Poor data quality is the Achilles’ heel of supply chain management (see “The Big (Crappy) Data Problem In Supply Chain Management.”) This is especially true for data received from suppliers, logistics service providers, and other external trading partners.
How would you rate the overall quality of the data you receive from your external trading partners? Has the quality of your supply chain data been improving or getting worse?
Back in June 2024, we asked those questions to members of our Indago supply chain research community, who are all supply chain and logistics executives from manufacturing, retail, and distribution companies.
Three quarters (75%) of our member respondents rated the overall quality of the data they receive from their external trading partners as either “Average”” (54%) or “Poor” (21%). None of the respondents rated it “Excellent.”
On a more positive note, more than half the respondents (54%) said that the quality of their supply chain data has been either “Improving” (50%) or “Greatly Improving” (4%) in the past 3 years; only 8% said that it was “Worsening.”
Who is primarily responsible/accountable for supply chain data quality management at your company? Part of the problem, as the chart below shows, is that nobody is formally accountable at many companies.
“The root cause of poor data quality is that it is segmented and siloed, giving an incomplete view of what is going on,” said one Indago supply chain executive.
Here are some other comments from our Indago executives:
“Our company has created a centralized Data Governance team to document accountability across all data inputs. Data is not just a supply chain issue, it’s an end-to-end business one.”
“Data quality issues are as much (or even more) an internal problem than an external one. Data management is not currently an area of focus for us, but that is where the primary actions for improvement reside.”
“Data and the management of data have become impossibly complex, especially in large global organizations. No single group has full responsibility or even understanding of the data sources, job runs, etc. End business users are even more in the dark and have no way to understand or troubleshoot when inaccuracies are found. When the data is there and available, it’s a wonderful thing and we can harness it to drive powerful insights, but when it’s not, we’re back in the dark ages.”
“I am with a new company now, and the issue here (and maybe with others) is the lack of ownership. At this point in time, IT owns data quality management which does not work; buyers/supply chain areas [need to own it]. As we all know, it is tough when the people dealing with the data coming in do not understand the long-term pain of inaccurate or missing data.”
For additional insights from the research, including which component of data quality (Accuracy, Timeliness, or Completeness) requires the most improvement from trading partners, Indago members can download the report from our website.
What are the root causes of poor data quality? What are the main challenges to improving it? What actions, if any, are you taking to improve data quality?
Post a comment and share your perspective!
