As Advanced Analytics becomes a prominent driver of revenue in the data-driven age, technologies are needed to help provide a context to data. Business leaders and the global Data Science community have realized that businesses need appropriate technology to comprehend the context of business data.
Metadata Management: The Promise of Context-Driven Data
In addition to Data Governance, the Data Management strategy that captured global business attention in recent years is Metadata Management, because it promises to clarify the context of data.
Metadata Management is a collection of policies, procedures, and systems that are used to administer data that describe other data. The basic purpose of Metadata Management is to ensure that data-driven insights or business information can be uniformly accessed, shared, integrated, analyzed, or linked throughout an organization. The term “metadata” is defined as data that describes other data. Though this descriptive characteristic is the starting point, metadata performs more functions than just describing data.
In simple terms, metadata can tell the user where a particular data resides and how to find it. Metadata functions in ways similar to that of old-fashioned library card catalogues, where individual cards indicate the name, title, author, ISBN, etc. and most importantly, the location of an item in the library.
Metadata possesses intrinsic journalistic attributes; it explores deep into the 5Ws of business data (who, what, when, where, why, and how), thus discovering and establishing the inner connections between data and data-centric application, assets, and processes. In a data warehouse environment, another type of metadata is known as an “Operational Metadata,” which Data Warehouse staff may use to enhance the ETL process.
University of Bath has done considerable research on metadata, and some of the findings are available in the university publication. This paper discusses the future use of every type of metadata.
Metadata Management: Managing Data Describing Data
In many organizations, data repositories are diverse and complex, such as side-by-side existence of legacy systems, extended Cloud storage, Hadoop clusters for Big Data, and external vendor data. Managing these complex data systems is not easy, and the IT staff often faces serious challenges while managing and maintaining such vast data repositories across the length and breadth of the enterprise.
Each of these repositories operates on its own rules and procedures, and a small change at one location can impact a number of processes. Moreover, these large businesses have the additional burden of accessing real-time insights for making just-in-time business decisions.
With such huge responsibilities facing enterprises, business leaders and operators are now relying on “metadata” to deliver fast and accurate access to the right data, as needed.
The growth and penetration of Advanced Analytics in global businesses have made Data Management a core competitive strategy. Gartner had predicted that gold-digging businesses would spend close to $18.3 billion on BI and Analytics tools in 2017. The webinar Demystifying Metadata – A Practical Approach to Solving Critical Business Problems gives a hands-on look at Metadata Management.
Data Governance in Metadata Management
The role of Data Governance (DG) in Metadata Management is to provide a 360-degree view of organizational data. As DG promises transparency in Data Management, the quality and risk attributes of the data in question is clearly available to the user.
Of course, the topmost criterion that preserves the integrity and security of business data is current inventory. Finally, to preserve the business context of insights, individuals identified as data owners and data stewards have the arduous task of administering data definitions, Data Quality standards, and data usage rights.
Metadata Management in Big Data Environment
While Big Data promises big returns, it can also indicate complex forecasts or insights. Managing the data behind these magical forecasts or insights is a huge challenge, and metadata offers the first layer of protection to the valuable data driving the forecasts or insights. The end results can be different than expected if all the data definitions from different sources do not match.
As large organizations continue to push siloed data out into the organizational metadata territory, it is imperative that a central metadata process is deployed to eliminate mismatched data definitions and analytic parameters across the organization. To keep the process going, change-management policies have to be worked in. A Strong Metadata Management Process Eases Big Data Woes details this.
A Use Case: Metadata in Image Libraries
Every image file that is scanned or digitally created carries some metadata stored within the file. In the case of specific graphic file-management standards like IPTC or PLUS, metadata information has to be filled in later.
When image files are exported out of a graphic application, the metadata usually stays with the files unless an explicit program option overrides the native metadata information. Demystifying Metadata: The Who, What, Where & When of Photos offers unique ways of using metadata to find images.
Best Practices for Metadata Management: Expert Opinions
- In an Analytics Age, businesses need metadata to understand the context of business Analytics.
- Metadata Management has been recognized as a fundamental requirement for state governments and federal agencies, without which these agencies cannot deliver value to their governments.
- Early adopters of Metadata Management will have a market lead with experience, trained staff, and an implemented solution. Such businesses will reap the rewards of MDM in every core function, from technology implementation to delivering enhanced insights.
- The eBay Metadata Management practice has proved that it can be used for agile Data Warehousing.
Best Practices for Metadata Management discusses the different types of metadata and their uses, the context in business analytics, and the importance of a Data Dictionary to manage data through controlled vocabulary.
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