“Garbage in, garbage out” is probably the oldest saying in computer science. It means that flawed input data to any computer program will produce nonsense output. Legend says it was first used in 1957, at the dawn of information technology. Yet, it applies even more today.
As companies develop their digital roadmap, they are realizing that the future of supply chain is really about data. In the Jumpstarting Your Digital Roadmap report, we discussed that 70% of enterprise leaders believe they have access to most of the data from the end-to-end supply chain. This data includes advanced shipping notifications, GPS traces, production flow signals, quality test logs, point of sales data, connected products telemetry, social listening, and newsfeeds.
Yet today most organizations lack quality data, and interoperability of data sets across different trading partners is often totally missing. Over 40% of supply chain leaders agree that their available data is difficult to correlate because it is siloed in a number of different reporting tools and systems, or simply because it is bad data. In this situation, technologies such as big data analytics and control towers are useless, so investing in them would be a waste of money.
In his book, “Bad Data Handbook: Cleaning Up The Data So You Can Get Back To Work,” author Q. Ethan McCallum tries to nail down a definition of bad data: “It includes data that eats up your time, causes you to stay late at the office, drives you to tear out your hair in frustration. It’s data that you can’t access, data that you had and then lost, data that’s not the same today as it was yesterday … In short, bad data is data that gets in the way.”
The bottom line is that today’s organizations don’t know which data to trust. Companies cannot embark on any serious digital transformation with bad data. Sound familiar?
Most supply chain leaders are oblivious to how bad data hampers their supply chain performance and cost. Fragmented and inconsistent data delays time to market, increases forecast error, produces wrong production and inventory planning, affects cross-sell/upsell opportunities, and creates inefficiencies.
Supply chain leaders should use master data management to get rid of bad data and succeed in supply chain digitalization. Yet it’s a process that most supply chain functions have overlooked for so long, with investments still insignificant.
Scanning through our library, it is not unusual to come across case examples of companies that acknowledge the essential value of master data management as part of supply chain transformation initiatives.
In his End-to-end collaboration to drive customer service levels webinar, the global director of supply chain planning at cash-and-carry retailer Metro highlights how master data management has traditionally been considered a low-value administrative task. As the company embarked on a thorough transformation of the organization into an end-to-end collaborative supply chain, it incorporated master data management as one core process component to ensure it could seamlessly connect customers with suppliers.
The purchasing development executive at Rolls Royce in his webinar, Enabling e-business through global transformation, shared how it is difficult to get buy-in from senior supply chain leaders to commit resources to master data management. It is especially hard to get buy-in for resources for cleaning up data — it is not a sexy task. To break this spell, this executive had to provide concrete examples of what the business benefits would be if master data was sorted out. The business case was compelling, so master data management is now fully integrated.
With data quality and consistency becoming critically important factors in supply chain performance, supply chain leaders must pay more attention to master data management. Effective master data management revolves around data accountability and ownership, which must be clearly defined and in the hands of supply chain leadership. Supply chain organizations need to be responsible for a set of master data about customers, products, locations, vendors, and suppliers.
Companies also require a sound process and tool in place to manage the workflow, the moment a change in data becomes necessary. Everyone contributes, but the entire process should be managed and controlled by a central team. Standards are also important; data must be structured in a way that allows for a consistent approach, over time and across the end-to-end supply chain.
As with Metro and Rolls Royce, master data management must become a core to your supply chain process.