People Analytics is used to make better decisions to create business value and improve the employee experience. To create maximum impact, insights should be easily available across the organization. However, this comes with challenges in the fields of technology, security, compliance, infrastructure and UX. The solution is Productization. Here are some key questions to ask to ensure that your solution can scale and go the distance.

People analytics can often start its life as a simple data project that soon balloons into something much larger. It is a common scenario that we see, and it usually goes like this… A business leader has a request for information that cannot be pulled from the existing HR systems. Someone in the team will volunteer – or be ‘voluntold’ – to build a dashboard. They may use Excel, or a business intelligence tool like Tableau or PowerBI. Or if they’re savvy with their statistics, they might go for a solution such as RStudio. They create a wonderful dashboard, share it with the stakeholders, and they answer the business question. Everyone is happy.

Then someone else has another request. And then another. Before too long, things start to pile up and the requests get more complex. They want more insights, more metrics, more visualizations. Then there are questions about data privacy – with all this employee data being emailed across the company – and there needs to be a proper authorization model for who can see what. The analyst has become burdened by their initial success, and there is a limit to how long things can carry on like this. Soon, their solution – and the analyst – will reach breaking point.

We have seen this scenario time and again, and we advise people to productize their solution. In the initial stages of the product, they need to think like a start-up and prioritize the business questions they will ask in their proof-of-concept stage. Then comes productization, the stage where they develop the solution so it can be used across the entire organization. This stage is more like when a start-up goes through the Series A to C, when they build the infrastructure, scale up and get more people to use their solution.

For teams that have developed their own solution, it is useful for them to think of it as a commercial product that needs to be marketed within the organization. And during the scaling stage of productization the foundations have to be set right, otherwise things may go awry later. Careful consideration needs to be given to how the people analytics team will be built, and how the data and technology infrastructure will be deployed.

At Crunchr we have developed a productization checklist so that people analytics teams can avoid becoming burdened by their success.

Here are the key questions:

Performance

• Is the solution fast enough to handle 10x more active users, while more data is being added?
• Is the infrastructure and data model ready to perform more advanced analytics and run artificial intelligence models?

Compliance

• Can the team take responsibility on data compliance and security to (internal/external) auditors?
• Does the team ensure that the results in the reporting and analytics are accurate, also in edge cases?

Operations

• Can the solution deliver on the growing list of feature requests and support tickets from the business?
• Does the organization have sufficient IT resource allocation to support the HR product and technology needs?

Continuity

• If critical team members leave, is there continuity risk in delivery to the users?
• Is there proper version control and back-ups in place as an operational safeguard?

Productizing and scaling a solution successfully across the organization requires architecture, product, security, compliance, and user experience expertise.

Asking the right questions at the beginning of the productization stage will ensure that the solution can scale effectively. Then you’ll be ready to move on to the next stage of adoption, where you can make sure that everyone in your organization embraces the solution you have put in place.