Preparing Your Data for AI, Blockchain, and other Emerging Technologies
Preparing Your Data for AI, Blockchain, and other Emerging Technologies

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? Read more here

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By: Adrian Gonzalez


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