Data Catalog Definition and Integration Strategy Roadmap

This is part of Solutions Review’s Premium Content Series, a collection of reviews written by industry experts in maturing software categories. In this submission, Boomi’s Chief Innovation Officer, Ed Macosky, offers a Data Catalog definition, an explainer, and how to integrate it into your organization’s onboarding strategy.

Premium SR ContentData is the lifeblood of a business… plain and simple. Businesses today have vast amounts of data, and it’s everywhere. Forrester reports that between 60 and 73 percent of business data is not used for analytics. In many cases, the data in question is unknown or dormant, which means that it is also uncataloged and inaccessible.

So what’s the problem ?

Missing and/or inaccessible data creates problems; this can cause business leaders to make decisions based on incomplete or incorrect information. This results in missed business opportunities and could mean that sensitive data subject to regulations such as GDPR and HIPAA is not properly protected. Data catalogs can help.

Data Catalogs Explained

Similar to a library’s physical or online catalog system that helps readers find and access the book they need, a data catalog is a comprehensive inventory of data assets within an organization. A data catalog manages metadata, enables rapid search and discovery, supports access control, and enables data governance. It leverages metadata to help data engineers, data managers, and users within an enterprise organize, secure, and find trusted data by identifying data type, classification, location, owners, and publishers, etc.

A survey by Wakefield Research and Elastic found that over 50% of professionals spend more time searching for files than working. It is a problem. Data catalog technology can help organizations improve their data readiness with transparent, searchable, and quality data, and enable broad business collaboration in the process. Providing a centralized repository enables organizations to bring together data from systems, applications and people to create a reliable, comprehensive and up-to-date business intelligence resource. Data catalogs can also streamline operations by migrating, consolidating and streamlining data at the speed of today’s business, with a customer-centric focus.

When data is trusted, properly governed, and easily accessible by those who need it, the entire organization benefits from improved operational efficiency, increased organizational trust, reduced risk, and reduced costs.

Data catalogs facilitate data discovery

Data catalog solutions allow users to import datasets, search and augment them, or add metadata by applying tags, which helps users in the data discovery process. Users can then see similar datasets in different solutions. This is particularly important in mergers and acquisitions (M&A). Before two companies can become one, IT managers need to know what data exists and where it resides to integrate data from both companies. Having a data catalog in this case reduces liabilities associated with unknown sensitive data or personally identifiable information (PII) when the integration takes place.

Another use case is self-service analytics where a centralized portal or data marketplace helps users find, understand, and trust democratized data — including master data — without IT intervention. Self-service analysis improves productivity and speeds up insights. Data catalogs can also help data stewards implement data governance, as they can effectively ensure that the right people have access to the right data at the right time based on established roles and policies, thereby preventing misuse. management of said data.

Integrating a data catalog into a company’s integration strategy

There are a few things to consider when integrating a data catalog into a company’s onboarding strategy. Look for a solution that offers the following features:

  • Fully managed service. A fully managed cloud-based service platform has no infrastructure to set up, manage, or maintain. This eases deployment, minimizes costs, and allows data engineers to focus on business goals and objectives.
  • Smart automation. Artificial intelligence (AI) promotes scalability. Once an organization has connected its data sources, an intelligent AI engine can automatically profile and tag data assets, map relationships, and find data similarities.
  • Discovery and Collaboration. Finding data should be easy. Natural Language Processing (NLP) search allows data users to quickly find the data they need. It also helps users understand it via a data dictionary and business glossaries. If questions arise, users should be able to chat with experts and peers within the platform.
  • Governance and security. With governance and security features built into the Data Catalog, organizations can automatically detect row- and column-level PII and control access with role-based permissions. This helps companies stay compliant with internal policies and industry regulations.

Do more work

The proliferation of data increases as companies progress in their digital journey. When users can easily find the critical data they need, when they need it, it gives them more time to focus on the business at hand. Connected, integrated, and truthful data enables smooth migrations, productive users, and happy customers. It’s a win-win for everyone.

Ed Macosky
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