Manufacturers produce finished goods from raw materials and sell them for a profit. To ensure the efficient and cost-effective delivery of quality products to customers, they must closely monitor and carefully manage their supply chains.
Similarly, organizations have data supply chains consisting of all the information they receive, create, process, analyze and deliver. All of this data helps drive the business, fueling performance, innovation and competitive advantage. It includes insight into the company’s operations, key business intelligence for internal decision-makers, knowledge related to customer buying patterns and much more. In fact, just about every action a company takes involves data in some way.
The difference between traditional supply chains and data supply chains is that the latter is not linear, but can be convoluted and divergent. Information from diverse sources goes through various transformations as it passes through different people, spreadsheets, files, applications, relational databases, data warehouses, data lakes, cloud platforms and reports.
Understanding Enterprise Data Supply Chains
The dependencies and timing requirements of any supply chain are often complex. Quality issues regularly occur in manufacturing supply chains as well as data supply chains. Sometimes these problems are a result of human error, but issues with the systems themselves can also be the problem. For example, with data, errors can easily occur during a batch data transfer.
Another difference between manufacturing supply chains and data supply chains is that issues in information supply chains are more difficult to detect. If a factory employee opens up a box of parts and sees that some are missing or broken, they can quickly react and rectify the issue. But flaws in digital information are frequently less apparent, particularly if data quality is checked infrequently or at limited points in the supply chain. That’s why organizations must manage data quality risk at every step of the data supply chain.
Organizations must take steps to proactively protect the integrity of their data throughout the data pipeline—from ingestion or creation through consumption. Otherwise, bad data can have serious consequences if delivered to internal stakeholders, customers, partners and regulators.
The Consequences of Bad Data and Failure to Manage Data Quality Risk
As a company formulates its information risk management strategy, it’s important to first understand the data integrity dangers across a multitude of categories in enterprise risk, including financial, operational, regulatory and reputational risks. Poor quality can turn data from asset to liability, and have a far-reaching impact on an enterprise’s overall exposure. There are four factors businesses can use to determine the magnitude of their overall information integrity risk initiative:
Once businesses identify their quality risk, they can protect their data supply chain from end to end and ensure high integrity information across the enterprise with data quality-powered data governance. Quality-powered governance reduces information integrity risk by preserving data accuracy and regulating data ownership, access and use. By implementing safeguards, even businesses with significant risk exposure can eliminate bad data across the data supply chain and foster data trust among data consumers.
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