Metadata is quickly becoming the next big sensation in data management, and will be crucial for the success of big data projects in 2018 and beyond. Metadata contains all of the information that is vital to both understanding and effectively deploying data across organizations – information such as meaning and purpose, lineage, utilization and more. It is critical in enterprise data environments to support effective data governance, ensure regulatory compliance and meet a growing inventory of data management demands.
In previous posts, we discussed how to extract value from metadata and how organizations can leverage their metadata to optimize data initiatives. However, it is unlikely that upper management will invest in metadata management technologies unless they are certain that they will see a significant return on investment (ROI). To help demonstrate that much needed ROI, this post will discuss how to make a business case for metadata and demonstrate value.
Businesses have one fundamental concern—their bottom line. What executive leadership may not realize is that investing in metadata can improve their bottom line by increasing operational efficiency, optimizing data security and maximizing the value of their data. Below are 4 key capabilities that demonstrate the importance of metadata and help build a metadata business case.
Discovery: Finding individual data sets in a big data environment can be a challenging proposition, but a robust central repository of metadata enables organizations to quickly and easily search and discover the data they need. By defining and tagging data sets with metadata, it becomes easier to find and to confirm the data is valid for a specific use. This improves operational efficiency, allowing organizations to more effectively leverage their data.
Impact Analysis: Being able to locate and understand the quality of your data is critical, but metadata can take this a step further. By using metadata, organizations can see how data is related and the impact that changing that data may have on other data sets. For example, if a user is searching for a business term such as “customer identification number,” an impact analysis can tell the user what other datasets, use cases, and subject areas are related to that term. Furthermore, they can determine the impact to the organization if that data element should be deleted, moved, or changed in some meaningful way. For example, if you changed the data type of a telephone number from a string to numeric, it might save some storage space, but it might break 10 algorithms across the organization. Having this information at your fingertips gives you a real view into the reach and criticality of data, and will improve operational efficiency, ultimately saving time and money.
Inventory and Assessment: Data breaches are a virtual inevitability in business today, but metadata can help assess the damage and prepare organizations to respond immediately and appropriately. Metadata can be used to classify and rank data sets based on their security risk, to ensure the potential impact of a breach is quickly understood and duly addressed. For example, Social Security numbers comprise highly sensitive data, however, without an associated name, they are just a sequence of 9 digits. If a hacker attains access only to social security numbers, it requires a different response than if they accessed full customer records with Social Security numbers as well as associated names and dates of birth. Having an up to date inventory of data before a hack or a leak can save a company from unnecessary customer mistrust and damage to the brand – and ultimately, bottom line impact.
Certification: Every organization needs high quality data for big data projects, so it’s important to have an effective and transparent certification process. If an organization is selling data to a third party and wants to receive maximum reimbursement, it will need to ensure the quality and reliability of the data. Metadata can be used to create data quality scores, so the quality of data can be quantitatively verified. Not only can organizations get maximum return on their data, but they can also feel confident in the quality of the data they are using internally.
A sound metadata management strategy can pay dividends, but to effectively leverage metadata, organizations will need to implement a strong, robust data governance platform.
To leverage metadata, organizations will need a comprehensive data governance platform that delivers a complete view of an organization’s data landscape. The platform should include interactive data visualization and lineage capabilities, and deliver transparency into all aspects of an organization’s data assets. It should also have automatic discovery capabilities, enabling the capture and monitoring of changes to metadata. Once changes are discovered, the technical metadata relationships may be investigated to deliver meaningful insights on data for better business decisions.
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