22 Explore & Engage: Analytical Skills

Even with data and statistics, the insights won’t just find themselves. For that, you need to pair the following techniques with your descriptive analytics skills:

• Identify key metrics: Understand what questions you want to answer with your data. Before you jump into running different statistics, start by asking yourself, what do I want to know? What are the best metrics to use? A great approach is to first research common analytics used for your chosen topic. Things like headcount, attrition, well-being, collaboration, innovation, hiring, etc. have all been analyzed before. So look for examples from others and try to replicate them. Start with a goal for what you hope to describe, then apply analytic techniques until you feel you have a clear answer.
• Learn to interpret: Simply calculating a statistic or producing a chart won’t lead to action. With each analytic skill you learn, go beyond simply learning how to calculate or create it, learn to interpret and explain it effectively – analytics require context-relevant translation to be meaningful.
• Practice with real data: If you have access to people data files, do all your learning and practice on them! If not, you can find open datasets related to human resources and business topics that include mock or anonymized people data from online platforms like Kaggle and many university’s public data repositories.
• Collaboration is Key: Partner with analysts, qualitative researchers, visualization experts, data scientists or AI specialists within your organization. Learn from their expertise and leverage their insights to enhance your people analytics capabilities.
• Learn foundations and stay up to date on trends: The field of analytics is based on statistical foundations that are long-standing and do not change; making it possible for you to build a solid foundation. But it also is rapidly evolving in terms of how foundational approaches are being applied and new approaches and techniques are emerging rapidly. Subscribe to industry publications or follow thought leaders on social media to stay ahead of the curve.
• Embrace Experimentation: Don’t be afraid to experiment with new tools and techniques. Just be careful about what you put into the tools that you experiment with – NEVER put company or employee data into a technology tool (especially not a Generative AI tool) that wasn’t explicitly built for HR data privacy!!!
• Start Small and Specific, then Scale Up: Begin by applying analytics to a very specific people analytics challenge. For example, start by running basic descriptive analytics on quantitative survey data and move along to try diagnostic, predictive, prescriptive, or cognitive approaches as you go. You might also simultaneously start learning to code the open-ended responses to that same survey using sentiment (sentiment analysis) before moving on to coding multiple types of themes (thematic analysis) and you would want to have a grasp of simple text analytics before branching into more advanced natural language processing approaches.