How do you maintain data quality in talent analytics?

1 Define your data sources

Before you start collecting and analyzing data, you need to define your data sources and how they relate to your talent objectives. For example, you might use data from your HRIS, ATS, LMS, surveys, assessments, performance reviews, or other systems. You need to understand what each source can provide, how reliable and accurate it is, and how it fits into your overall talent analytics framework. You also need to ensure that your data sources are consistent, compatible, and compliant with data privacy and security regulations.

2 Clean and validate your data

Once you have your data sources, you need to clean and validate your data to remove any errors, duplicates, outliers, or missing values. You can use various tools and methods to do this, such as data cleansing software, data validation rules, data quality audits, or data governance policies. You also need to establish a process for updating and maintaining your data regularly, and for resolving any issues or discrepancies that might arise. You want to ensure that your data is complete, accurate, and relevant for your talent analytics purposes.

3 Integrate and standardize your data

After you have cleaned and validated your data, you need to integrate and standardize it to create a unified and coherent view of your talent. You can use data integration platforms, data warehouses, or data lakes to store and manage your data from different sources. You also need to standardize your data formats, definitions, and metrics to ensure consistency and comparability across your data sets. For example, you might use a common terminology for skills, roles, or competencies, or a common scale for measuring performance or engagement. This will help you avoid confusion and ambiguity in your data analysis and reporting.

4 Analyze and visualize your data

The next step is to analyze and visualize your data to generate insights and recommendations for your talent management. You can use various tools and techniques to do this, such as descriptive, predictive, or prescriptive analytics, dashboards, charts, graphs, or reports. You need to choose the right methods and formats for your data and your audience, and to ensure that your analysis and visualization are clear, concise, and actionable. You also need to validate and verify your results, and to test your assumptions and hypotheses with data.

5 Communicate and act on your data

The final step is to communicate and act on your data to improve your talent outcomes. You need to share your insights and recommendations with the relevant stakeholders, such as managers, leaders, or employees, and to explain the value and impact of your data. You also need to solicit feedback and input from your stakeholders, and to incorporate their perspectives and preferences into your data-driven decisions. You need to monitor and measure the results of your actions, and to adjust your data strategy and tactics as needed. You want to create a culture of data literacy and trust in your organization.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?