written by Lotte Van der Sijpt.

How can you make sure valuable analyses do not have to be repeated every time, but take the next step and make data consistently available whenever HR needs it?

In our latest webinar, Caitlin van Mil, Lead Data Scientist in the Workforce Intelligence team at Wolters Kluwer, introduced us to the world of “productizing HR analytics”. An approach that turns analyses into reusable applications, allowing HR and the business to access insights themselves whenever they need them.

Traditional HR analytics often follows the same sequence: the business asks a question, an analyst gathers and prepares the data, carries out the analysis and shares the results. This process can easily take several weeks, especially when data has to be extracted from different systems and cleaned up each time.

With productizing, the aim is to automate as many of these recurring steps as possible and make the analyses directly available to the end user.

Treating HR analytics as a product can take this further. Think of a workforce planning tool that lets managers compare different staffing scenarios themselves. Or an application that matches employees to open vacancies based on their skills. Even that Excel file where you keep rebuilding your pivot tables can be automated further: you simply add the data, and everything else (cleaning, calculations and updating pivot tables) happens automatically.

Does this mean dashboards are a thing of the past? Certainly not. Dashboards remain very valuable for presenting HR figures and trends clearly. But when a business question calls for more flexibility, interaction or advanced analysis, a dedicated analytics product can offer more possibilities.

The essence of HR analytics as a product? Develop insights so they remain available and can be reused, instead of producing the same insights over and over again.

Why invest time in developing an analytics product? According to Caitlin, there are two key benefits.

1. From waiting weeks to insights on demand

A traditional HR analysis involves a lot of preparation. Collecting, cleaning, analysing and visualising data, then discussing it with the business: all these steps take time.

By building recurring analyses into a product, this process no longer needs to be repeated each time. Users can, for example, run an analysis themselves or generate a report with the latest insights at the click of a button. The analyst no longer has to repeat all the preparatory steps and can focus straight away on interpreting and discussing the results. That is much faster.

2. Getting answers faster and asking better questions

A second challenge is that the business does not always know exactly which analyses are possible or which question it should be asking.

Suppose an HR manager wants to know how the average engagement score differs between departments. What that manager may really want to know is whether the differences are statistically significant and which factors might explain them.

A key challenge for HR analysts is therefore to uncover the “question behind the question”. But when advanced analyses are directly available in a user-friendly tool, the business can also discover for itself which insights are possible, without needing to know all the statistical techniques involved. Productizing therefore makes HR analytics both faster and more accessible.

At Wolters Kluwer, too, HR reporting used to rely largely on its HR system, Workday, and Power BI. To refresh a report, analysts had to download data from Workday, clean it where necessary and load it back into Power BI. Analyses were also limited to the data and functionality offered by those systems.

To overcome these limitations, the Workforce Intelligence team built a central data environment in Snowflake. It combines Workday data with other data sources and refreshes the data automatically. This creates a more flexible foundation on which the team can develop its own analytics products.

One concrete example is their redesigned Workforce scorecard: an application that lets users monitor key HR KPIs. While the original scorecard largely followed the capabilities of Workday, the new version offers a range of additional features:

• More flexible filtering: users can filter not just by organisational structure, but also by country or region, for example.
• Combining more HR data: engagement figures can now be included too, even though they were not directly available in Workday.
• Deeper analysis: users can click through from a KPI to the underlying tables and trend charts.
• Easier reporting: the scorecard can be exported directly to PowerPoint.

The result is more than just a new dashboard with the same figures. Wolters Kluwer is building an application that better addresses business questions and gives users more ways to explore insights independently. This work is still very much in progress, and we are already keen to see the results.

Ready to get started with productizing? According to Caitlin, it starts with a clear business question and a reliable data foundation, rather than technology. A few key steps:

• Ensure your data is reliable: identify the right data source for each data element (the source of truth), agree on clear KPI definitions and ownership, and monitor data quality.
• Make data available automatically: bring the necessary data sources together in a central data environment so that analyses and applications always work with up-to-date data. You do not need to connect all your HR systems at once: start with the data you need for your most important business questions.
• Start with the end user: which recurring questions do you want to address? Which features are missing from your existing systems? And who will use the product?
• Bring the right expertise together: alongside data analysis and, where relevant, data science, you also need expertise in data engineering, application development and user interface and user experience design (UI/UX).

A successful analytics product brings together reliable data, sound analyses and user-friendly features. It should address a recurring question, be reusable without assistance and fit into the user’s day-to-day working environment. Think of an application available directly through Microsoft Teams, rather than a separate platform that employees have to keep looking for. And as business questions continue to evolve, the product needs to be able to grow with them.

So where does AI fit into this picture? According to Caitlin, AI and Workforce Intelligence can complement each other very well, but each has different strengths today.

AI is particularly good at answering questions and automating tasks. But a correct answer to the wrong question still offers little value. Suppose an HR manager asks AI to calculate the correlation between two variables. AI can perform that calculation, but it will not necessarily question whether correlation is the most appropriate method for investigating the underlying business problem.

This is where the expertise of HR analysts and data scientists comes into play again. They know the organisational context, understand the limitations of the data and can critically assess which analyses are relevant.

According to Caitlin, the biggest opportunity therefore lies in integrating AI into analytics products. For example:

• Automatically generated insights: AI that adds a clear explanation to a chart, tailored to the user’s filters or selections.
• A built-in AI assistant: a chatbot that helps users interpret analytical results. For example, by explaining what a statistically significant difference means or how to understand a p-value.

In this way, AI can make advanced HR analytics even more accessible. It can support analytical thinking by making insights easier for the business to access and understand, without taking over that thinking entirely.

The key takeaway? Start small and build with purpose. Not every HR question needs a separate application. Sometimes an existing dashboard or your HR system is enough, while other, more complex or recurring analyses benefit more from a dedicated analytics product.

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