Today, massive amounts of data are being collected, but how much of it is being put to good use? Zettabytes of data are piling up while CIOs face pressure from competitors to implement advanced technology. With these technologies maturing so quickly, are industries really preparing their data appropriately and leveraging it strategically?
Look, sometimes it’s perfectly acceptable to say yes to an exciting new project and then work out the details as you go along—the entire Agile method is built on this iterative development idea. But making the decision to become a truly data-driven organization is a significant step that deserves some careful consideration and preparation before making the leap.
Creating an organizational culture that prioritizes collaboration to gain shared value from data is paramount to solving the problems you are trying to address with technology. Using global data standards, such as the GS1 System of Standards, instead of proprietary, closed ecosystems to support data management can unite multiple departments, functions, and external partner systems. Through the use of a common language, companies can enhance data usability and scale technology effectively, as standards provide structure and unique identification that streamline external collaboration and data sharing.
Here are three reasons why we need to work together to pulverize traditional data silos and unlock data’s potential using standards.
A crucial part of becoming data-driven is increasing the amount of automation—this is precisely the attraction of technologies like AI, where a vast amount of resources would be spent on manual labor in its place. Before a company commits to automation like this, data siloes must become a thing of the past in favor of interoperability. According to a recent survey of senior executives by Capgemini, 56% said that organizational siloes were the biggest impediment to effective decision-making using big data.
Standards enable supply chain partner systems to become more interoperable—meaning one external system can quickly interpret data in the same format as another external system. While this is the norm for operations such as barcode scanning, where the UPC is universally accepted, it’s a concept often missing from the rush to apply new technology. In the blockchain space, for example, we see different ecosystems are emerging, e.g., IBM Food Trust is built on Hyperledger Fabric, while SAP leverages MultiChain. As more industry leaders adopt blockchain, they will no doubt demand solution choice. This goes for other technology too, including AI, machine learning, and the Internet of Things. Data sharing across and between several different software platforms will need to be possible.
Put it this way—when you email a colleague, do you first have to ask what kind of PC they are using? Advanced technology will need a foundation based on standards to interoperate and create a smoother user experience.
It’s encouraging to see that blockchain and other technology has made data sharing “cool” again. But if we don’t prioritize getting the data right on the outset, we’ll only be sharing bad data faster, or on an immutable ledger, in the case of blockchain.
In supply chain use cases, trading partners that collaborate to improve data quality in a standards-based framework— from packaging engineers to quality assurance professionals to supply chain data managers—are gaining more manageability during their blockchain pilots and are learning more about the technology’s viability for future applications. Establishing a data quality program can help a company determine what types of data are essential to share on a blockchain too.
There is an uptick of supply chain industry stakeholders participating in data quality initiatives today, as many companies are finding that any disruption often exposes the need for proactive industry collaboration to provide and exchange quality data. Standards help elevate organizations to the required level of operational efficiency to make technology investments worth it.
Standards enable supply chain partner systems to become more interoperable
Having more data doesn’t necessarily mean anything if the right strategies and standards aren’t in place to make it usable. For example, energy is critical in the retail industry today, as the time to bring a product to market is dependent upon the completeness and accuracy of product information.
As consumers, we all are gravitating more toward the brands and retailers that offer extended product details online (such as gluten-free, organic, dishwasher safe). There is an opportunity for the industry to gather and present these extended attributes more efficiently. But, this likely won’t be achieved overnight through investment in AI, for example. It relies on efficiency gained from standards, which lead to both streamlined operations and data completeness.
Consider this example: when retailers don’t have complete product information from brands to list on their websites, they are often forced to scramble last minute to fill in the blanks which can have negative repercussions on operations. The data is less trustworthy because it is not sourced directly from a brand, leading to potential consumer dissatisfaction. Or, the data could be out of sync with product shipments, causing a delay in listing the product for sale. When brands, retailers, distributors, technology providers, and other stakeholders continually work to form a consensus on the best practices that benefit everyone, they create a level playing field for all to move forward in a profitable direction that ultimately benefits the consumer.
Henry Ford once said, “Coming together is a beginning; keeping together is progress; working together is a success.” CIOs who are committed to innovation must also commit to collaboration to achieve continuing success and real digital transformation.
As senior vice president of corporate development at GS1 US, Melanie leads a team that investigates new technologies, partnerships and business opportunities to increase the relevance and reach of GS1 Standards—the most widely used supply chain standards in the world. She oversees the exploration of collaboration opportunities to help businesses leverage emerging technologies including the Internet of Things (IoT), blockchain, and machine learning. Ms. Nuce has more 20 years of retail supply chain experience, focusing in recent years on retail industry collaboration to improve inventory accuracy, exchanging standardized product data and achieving source to store supply chain visibility.