One of the biggest mistakes companies often make is chasing after the next, new shiny technology application without first clearly defining the business problem or opportunity they’re looking to address — and without first getting their data in order. Why do companies still struggle with data quality management? How do you get over the change management hurdles? What are some best practices for getting the most value from emerging technologies? Those are some of the key questions I discussed with John Richardson, Vice President of Supply Chain Analytics at Transportation Insight, during a recent episode of Talking Logistics.
Productivity vs. quality in data management
I began our discussion by asking John why, with all of the great new technology out there, data quality continues to be a stumbling block to realizing their real value. John pegs the heart of the problem as where the incentives are for data capture. He notes that, “In general, companies don’t have incentives and measures around data quality. The people responsible for data capture are usually incentivized on productivity rather than quality, and they don’t understand the downstream impacts of poor data quality.
“For example, if the dimensions of a package are entered wrong in the item master, it can impact how space is allocated in the warehouse or how a load is built for transportation. This can cause a lot of problems. People don’t understand the effects it can have.”
Change management
Changing the way companies measure and incentivize data quality brings up the age-old issue of change management. I asked John how companies can overcome the “we’ve always done it this way” syndrome. John says, “Too often people think of change as ‘I haven’t done my job right.’ Therefore, change has to be non-threatening and incremental. People fear change because they are afraid of failure. You have to create an environment where it is okay to fail because that is how people learn.
“Sometimes what it takes is to bring in a third party who can ask the ‘stupid questions’ like ‘why are you doing it that way’ to make people rethink their practices. Too often people are not incentivized to challenge the status quo and that’s why it makes it hard for companies to change.”
What data is valuable?
With IoT and other sensors, we’re drowning in data. So, how do you determine which data is important? John comments that it comes down to how you use it. “You want metrics that are actionable, that you can use to drive your business. You also want to make sure you’re measuring the right things and focusing on metrics everybody can get behind. Sometimes metrics and incentives in one part of the business can be at odds with incentives in another part of the business. For example, a plant manager is incentivized on pounds of production, so he’s focused on cranking products out. But the warehouse manager is getting measured on cases of inventory in storage and how much he’s shipping. So you have a conflict there. You have to work those things out so it is equitable for everyone so you’re not working at cross purposes.”
Sharing data
Supply chains by their nature are multi-company networks. You have to share data with other parties, but companies are sometimes reluctant to share “too much.” John thinks the current environment is changing that as companies are trying to recover from the COVID-19 pandemic. “There is a feeling that we are all in this together.
“But there are ‘black holes’ of missing data in our supply chains. If you’re going to be a good partner and understand your end-to-end supply chain to do risk assessments, you have to be as transparent as you can. You have to get beyond the idea that the data is ‘secret’ to form good partnerships. This can be especially important for things like tracing the movement of goods from origin to final destination in order to assess risks and respond to disruptions more quickly and effectively.”
Getting ready for AI, Blockchain and other new technologies
So, how do you get your data processes ready for the new technologies? John says, “It starts with creating a culture of data quality management. You have to train people at the lowest levels on the importance of data quality and how the data will be used so they understand the impacts. For these emerging technologies to provide value in the supply chain, you have to get to the point where you no longer question data quality. Too often we see companies cleaning up data throughout their processes. But with these technologies, you’re turning data over to machines to interpret it, so you definitely want to give those machines the best information that you can because you’re losing a little bit of human judgment as you look at the results.”
Leveraging data
How should companies leverage data to drive business results? Why did a tire company wrap its tires? Why did Winston Churchill create the Special Operations Executive during WWII? (Yes, it’s related to this topic). I recommend that you watch the full episode for John’s insights and advice on these questions and more. Then keep the conversation going by posting your own thoughts and experiences on this topic.
