How Data Literacy Powers Inspirant Group’s ‘Unconsulting’ Team
Whether analyzing billed hours to make sure their team is maintaining work-life balance or using Agile methodology to help customers understand project timelines, the “unconsulting” team at Inspirant Group has data at the center of their decision making processes.
“Effectively interpreting and communicating how to use data helps to make informed decisions, set expectations, improve processes and planning,” said Ken Wong, an Agile transformation coach at Inspirant.
For Wong, when clients increase their data literacy, it supports long-term process improvement by empowering them to better understand opportunities and roadblocks.
“We conduct training for clients on utilizing team performance data such as average velocity and capacity to assess potential areas of improvement,” he said. “For instance, if an engineering team over the past several cycles has not completed everything originally planned, it could mean that work needs to be split into smaller pieces or the team may need another engineer to assist.”
When Inspirant Group’s healthcare clients improve their processes and data literacy, those benefits improve patient experience as well, Senior Strategic Solutions Consultant Chris Goodman told Built In Chicago in 2019.
“It’s exciting to help a company get more out of the data they already have and to help them improve their reporting metrics for the future,” Goodman said. “By providing those users with clear insights into what is and what is not working, we hope to be a small part in making healthcare more efficient for the general public.”
A foundation of data literacy is improving outcomes across Inspirant Group. Built In Chicago learned more about how Wong and his team are helping their clients build that same expertise.
Why is broad-based data literacy important for Inspirant Group?
Data literacy is key in being able to provide strategic recommendations for our clients. We demonstrate how to leverage performance data obtained from the development effort by using story points to forecast a realistic completion timeframe. This is essential in level-setting both internal and customer expectations around the product or application’s delivery timeline. If we estimate that the product takes 200 story points to fully build and the team is able to complete 40 points worth of work every two weeks, then we can anticipate that the product will be completed within ten weeks, barring any unforeseen delays.
Performance data also provides insight on the development team’s bandwidth threshold. If during a two-week development cycle, the team planned to complete 40 story points but was only able to complete 25, we can look at the reasons why. Perhaps a developer became unavailable unexpectedly or a piece of development work was more complex than originally thought. This information is the catalyst for actionable next steps.
What programs, initiatives or training did you use to promote data literacy across Inspirant Group?
We provide training workshops on Agile fundamentals, including teaching techniques of story pointing and how to estimate the relative size of work a project would need. This includes using a Burn Up Chart tool where key data points from historical team performance can be plotted on a graph to visually depict how much work is remaining against what has already been completed. This helps steer the conversation around the product or application delivery timeframe with customers and stakeholders, so they can identify how much work is left to be done and how long it will take to complete.
Being data literate has enabled our team to become more cross-functional and collaborative.”
What new capabilities has data literacy unlocked for your team?
Being data literate has enabled our team to become more cross-functional and collaborative, so that we continue producing quality recommendations for our clients. Our clients have become more predictive and accurate in forecasting delivery timelines when a product or application is expected to be completed, as compared to setting a random arbitrary date that “sounded good at the time.” They are better equipped to leverage data from their teams to improve internal processes and level-set expectations with their customers to be successful in their initiatives.