveness, diversity metrics, training impact, feedback from managers, industry benchmarks, employee engagement scores, time and attendance records, and advanced workforce analytics. This comprehensive data helps in recruitment, performance management, and succession planning for better talent management.

By collecting, analyzing, and integrating these various data sources, organizations can make well-informed talent management decisions that align with their strategic objectives and ensure they have the right people in the right roles to achieve their goals.