One of the chief obstacles in this continual process is the isolated nature of many data environments these days. Whether in the data center, colocation facilities, or the cloud, data silos prevent the kind of data integration that is needed to excel in a digital economy.
This isolation is not only physical, but categorical as well. Multiple processes tend to create volumes of increasingly diverse data sets – such as sales data, finance data, even maintenance records – that at first glance might not seem to influence each other but in fact provide broader views of the truth when analyzed in tandem. The value of data, after all, is enhanced primarily through its relationship and influence on other data.
Data for Data’s Sake
The best way to bring order to this chaos is through metadata, which essentially acts as a tag describing the contents of files, documents, and other pieces of information so they can be more easily discovered by database queries. For this reason, managing metadata has become essential to any digital-facing business model.
Broadly speaking, metadata found in most digital ecosystems consists of three different types:
• Technical metadata describes the basic format and structure of a given set. This can be anything from the underlying model on which it was created to its processing lineage as it makes its way through various functions, as well as data generated from things like access permission and compliance rules.
• Business metadata defines characteristics regarding its use within the enterprise. These could be the business processes and rules the data is used for, as well as any requirements regarding sharing, quality metrics, and other operations.
• Content metadata contains keywords, the nature of the content, how it functions within the data environment, and so on.
Understandably, managing all of this metadata is a significant task involving the careful coordination of functions ranging from data analysis and labeling to classification and the creation of taxonomies that oversee the search and discovery process. This is often tedious work and becomes more onerous as volumes grow. Nevertheless, it is necessary to kickstart Data Management to the speed at which modern business is conducted.
Getting with the Program
Simply managing metadata without specific goals in mind can lead to confusion and wasted effort. To implement a truly effective program, the enterprise should focus on strengthening the following general data attributes:
• Make It Useable: The ability to leverage data to glean true insights in support of key business processes is often hampered by the inability to deliver data to key decision-makers in a timely fashion. Metadata provides the consistency, accuracy, and relevancy needed by individual processes to cut down on wasted effort and overconsumption of resources.
• Ensure Adaptability: Particularly as data volumes grow, metadata becomes crucial in the effort to ensure individual sets can be applied to different functions and viewed in multiple contexts. Metadata allows individual teams to interpret data on their own terms while also enabling more effective communications between teams to improve complex workflows.
• Enhance Discovery: Metadata assists in content discovery by simplifying the means to locate specific data. This can streamline both internal processes and customers’ ability to navigate web sites and other data stores to make purchasing decisions. In both cases, enhancing discovery tools using metadata is key to reducing costs and boosting revenue.
• Meet Governance: Regulatory compliance is a vital aspect of running any business, large or small. Metadata provides the tools to maintain proper records and deliver them to appropriate authorities completely and accurately. Failure to do this could result in civil, or even criminal, penalties.
Metadata Done the Right Way
Implementing metadata management successfully requires careful coordination of diverse elements, and this can become fairly complex as volumes grow and the diversity of data sources, uses cases, and other requirements expands. In general, however, the task relies on forming the proper relationships between three key data sets:
• The terms used to build common business language and definitions. These may arise from industry sources and policies, as well as individual contracts
• Data from business-specific resources like systems, reports, technical documentation, etc.
• Data from digital resources, such as databases, spreadsheets, and data models
To effectively manage this metadata, enterprises should look for critical capabilities within an automated solution. First, it must have an inventory system that can identify data characteristics, resolve disparities, and identify relationships. Equally important is the ability to categorize data lineage and assess the impact of any changes to that history.
It helps to have an intuitive user interface that supports collaboration across diverse use cases. This can be greatly enhanced by semantic language processing that comprehends various terms and rules-based transparency. And, of course, all of this should be automated to support high-speed, flexible Data Management up and down the entire enterprise data stack.
Metadata management is primarily an exercise in preparation and execution. Once you have determined the ways your business will benefit from a streamlined, automated management regime, the challenge of putting the right tools and policies in place becomes a lot less daunting. But realize that this is not a set-it-and-forget-it kind of function. It will have to be continually updated to meet the changing needs of the business model as it keeps pace with the changing economy.
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