• Written by Azhar Aliyar, Director

Can we really answer questions about staff turnover just by querying the HR database, or is there more to it than that? The truth is, getting accurate and useful answers from our data depends on a number of underlying factors, and it's not just a matter of asking the right questions. In this article, we'll explore the data foundations that need to be in place before we can start getting meaningful insights from our human resources data.

Field-level meaning and why column names are not documentation

The first hurdle to overcome is ensuring that our data fields have clear and consistent meanings. This may seem obvious, but in practice it's often a challenge. Column names like "Employee Status" or "Job Title" may seem self-explanatory, but without further context, it's difficult to know exactly what they represent. For example, does "Employee Status" refer to the employee's current job status, or their employment status with the company as a whole? Without clear documentation, it's easy to misinterpret the data, leading to incorrect conclusions.

This is why column names are not sufficient documentation on their own. We need to have a clear understanding of what each field represents, and how it relates to other fields in the database. This requires a thorough understanding of the data model, as well as the business processes that generate the data. Only then can we start to build meaningful queries that give us accurate insights into our human resources data.

The systems that get left out of scope and the partial answers that follow

Another common pitfall is to focus too narrowly on a single system or database, without considering the broader ecosystem of HR systems and processes. For example, we may have a robust HR information system (HRIS) that tracks employee data, but if we're not also considering data from other systems, such as performance management or recruitment software, we're only getting a partial picture. This can lead to incomplete or inaccurate answers to our questions, which can be misleading or even damaging.

A good example of this is when a category manager compares two suppliers, they need to consider not just the pricing and product information, but also the supplier's performance history and any relevant certifications. By taking a more holistic approach to our data, we can get a more complete understanding of our human resources landscape, and make more informed decisions as a result.

The human resources data this depends on: policy library, headcount and skills, attrition history, hiring plan

So what specific data do we need to have in place in order to start getting meaningful insights from our human resources data? There are several key areas to focus on, including our policy library, headcount and skills data, attrition history, and hiring plan. Our policy library provides a foundation for understanding our HR policies and procedures, while our headcount and skills data give us a snapshot of our current workforce. Attrition history and hiring plan data, on the other hand, help us understand trends and patterns in our workforce, and make more informed decisions about recruitment and retention.

For instance, a shift supervisor checks the maintenance log to ensure that all equipment is properly maintained, and that any issues are addressed promptly. Similarly, our HR data needs to be properly maintained and updated in order to ensure that we're getting accurate and useful insights. By prioritizing data quality and integrity, we can build a stronger foundation for our HR analytics, and make more informed decisions as a result. You can learn more about human resource management and how it can benefit your organization.

What it changes about: the same policy questions every week and planning that runs on impressions

By having a robust data foundation in place, we can start to answer common policy questions in a more systematic and data-driven way. Rather than relying on intuition or anecdotal evidence, we can use our data to inform our decisions and drive our planning. This can be particularly powerful when it comes to areas like recruitment and retention, where small changes can have a big impact on our bottom line.

For example, rather than relying on impressions or assumptions about why employees are leaving, we can use our attrition history data to identify trends and patterns, and develop targeted strategies to address them. By taking a more data-driven approach to our HR planning, we can reduce the risk of errors or missteps, and make more informed decisions that drive real results.

Measured with: repeat question volume, time to answer, attrition by tenure

So how do we measure the effectiveness of our HR data foundation, and ensure that we're getting the insights we need to drive our business forward? There are several key metrics to focus on, including repeat question volume, time to answer, and attrition by tenure. By tracking these metrics, we can get a sense of whether our data foundation is providing the answers we need, and identify areas for improvement.

By regularly reviewing these metrics, we can refine our approach to HR analytics, and ensure that we're getting the most out of our data. Whether we're looking to reduce turnover, improve recruitment, or simply get a better understanding of our workforce, having a robust data foundation in place is essential. If you have any questions about how to implement this in your organization, you can contact us for more information.

What this does not do, or when it is the wrong choice

While a robust data foundation is essential for getting meaningful insights from our HR data, it's not a silver bullet. There are certain situations where it may not be the right approach, or where it may not provide the answers we need. For example, in situations where the data is highly complex or nuanced, a more qualitative approach may be needed. Alternatively, in situations where the stakes are very high, or where the decision has significant ethical implications, a more careful and considered approach may be required.

In these cases, it's essential to take a step back and consider the broader context, rather than simply relying on our data foundation to provide the answers. By taking a more thoughtful and nuanced approach to our HR analytics, we can ensure that we're getting the insights we need to drive our business forward, while also being mindful of the potential risks and limitations. This requires a deep understanding of the data, as well as the business processes and systems that generate it.