Imagine walking into the largest library you’ve ever seen. You have a specific book in mind, but you have no idea where to find it. Fortunately, the library has a computer at the front desk you can use to search its entire inventory by title, author, genre, and more. You enter the title of the book into the computer and the library’s digital inventory system tells you the exact section and aisle where the book is located. So, instead of wandering the aisles in hopes you’ll stumble across the book, you can walk straight to it and get the information you want much faster.
An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. It synthesizes the information across your data ecosystem—from data lakes, data warehouses, and other data repositories—to empower authorized users to search for and access business-ready data for their projects and initiatives. It also serves as a governance tool to drive compliance with data privacy and industry regulations. In other words, a data catalog makes the use of data for insights generation far more efficient across the organization, while helping mitigate risks of regulatory violations.
For example, imagine business analyst Alex is working on a data analytics project to help her retail company better quantify the success of shoe sales versus jewelry sales. She also wants to predict future sales of both shoes and jewelry. Since her company doesn’t have a data catalog, Alex must first communicate with the shoe line-of-business and the jewelry line-of-business departments to ask what data she needs to conduct her analysis. Next, she submits a request form for each dataset she thinks will be most helpful, then waits while the IT team completes her request. Weeks pass by until the IT team locates and masks the data. Once Alex finally has the information she requested, she still must make sense of it before she can use it. Before she knows it, four weeks have passed from the time she requested the data until the time she has the data in her possession and in a usable form. This is anything but efficient and practical. Thankfully, a data catalog can help.
Let’s look at five benefits of an enterprise data catalog and how they make Alex’s workflow more efficient and her data-driven analysis more informed and relevant.
1. Speed and self-service
A data catalog replaces tedious request and data-wrangling processes with a fast and seamless user experience to manage and access data products. If Alex’s company had an enterprise data catalog in place, she wouldn’t have to submit requests to multiple departments to get the data she needs. Instead, she could simply search the data catalog and access the required information in minutes. So, Alex and other business analysts could complete their projects faster. Meanwhile, the company’s IT teams could optimize their time by focusing on other important workloads.
2. Comprehensive search and access to relevant data
Because Alex can use a data catalog to search all data assets across the company, she has access to the most relevant and up-to-date information. She can search structured or unstructured data, visualizations and dashboards, machine learning models, and database connections. Conversely, without a data catalog, Alex has no guarantee that the data she’s using is complete, accurate, or even relevant. After all, Alex may not be aware of all the data available to her. With a data catalog, Alex can discover data assets she may have never found otherwise.
3. Meaningful business context
An enterprise data catalog automates the process of contextualizing data assets by using:
Business metadata to describe an asset’s content and purpose
Technical metadata to describe schemas, indexes and other database objects
A business glossary to explain the business terms used within a data asset
With this detailed level of intelligence about the data, Alex can view details regarding data lineage and data structure alongside comments from other data users about what each dataset contains. This context helps Alex quickly gauge how useful a particular data asset will be for her analysis. As most enterprise data catalogs allow for curation of metadata, data assets become easier to find, trust and use.
4. Improved trust and confidence in data
As Alex searches the data catalog to gather necessary information, she can preview datasets and their profiles to see if important fields have null or incorrect values. Ensuring data quality is made easier as a result. And because data assets within the catalog have quality scores and social recommendations, Alex has greater trust and confidence in the data she’s using for her decision-making recommendations. This is especially helpful when handling massive amounts of big data.
5. Protected and compliant data
A data catalog when tightly integrated with the company’s data governance platform helps an organization comply with changing regulations and policies while ensuring fast data access and maintaining appropriate data privacy. Rules can be created that anonymize or restrict access to certain data assets throughout their lifecycle so that Personal Identifiable Information (PII) and other sensitive data don’t end up in the wrong hands.
For Alex, this means she won’t have to wait for weeks while the IT team masks columns that contain sensitive information. Instead, governance rules automate which data is viewable and accessible based on permissions and policies. Alex gets the information she needs while the organization protects data from being accessed by unauthorized users or moved to less secure, non-compliant environments.
Why IBM Watson Knowledge Catalog?
IBM Watson Knowledge Catalog on IBM Cloud Pak for Data offers integrated data cataloging and data governance capabilities powered by active metadata, to facilitate advanced data discovery, automated data quality, data governance, data lineage, and data protection across a hybrid distributed data landscape to enable discovery and access to the right data for insights and compliance.
Gartner calls out IBM’s innovation in metadata and AI-/ML-driven automation in Watson Knowledge Catalog on Cloud Pak for Data, along with fully integrated quality and governance capabilities, as key differentiators that make IBM a leading vendor in competitive evaluations.
Watson Knowledge Catalog has numerous use cases. It helps data stewards enable intelligent curation and delivery of trusted, high-quality data to data consumers in a self-service manner to accelerate insight generation, compliance, data quality management. It simplifies policy management and enables organizations to comply with data privacy and industry regulations while ensuring that sensitive and confidential information is protected from unauthorized access. The solution also helps with data quality management by assigning data quality scores to assets and simplifies curation with AI-driven data quality rules. It seamlessly integrates with IBM’s data integration, data observability, and data virtualization products as well as with other IBM technologies that analysts and data scientists use to create business intelligence reports, conduct analyses and build AI models.
Data professionals such as data engineers, data scientists, data analysts and data stewards benefit from these self-service data catalog tools that allow for self-service analytics, data discovery, and metadata management. AI recommendations and robust search methods with the power of natural language processing and semantic search help locate the right data for projects. Data engineers can build trusted data pipelines without having to wait on IT teams to make data accessible.
When it comes to deploying IBM Watson Knowledge Catalog, organizations can do so wherever their data resides—be it on-premises or in cloud environments.
With IBM Watson Knowledge Catalog, Alex would’ve found out that jewelry is way more profitable than shoes in the same amount of time it took her to submit data requests to the departments. She then would have had another month to predict buying trends in other lines of business. Finally, her company’s IT department would have had more time to finish their data projects as it would have been less distracted by data requests. Everybody wins with a data catalog.