Understanding Data Quality
There is an interesting article in the Harvard Business Review highlighting how pervasive the problem of poor data quality has become. The implications of this problem for an organizations Facilities Data are truly sobering.
Through their research, they have discovered that “only 3% of companies’ data meets basic quality standards”. And the standards that the data was being evaluated against were developed by the companies in the course of the study. “On average, 47% of newly-created data records have at least one critical (e.g., work-impacting) error. A full quarter of the scores in our sample are below 30% and half are below 57%”.
The Financial Impact
As a way to estimate the financial costs of bad data, the authors cite the approximation of the “rule of ten”. This rule states “it costs ten times as much to complete a unit of work when the data are flawed in any way as it does when they are perfect.”
From the HBR article:
Think of the implications of this study on the costs of facilities occupancy, maintenance, and operations. Your facilities are your largest financial asset and likely the second largest expense on your income statement behind salaries and wages. Facilities managers focus the efforts of their teams based on key performance indicators (KPIs) such as occupancy rates, density rates, average age of work orders, deferred maintenance backlog, etc.
“If the basic data supporting these KPIs is flawed, then the decisions informed by those indicators are compromised as well.”
Getting Serious about Data Quality
At PenBay, we take data quality very seriously. With each software release, we introduce new enhancements to identify and report on potential data quality problems. Our data interoperability tools automate the cleanup of common data quality issues in the process of the data interoperability workflows. Data filters and reports identify problem records that may need user intervention to fix an identified problem. These data quality assessment tools help you quickly find facility data issues, address them, and avoid the 10x cost of relying on incomplete or flawed data.
We are always interested in hearing from you about how to make InVision most impactful for your organization. What are your most challenging facilities data quality problems? How can we improve InVision to address those challenges?