Comment renforcer la confiance et la transparence dans la gestion des talents grâce à l’analytique ?

By analyzing historical data and trends, you can make proactive decisions and develop strategies to address emerging issues. This forward-thinking approach sets you apart as a talent management advisor who not only reacts to the present but also strategically plans for the future.

To establish trust and transparency in the talent management process through analytics, it is essential to communicate effectively. Here's how: - Clearly communicate your objectives, such as enhancing the process, increasing the number of hires, improving employee retention, etc. - Gather information from the past records and reviews. - Ask questions, gather data, and clearly define your purpose. - Regularly and clearly communicate results with stakeholders. - Continuously monitor the effectiveness of the data and make any necessary changes.

To create a transparent talent management (TM) system based on objective data, establish KPIs, ensure transparency in data collection, develop an objective evaluation system, and implement a personalized approach to talent development while monitoring employee well-being and turnover risks. Utilize emotional well-being analytics, trust panels for metrics, anonymous feedback, soft skills assessment, a transparent career advancement system, and predictive analytics for planning skill needs. This approach fosters a TM system where employees feel valued. For analytics, use Power BI for visualization, Google Cloud AI for processing large data volumes, and Google Analytics for analyzing traffic, providing insights to enhance TM processes.

Define the problem statement, what is it that you are trying to solve, it could even be a hunch/thought/observation. For example, Retaining top performers in the organization. Find out what are the variables affecting this issue. Are they directly or indirectly having either a positive or a negative impact on the outcome?

I would add to this to consider what data is already present to determine what data needs to be collected. Organisations are likely to hold more data than they realise on this topic.

First, identify key participants, such as HR, department heads, and employees, to gain diverse perspectives. Form a team with representatives from different departments to ensure objectivity in data collection. Hold a meeting to define the analytics goals and key metrics collectively. During data collection and analysis, involve stakeholders for feedback to refine the results. After completing the analysis, present the findings and recommendations while encouraging discussion and support among participants. Maintain regular communication to foster trust and confidence in the talent management processes. This approach will create a more effective and transparent system where everyone feels valued and influential.

Develop data visualization techniques that resonate with your stakeholders. Tailor your reports and dashboards to highlight information most relevant to each group's interests and needs.

The key to building trust in your talent management analytics is to understand your stakeholder's goals and needs and to show a data matrix that correlates with achieving these goals to help stakeholders make better decisions based on reliable data

Management buy-in and acceptance of the 'problem statement' is key in successful analysis (because you will need access to data, people, etc.) and then action for solving the problem at hand. Storytelling skill will go a long way to help one with this.

Regularly audit your data sources to identify and eliminate irrelevant, outdated, or biased information. Implement periodic assessments of data quality and relevance to keep your analytics up-to-date and reflective of your goals.

A large amount of time will be spent on cleaning the data that is collected or already held. Consider this when looking at what data to use and how to anonymise it.

To share your data analysis results with stakeholders, state the purpose and use the "problem-solution-result" structure. Use graphs, charts, and infographics to visualize data, and tell a story with specific examples. Avoid jargon by using simple language, and segment your audience for relevant information. Use metaphors and analogies to explain concepts, and add interactive elements with questions. Prepare a brief synopsis of findings, create a conducive environment, and be concise with bullet points. Incorporate video or multimedia, and provide references to authoritative sources. Conclude with clear takeaways and recommendations to encourage discussion.

De uma maneira bem simples, as análises precisam ser feitas realmente com as entregas e resultados. A empresa só ganha essa credibilidade quando ela demonstra que não existem ‘favoritos do chefe’ e sim avaliação do desempenho de cada um.

Consider the use of systems to support you. They could be the HR system, a specific talent analytics system or the systems used for recruitment. There are systems out there that look at potential job satisfaction rather than a pure psychometric test.

a) Providing data-driven insights to inform talent decisions and strategies, such as hiring, retention, performance, engagement, and development. b) Enabling employees to access and control their own data, and ensuring data privacy and security. c) Communicating the purpose and value of people analytics to employees and stakeholders, and involving them in the process of data collection and analysis. d) Developing ethical and responsible practices for using people analytics and artificial intelligence, and avoiding biases and discrimination.