A report from Gartner states “By 2019, data and analytics organizations that provide agile, curated internal and external datasets for a range of content authors will realize twice the business benefits of those that do not.” However, organizations still struggle to understand the value of metadata management and cataloging. As data unification and data collaboration become key critical success factors for organizations, it’s worth revisiting the data catalog as well as its benefits for the entire organization, given it will soon become the cornerstone of your data-driven strategy.
What is a data catalog?
Gartner describes the data catalog in another report: “A data catalog maintains an inventory of data assets through the discovery, description, and organization of datasets. The catalog provides context to enable data analysts, data scientists, data stewards, and other data consumers to find and understand a relevant dataset for the purpose of extracting business value.”
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Gartner’s description is a good start, but it might be too restrictive. Data catalogs not only provide context to key stakeholders to find and understand data, they also automate metadata management and make it collaborative. In this way, external stakeholders will not only understand it but also act on it and curate it so they can leverage the data catalog for extended use. Consequently, the dimension of automation and data collaboration is of utmost importance.
A modern data catalog will become the single source of trust that unify all your metadata that can be shared within your organization and will make collaboration easy. It can automatically discover, profile, organize and document your metadata and makes it easily searchable. A data catalog will give a clear understanding of your datasets making your data systems more intelligent and unlocking data value.
By activating all your data, a data catalog automatically harvests your data and then adds data on your data (or metadata). Metadata (data about data) will add meaningful information to your datasets, so you improve its usability to make data a real asset for your organization. To illustrate, let’s take the example of an online catalog for finding books in a library. This is a centrally managed place where readers find what they need to know on the assets and where to find them: their title, author, summary, placeholder, but may also other reader’s reviews and recommendations. Consequently, they will search faster and easily find valued and curated content in the library without physically visiting the library.
What does a “modern” data catalog mean?
Now that you have an introduction to the concept of a data catalog, it’s time to ask the question, “what is a modern data catalog?” Let’s look at another common example, the Amazon marketplace.
Now imagine that your Amazon marketplace is linked to any shop, retailer or even other e-tailers. This is how powerful and useful a data catalog can be. It’s the Amazon of all your data. But unlike Amazon, you have the power to shop and curate all your data and equip your data owners with tools to curate, cleanse and trust over time what’s inside your datasets so your catalog can become a living marketplace of any valued data within your company.
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Doing this manually can be a heavy and time-consuming operation. Fortunately, modern data catalogs have an extensive range of powerful capabilities such as pattern detection, relationship discovery, pervasive profiling, automatic harvesting and classification so you can highlight data quality issues very easily and start applying corrective actions.
Key ingredients of a successful data catalog
Not all data catalogs are equal. When choosing a data catalog, it’s essential you filter players on key capabilities. Consequently, several data catalogs including Talend Data Catalog rely on key components that will make your data strategy successful. Let’s explore some of its key capabilities:
- Connectors and easy to curation tools to build your single place of trust: Having a wide array of connectors reinforces data catalog’s ability to map physical datasets in your dataset whatever the nature or the source of your datasets. With powerful capabilities, you can harvest metadata from business intelligence tools, data integration tools, SQL queries, enterprise apps like Salesforce or SAP, or data modelling tools so you can onboard people to validate and certify your datasets for extended use. Building a single source of trust shouldn’t rely not only on data source connecting capabilities but also on validation and certification tools to make your data governance a living process over time.
- Automation to gain speed and agility: With enhanced automation, data stewards won’t spend time connecting data sources manually. They will then focus on what’s really important— correcting data quality issues and curating it for the benefit of the whole organization. Of course, you will supplement automation with stewards’ help – to enrich and curate datasets over time.
- Powerful search to explore datasets in a snap: As the primary component of a catalog, the search should be multi-faceted so you can specify different parameters to perform an advanced search. Name, size, time, owner, and format are examples of search parameters.
- Lineage to perform root cause analysis: Lineage helps you to link a dashboard to the data it exposes. Lineage and relationship discovery play a big role in understanding the relationship between different types and sources of data. So, if your dashboard displays inconsistent data, a steward can use the lineage to see where the problem is coming from. We can take the same approach to spot applications containing shadow IT that escape to IT’s control such as market datasets using consumer databases containing PII data.
- Glossary to add business context to your data: Governance relies on the capacity to federate people on your data. To do so, they need to share a common understanding of terms, definition, and link it to the data itself. Consequently, the glossary is actionable. Search for PII in a data catalog and you will find the data sources that contain them: it’s very useful in a GDPR context where you need to take control of all the data that contain personal information.
- Profiling to stop polluting your data lake: When connecting different data sources, data profiling is key to assessing your data quality for completeness, accuracy, timeliness and consistency. Not only it will save you time but it will also help you to quickly spot inaccuracies, so you can make stewards aware of this issue before polluting the data lake.
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You will reach your data catalog’s ultimate value if you can link it with self-service tools that will help your stewards and business users to prepare datasets and curate your data overtime. Making people accountable require you put easy tools at their disposal to act on your data.
Learn more about data catalogs
A data catalog should be the cornerstone of your data strategy. If you wish to take control of your data, stop polluting a data lake, build a single place of trusted data the collaborative way, start a data strategy or act on privacy regulations such as GDPR, then you will need a data catalog. Explore Talend Data Fabric, an end-to-end unified platform that allows you to manage and automatically catalog all your enterprise data within a single environment.