Sonya Badigian

Summary

NEED

Procore, a construction project management software, was rolling out an advanced analytics tool that aimed to make smart recommendations about project management & budget, based on the existing data that people were entering into Procore.

Designers & project managers realized that this tool would only be helpful if data was being entered into the tool consistently - ie, high data quality leads to accurate predictions and recommendations. Internally, they were mainly imagining formatting/validation issues might be the cause of data quality problems from our customers’ perspectives.

I was brought in to help understand our customers’ desires & challenges around data quality, and what was standing in the way of high data quality as they defined it themselves.

PROJECT DESIGN

I recruited for and conducted a series of in-depth interviews with various types of Procore customers who might be engaging with and managing data quality at their organizations, large and small.

RESULTS

As a result of these interviews, I was able to create definitions of good data quality that were much broader than simply looking at formatting and data validation. The results helped us understand customer pain points around data that we wouldn’t have expected; for example, how workforce turnover can lead to decreased data quality on a construction site. I was also able to pinpoint three specific tools within our product that needed improvement with regard to how they mediated data entry.

IMPACT

Insight into how our customers are engaging with their level of data maturity:

• Increased empathy for our customers whose low data quality was preventing them from taking advantage of our analytics offerings.

• Highlighted some huge pain points in the structure of our offerings that were preventing customers from being able to focus on data quality.

• Think about how we can educate customers within the tool; not just during onboarding.