Blog Post

Blackbaud's Journey to Build an Effective HR Data Management Plan

Cornerstone Editors

An Economist Intelligence Unit found 82 percent of organizations planned to either begin or increase their use of big data in HR before the end of 2018. While it's an important step for any organization, creating and executing a successful HR data management plan is a huge undertaking; one that requires a lot of careful planning and know-how to ensure things go smoothly.

This was exactly the challenge Blackbaud—a cloud-software company that provides online tools to organizations powering social good—faced in 2015. After experiencing rapid growth, the company quickly realized that continuing to scale would require a centralized place where they could view data and insights from all 18 of their core products.

"The goal was to pull all of our systems together and create a team of people that could make informed decisions for the business based on their knowledge of all of the different systems," says Steven Infinger a senior business analyst on the educational services team at Blackbaud.

For example, Infinger's team was was responsible for providing training and certification to customers for all of Blackbauds products. As the company grew, they saw the number of people taking their product certification training course skyrocket from 60 certifications in 2014 to over 4,000 (and growing) in 2018. With the right analysis, they knew data could help to better tailor training programs for their customers, automate manual processes and boost operational efficiency.

But before the company could transform years of collected data into actionable business decisions, they needed to implement a solid HR data management plan. And while Blackbaud is still in the early stages of this process, the company is already seeing results from their approach.

Fixing What's Broken

To create an HR data management plan, Blackbaud leveraged Lean Six Sigma, a methodology for process improvement that aims to eliminate extra steps and reduce the probability that an error will occur. There were five main steps: 1) Define the problem, 2) Measure the problem, 3) Analyze and identify the cause of the problem, 4) Improve and implement a solution and 5) Control to maintain the solution.

To define the problem (step 1), Infinger's team set up interviews with the different product teams to get a better understanding of the processes they had in place for collecting data. Not only did they find that there were a lot of repetitive steps, they also discovered the first thing they would need to do is clean up the data they had in order to make it usable.

In order to clean, sort and analyze their data, Blackbaud is leveraging a few different strategies including: using custom fields in their Cornerstone learning management system to help fill gaps in data, standardizing processes and using filtered templates. Through these actions, Blackbaud ensures data reliability, decreases manual input and can narrow down redundancies to single sources of data.

Once the data is clean, Infinger and his team are hoping to use it to answer questions such as:

  • What is the average time it takes a customer to take a training after buying the product?
  • Once a customer takes a training how likely are they to get certified?
  • Is there a correlation between certification and renewal?

The Road Ahead

One of the biggest challenges in creating an HR data management plan has been meeting the needs of stakeholders in a timely manner—the entire project is slated to take about two years to complete. "We have a lot of people who are curious," Infinger says. "If our bigger team wants to use insights from the data to make a decision for a customer, we have to hold them off and say, 'that's a great question, but don't go digging for it because you may pull something that's not an accurate answer.' They have to be patient."

Using the Lean Six Sigma approach and mapping out the process of creating a data management plan from start to finish has been a key exercise in revealing inefficiencies and ultimately creating a better way of functioning in the future. Infinger believes that quick wins, such as the strategic use of custom fields that make it easier to find and pull data, will help support bigger wins down the road.

Infinger says getting meaning out of your data is critical for any business, whether that's revenue information, transactional data or user data. "Really look at your process and be open to change," he advises. By questioning, rethinking and revising existing processes, Blackbaud is following a holistic data management plan—and is well on its way to making data-based decisions.

Photo: Creative Commons

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