Fostering enterprise-wide data understanding among Business users to turn big data into actionable insights is the goal of every Organization seeking competitive advantage. However, with data being collected, shared and deployed at today’s rapid pace, IT is often the only organizational department equipped to manage preparation of massive data amounts. At the end of the day, business users are all too often left confused and confounded by IT’s technical tools and jargon.
Data is a valuable commodity, but it is only an asset if business users can transform it into meaningful customer insights and improved outcomes. Organizations can no longer afford to leave business users solely dependent upon IT for the data they need given the current volume and velocity of data and a recurring IT/Business disconnect: IT typically doesn’t understand data from a business standpoint, and business users often lack the technical expertise to translate IT data tools into clear analysis in a business context.
Ensuring success with enterprise data requires the formal alignment of diverging lines of business, technology and processes through data governance to create a comprehensive data catalog. A data catalog organizes the technical details around data assets, or metadata, into defined, meaningful and searchable business assets to enable consistent understanding among all data consumers.
A data catalog is essential to business users because it synthesizes all the details about an Organization’s data assets across multiple data dictionaries by organizing them into a simple, easy to digest format. The data catalog provides clarity into data definitions, synonyms and essential business attributes so all users understand and can leverage their data as an asset. It also identifies data owners, stewards, and subject matter experts, so business users know where to go when they have important data questions—enabling easy collaboration between different departments.
A data catalog can also provide business lineage to give business users a clear understanding of the flow and dependencies of their data, from origination and throughout the data supply chain.
