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Businesses today operate in a data-driven world with ample opportunity to gather large volumes of data from a disparate variety of sources. But collecting data alone doesn’t suffice in realizing its value — many organizations lack a modern data catalog that makes it easy for business users to access and analyze rich sources of information. This article explains the process of data cataloging and explores its importance in ensuring your company maximizes the value of available data.
The importance of data analytics
The fundamental power of data as a business enabler is that analyzing it often uncovers insights that would otherwise remain hidden. Data analytics finds trends in data, helps answer questions, and assists in making predictions. These are all incredibly valuable capabilities for organizations spanning diverse sectors, including at the government level.
Some example use cases of analytics include:
- Analyzing demographic data and past orders at an eCommerce business to identify new products that may appeal to different groups of customers.
- Informing important strategic business decisions, such as engaging in mergers and acquisitions deals or expanding to new locations.
- Improving operational efficiency by examining data pertaining to existing processes at an organization and how effective they are.
- Identification and risk assessment of personal information in your entire data corpus to drive compliance with relevant data privacy regulations.
- Using machine-generated data or data from IoT devices to determine when in-service equipment needs maintenance in advance rather than reactively waiting for the equipment to go out of service.
Why invest in a data catalog solution?
Skilled business analysts and other users take responsibility for data analytics projects, but not being able to access all the available data limits their insights. Without an effective data catalog in place, business users depend on undocumented tribal knowledge or existing documentation to find what they need. And in a data landscape defined by unstructured data files, such as Microsoft Word documents or PDFs, it’s almost impossible to find everything of value without a dedicated catalog.
A data catalog tool establishes an inventory of all data assets along with metadata to facilitate the rapid discovery and understanding of available data assets regardless of where they reside. In today’s hybrid, distributed IT infrastructures, information lives everywhere from cloud storage repositories to on-premises data centers.
The hallmark features of analytics exercises conducted without a data catalog are wasted time and lower quality insights, divorced from business context. Gartner further exemplified the value of a data catalog with its prediction that organizations offering access to a curated catalog of internally and externally prepared data will realize 100% more business value from analytics investments than those that don’t.
Improved search capabilities and getting more value from business data aren’t the only compelling reasons to invest in a data catalog. Compliance with regulations governing certain types of data becomes easier when you have a comprehensive data catalog. You can easily identify and remediate instances where sensitive data, such as Personally Identifiable Information (PII), ends up in places where it shouldn’t be. Compliances breaches often occur because of insufficient visibility into regulated data.
Essential aspects of data cataloging tools: automation, integration, and control
Data cataloging is an area in which AI-powered automation is not just something nice to have; it’s mandatory for connecting to and classifying information at scale. Data catalogs are built using metadata, which describes information and provides a reference structure to quickly search through data assets. This includes technical metadata relating to the format and structure of data, and business metadata that describes the meaning of the data from a business perspective.
Often, metadata is dispersed throughout many tools and locations in your data environment. Organizational and information silos can make finding this metadata a gargantuan and expensive task, especially for large enterprises. Data catalog software with automation features can break down these silos and automatically gather metadata throughout your environment much faster.
Ongoing automation is critical because new data sources emerge frequently and the catalog needs to stay up to date to reflect these changes. Other automated processes to look out for include data lineage generation to track how data flows over time and simplified search queries using natural language processing.
Data minimization is a crucial requirement of a good data catalog, and it’s also a domain in which automation has great potential. Minimization removes redundant, obsolete, or trivial data to help analysts get more value from information while reducing unnecessary costs and risks from retaining data you don’t need.
Since it’s manually time-consuming to review data and flag what’s worth keeping, catalog solutions with automated data minimization features are well worth seeking out. A data catalog lacking in automation is doomed to fail because business users won’t trust it.
Integration is paramount for data catalogs. With data living in so many different systems, including relational databases, data lakes, data warehouses, cloud storage repositories, and NoSQL databases, you need to be able to connect to all these data stores. A lack of comprehensive integration means potentially missing out on a treasure trove of data lurking in a system that your catalog doesn’t support or can’t connect to.
Another key aspect is that a data catalog must deliver higher levels of control than what your organization currently has in terms of data visibility and understanding who’s using the data, and how it’s being used. These controls should extend to access management: giving users or groups the ability to request access to data and also being able to revoke data access.
What to look for in a data cataloging tool
An abundance of available solutions makes it tricky to choose a suitable enterprise data catalog. Here are a few things to look for that can help to narrow down your choices:
- Simple self-service access to data with seamless search capabilities that don’t present the user with clutter or don’t impose barriers in terms of the technical competency needed to find data.
- A data cataloging vendor that offers connections to all of your data sources and continually builds out its own ecosystem of data sources.
- Automated compliance features with the ability to enforce pre-written rules for masking or restricting access to certain data depending on its classification.
- Effective data governance that connects policy to how data is actually being used without imposing so many controls that analytics efforts are stifled.
The RecordPoint solution
RecordPoint’s platform has everything you need from a modern data catalog solution. Advanced filtering capabilities mean you don’t capture or archive redundant or trivial data that has no analytics value. Only the highest value information gets classified and search capabilities are vastly improved with less noise or clutter. RecordPoint allows for consistent classification, data minimization, in-place management and more, making it easier to manage information efficiently and effectively, and demonstrate how your activity contributes to business outcomes.
Bringing consistency to your data management, no matter where the data is
Connectors provide the same high-value inventory and sensitive data identification to more and more data sources without the ongoing headache of integration maintenance and code-based customization
Benefits of centralized data records management
Three reasons why record and information management for your data pipeline, across platforms and the cloud, would benefit your organization.