Metadata is “data about data.” It provides additional context about data, such as the name, data type, relationship to other data, definition of the data individual element, data mappings, and data transformation rules. Like an iceberg, there is quite a bit of understanding for what is seemingly only a data asset.
For true operational value, metadata must be part of a modern workflow, where solutions for cloud, APIs, and other federated data products need context and lineage from metadata. In addition, as part of a new wave of data catalog management, modern metadata must enable collaboration for all data users. Metadata itself continues its growth with datasets that are searchable and ready to be analyzed.
For the data scientist, taking a look from three perspectives provides a foundation to understand the value of metadata, shown below in three areas.
Data practitioners – from data scientists to business users – must assess what they need from metadata as they are a critical part in managing the data product. In effect, they get telemetry from logical data workflow so they can optimize performance of the architecture and make changes as new assets arrive. This approach promotes the deep use of enterprise data catalogs to support evolving DataOps environments as well.
Great data scientists are always asking questions to start and since business needs can be met with the right architecture and technology implementation, the 5 W’s of metadata management are a good place to start. Shown below are the key questions.
Moving down each column for each W (with a bonus How), data scientists can ensure they have metadata management covered, as well as collaborating across teams.
Does the naming of data assets and taxonomy matter? Decidedly yes. Decision-making can be dramatically impacted through better metadata management; and technology can augment the curation of metadata quickly and effectively to speed the process.
First, as organizations grow into more cloud data repositories, they simplify the ingest and storage to move beyond analyzing structured data assets (customer data, invoices, inventory, finance).
Today, the ability to extend and augment with unstructured data assets (e.g. emails, customer feedback, weblogs, call center info) is a major requirement for complete understanding of the data landscape. This super set of data feeds analytics and machine learning like never before.
As a result, decision-making is no longer limited to descriptive analytics but more as predictive and prescriptive, meaning more data needs to be ingested. This enables the models to generate outcomes that are quicker, meaningful, and accurate. It also means data practitioners need to be more precise with their data discovery, a precursor to building reliable reports and models for trustworthy outcomes.
Enter metadata management. The older “structured” taxonomy created and managed through traditional means is no longer enough. Modern management demands an intelligent means of metadata curation and the adoption into the right technology makes it possible. Three principal areas are impacted during this process.
A central data catalog allows users to collaborate using metadata. This enables consistency of data accuracy, creates data congruency for quality and structure, and makes the data accessible to all users. In short, it creates an overall data literacy used across the enterprise.
“Organizations that offer users access to a curated catalog of internal and external data will realize twice the business value from analytics investments than those that do not.” “Gartner Report: Augmented Data Catalogs”
With data literacy gained through a centralized data catalog, more data practitioners (especially in non-engineering roles) also now have a mutual understanding of data assets.
Finally, when this management process is augmented through low-code/no-code technologies within an engineering and analytics realm, opportunities also open up for generalists who can be brought into the development folds to further increase developer productivity and speed. Organizations which are already struggling to on-board qualified talent will now be able to deliver more with less.
Implementing the combination of policy and practice with all data practitioners is a challenge of leadership for certain. Doing so has many benefits for the entire management of the enterprise.
A typical data scientist spends about 85% of their time discovering and preparing (“wrangling”) data. It is challenging to understand the data and its context without proper documentation and metadata focus.
With a push towards self-service it can also mean that the catalog can assist the work of wrangling for the citizen data scientist or business user, while the more advanced IT data scientist can focus energies to ensure the models generate meaningful insights. Saves time and money.
Data insights can be complicated. But to truly benefit, one needs to understand their context through definitions, behavior, embedded associations through a simple searchable and centralized curated catalog. In addition, common shared knowledge into data can remove inhibitions from other corporate citizens, thereby eliminating literacy silos (not just limited to a select few SME’s) and enabling data driven conversations every day.
Rama Ryali serves as Vice President of Product Evangelism and Strategy at RightData, a thought leader for modern data. Throughout his career, dedicated to data management and implementation, Rama served as CTO and has directed many multi-million-dollar data management initiatives. firstname.lastname@example.org
RightData is a trusted total software company that empowers end-to-end capabilities for modern data, analytics, and machine learning using modern data lakehouse and data mesh frameworks. The combination of Dextrus software for data integration and the RDt for data quality and observability provides a comprehensive DataOps approach. With a commitment to a no-code approach and a user-friendly user interface, RightData increases speed to market and provides significant cost savings to its customers. www.getrightdata.com