Every business that deals with data has a data supply chain. It is a way to understand the lifecycle of data and help a company see their big data picture. When data enters an organization, whether through manual entry, automatic feeds or third parties, it is typically stored, processed and distributed for analysis. While this basic pattern is found in most organizations, the process to improve data in a consistent, more optimized way is not adequately developed in most organizations. The vast majority of organizations are far more concerned with ingesting and analyzing data instead of focusing on the processes involved that enable meaningful insights.
Breaking Down the Data Supply Chain
Akin to a traditional supply chain, a data supply chain involves both supply and demand, as well as the management and exchange of information as it moves from one side of the supply chain to the other. On the supply side, data is created, captured and collected. In between supply and demand is the central exchange where data is enriched, curated, controlled and improved so it is ready for the demand side.
Once data is prepared, the demand side allows users across the organization to utilize, consume and leverage data through visualization portals to uncover practical business insights. However, many businesses are far too focused on accumulating and taking advantage of data, and they dismiss what must happen in the critical middle of the data supply chain to ensure data is appropriately managed.
To create a truly valuable data supply chain, organizations must figure out where their data is, how to prepare it, how to ensure its high quality and how to deliver it quickly, reliably and securely to the point of consumption.
Establishing a Present-Day Data Supply Chain
Creating a functional data supply chain requires businesses to track conceptual metadata, trace data lineage and ensure data quality, thereby enabling searchability.
Conceptual Metadata Tracking: Conceptual metadata describes individual data sets from a business perspective to detail how they are used in business processes. This information is captured from the minds of business users and is documented in a data governance tool.
Tracking Data Lineage: Data lineage provides an understanding of where data came from, what systems and processes it went through, how it was formatted, how it was transferred and how it transformed. Tracking this crucial information enables organizations to visualize the entire lifecycle of their data while ensuring there are no gaps.
Ensuring Data Quality: Data quality is of critical importance. It helps ensure data is complete, accurate and consistent so business users know if they can trust their data or not. Analysis based on low-integrity data leads to flawed business decisions and, ultimately, a diminished bottom line.
With the growing volumes of data and constant movement across environments, systems and processes within the data supply chain, businesses require modern tools to ensure positive outcomes.
Incorporating Modern Technologies to Foster Data Supply Chain Prosperity
Today, state-of-the-art technologies take a business-centric approach to data management, fostering data understanding and empowering users of all skill sets to engage with, and derive value from, data. Unified tools provide business users with self-service options by combining data governance, data quality and analytics capabilities to improve control over data.
Current data governance capabilities provide transparency into an organization’s data landscape, including the data available, its owner/steward, lineage, usage, corresponding definitions, synonyms and business attributes. In addition, the tool allows users to define, track and manage all data assets, promoting collaboration across the organization and increasing data understanding. Data understanding also generates trust in data assets, encouraging data utilization and creating more data insights.
Data quality capabilities help data users administer data integrity checks for data profiling, consistency, conformity, completeness, timeliness, reconciliation and transaction tracking. As data quality is validated across the data supply chain, trust is built among business users to leverage their data. Additionally, analytics capabilities automatically monitor and improve data quality rules by employing machine learning algorithms.
With a robust data supply chain enabled by business-friendly technologies, enterprises can trust their analytics and develop the useful and relevant business strategies to grow their business and bottom line.
Emily Washington, Executive Vice President of Product Management at Infogix.
Emily Washington is executive vice president of product management at Infogix, where she is responsible for driving product strategy and product roadmaps. Since joining Infogix in 2002, Emily has worked closely with product development teams and customers to drive the introduction and adoption of all new products. Before Infogix, Emily worked at Cyborg Systems and Respond.com. Emily holds a Bachelor of Arts degree from San Jose State University. She also holds a certification in graphics design from The Art Institute.