What is your approach towards an HR data analytics framework?

Great question.  I am an HR Data Analyst with 25+ years of experience, the last 5 of which have been focused on HR data analytics and business intelligence.  I agree with the responses that others have submitted to this question.  Since my role is at the individual contributor level, my thoughts on this topic are pragmatic in nature.  Having worked with multiple HRIS platforms, data query tools, and BI applications, I subscribe to a perspective that has been advocated by data analytics thought leaders such as Vin Vashishta.  Here are my suggestions, in no particular order:  identify important questions you want the framework to answer; create the simplest model / application that provides timely and accurate answers to these questions; develop and implement tools that allow you to monitor how the analytics tools are being accessed / utilized; collect anecdotal and quantitative feedback on how the data analytics insights are creating tangible benefit to the organization; communicate these success stories to the organization for the purposes of enhancing leadership trust of the tools and bolstering the overall data literacy of the organization.

Crafting an effective HR data analytics framework requires a delicate balance between understanding human behavior and leveraging analytical prowess. It's crucial to blend qualitative insights with quantitative metrics, going beyond mere numbers to comprehend the intrinsic motivations and dynamics of individuals within an organization. My approach emphasizes the convergence of HR expertise with data fluency, wherein we don't just analyze trends but delve deeper to unearth the human narrative behind the numbers. It's about building frameworks that not only streamline processes but also embrace the human aspect—aligning organizational objectives with the well-being and aspirations of its workforce. Ultimately, this framework aims to foster a culture of continuous improvement, where data-driven decisions complement the humane essence of effective HR management.

Often HR data - reports and dashboards are governed by HR hierarchy and reporting structure. It is important when planning for HR analytics, you inherit the hierarchy from the HCM system.  As for the content of the HR analytics, you need to plan to integrate the data from disparate systems when core HR, payroll, benefits, recruitment, performance management, LMS etc are not in the same HCM system. 

Creating an effective HR data analytics framework involves a blend of precision, adaptability, and empathy. Initially, it's crucial to identify the key metrics that align with organizational goals—factors like employee engagement, retention rates, and performance indicators. Simultaneously, a holistic approach is essential, ensuring that data analysis doesn't merely interpret numbers but also comprehends the human element behind those figures. Empathy in HR analytics involves understanding how data translates to real-life scenarios, recognizing patterns and trends without losing sight of the individuals behind the statistics. Moreover, an adaptable framework should evolve, embracing new technologies and methodologies as the HR landscape continues to transform. Ultimately, the goal is to use this data not just for assessments but to strategize and foster a thriving, people-centric workplace culture.

Work in progress with my current company but I am used to have an analyst who does not only the data crunching but also the 'what does this tell us' and build correlations between different data sources and outcomes. PowerBI is amazing to do this.