Metadata Management and Analytics: What is the Intersection?


Metadata Management implies a set of activities, which administer data for better usage and outcomes. Thus, this practice involves establishing stringent roles, responsibilities, policies, and processes to ensure that data-driven information is available, accessible, sharable, and maintainable across an organization for the best analysis and application of such information in daily business. In that sense, Metadata Management works alongside Data Governance – a well-planned, systematic exercise to put the right controls to administer and manage enterprise data.

According to Forrester Research, metadata either describes business data or provides a context for business data and its associated processes, services, rules, and policies. Therefore, metadata may be of various types – technical metadata, business metadata, or operational metadata, each of which provides distinct functions. The article titled Metadata Management vs. Master Data Management: An Overview mentions that metadata conducts investigative analyses (what, why, where, how, and when) on data. In that sense, it aids the business users to understand the data they use.

In the Gartner Report Market Guide for Metadata Management Solutions, the verdict from  major business owners is clear; the businesses want Metadata Management (MM) to address longstanding issues related to Data Governance, MDM, BI, and Enterprise Metadata Management. In clear terms, these are the top five MM contenders in business users’ wish lists:

  1. Decentralized Data Management and self-service tools while ensuring security and privacy
  2. More stringent metadata requirements for IoT data
  3. More contextual metadata for online information
  4. Applicability of metadata to predictive analytics
  5. Unlocking of data silos with metadata tools

Within the context of this article topic, all the above business requirements are relevant, as Business Analytics cannot happen without a consideration of any one of them. The article Fundamentals of Metadata Management explains the core components of metadata. The article focuses on three broad benefits of Metadata Management to any-sized business, as far they relate to enterprise BI. For enterprise BI or analytics to outperform competition and create opportunities, data definitions have to be consistent, data inter-relationships have to be clear, and data lineage must be clearly traced. Metadata Management can deliver all three.

Business Analytics with a Data Warehouse

In a typical data warehouse, the repository for collecting metadata is an integral part. The data warehouse is defined in terms of its “data definitions, schema, views, hierarchies, locations, and content”. This readily available information becomes very useful during business analytics and removes a lot of excessive labor associated with data explorations.

Big Data Analytics and Metadata Management

The benefit mentioned above is more deeply perceptible when the analytics is conducted with big data, which is 80 percent unstructured data. If the Data Management structure in such a complex scenario is not handled well, then a business may end up losing significant market share with erroneous analytics. Thus, Metadata Management is critical for BI or analytics with big data. The better the “data categorization and organization,” the faster the decisions. The article Five Reasons Why Big Data Needs Metadata Management and How to Leverage It offers five good reasons why metadata is critical for the success of big data analytics.

When data is spread across an enterprise in varied data troves such as data warehouses, data lakes, or silos, nothing works like metadata to aid quick search and access to required data. The article Data lakes and Big Data Analytics: The What, Why and How of Data Lakes offers a convincing scoop on why a data lake serves as a partial solution for storing multi-structured data,  but the solution is still not complete without Metadata Management.

Data Science Central explains why Metadata Management is intrinsic to the success of big data analytics. The presence of metadata in vast amounts of semi-structured or unstructured data can make it easy for a user to quickly sift through irrelevant information and locate exactly what is needed. This imposed structure was always present in a data warehouse or a data lake. Also, Metadata Management can help apply consistent business rules to enterprise data. In other words, metadata brings more clarity, consistency, and transparency to critical business information. Social Media Today says that in absence of qualified in-house data experts, organizations are increasingly seeking managed services to handle big data analytics.

Why is Metadata Important for Data Governance?

Data Governance defines who has access to data and how, sets standards for data ownership and control, and imposes roles and responsibilities for Data Stewardship, Data Management becomes a completely “auditable and accountable” exercise. As metadata provides different descriptions, definitions, and tags to classify, categorize, and organize data, it is obvious that Metadata Management and Data Governance will work hand in hand to implement the right controls on enterprise data. In this context the article Data Management vs. Data Governance: Improving Organizational Data Strategy clarifies the distinction between Data Management and Data Governance. Without Metadata Management, enterprises cannot deliver “timely” and “trustworthy” information. Metadata Management, like Data Governance, is as much about people, policies, and processes as about the “data.”

The Data Virtualization Blog makes a strong case for the necessity of Metadata Management in data virtualization tools. The author of this post believes for data virtualization application to work, an end to end solution is required from the data capture to data publishing stages.

Additionally, a recent webinar helps to understand data dictionaries and data catalogs, and how metadata populate these data stores. The webinar also discusses how Data Governance is applied to metadata.

Metadata Management in BI

Here are some solid reasons why Metadata Management is needed for BI or enterprise analytics activities:

  • For searching highly specific information from mountains of data. Traditionally, ETL processes for drilled search took a long time, which has been reduced by at least 50 percent due to Metadata Management.
  • Metadata Management enables “slicing & dicing” of datasets, which helps in establishing correlations and hidden patterns in data. Centralized management of metadata is even better because it quickly provides sense to a lot of disparate data from different sources.
  • Metadata Management cuts down analytics time by providing efficient Data Management features and creates more value for BI activities.
  • Metadata Management improves data alignment across data silos, and helps create a uniform language to interpret data.
  • Metadata Management helps individual departments to comply with sudden change of reporting requirements, for example, from monthly to daily.

Closing Note

The pace of data growth has necessitated Metadata Management in enterprises. In the Big Data Age, data preparation for analytics and BI is now a source of opportunity rather than a hindrance. This is where Data Science and its allied technologies come in. So long as big data analytics has critical importance in businesses, Metadata Management will also remain another critical activity.

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