5 Key Takeaways from Back to Basics: Data Discovery
What is data discovery?
- Identifying, cataloging and classifying business-critical and sensitive data
- Detecting information about your data’s structure, content and relationships
- A critical component of any data governance program
- All the above
As you probably guessed, the answer is “d. all the above”!
During our second installment of the Back to Basics: Data Catalog webinar series, our experts Sid Bardoloye, Principal Product Manager, and James Sizemore, Data Governance Domain Expert joined Nathan Turajski, Sr. Director, Product Marketing and me to define data discovery within the context of a data catalog. We also covered how data discovery is performed, what it enables and why it’s important for data governance.
If you missed this episode of our series (or our first episode), you can now watch both episodes on demand: Back to Basics Series Episode 2: Data Discovery. We’ve also summarized some of the highlights of this episode here:
1. Data Discovery Enables You to Find, Understand and Trust Your Data
Data discovery begins with scanning for data across your organization’s landscape, which may include on-premises or cloud-based sources, from data warehouses to ETL and BI tools, SAAS applications and more. Once you’ve located your data, you can discover further information about it, such as its structure, content and relationships. This information can be used to catalog data, enrich its metadata and provide context to help you understand what data exists, its source, its lineage, how it’s related to other data, etc.
Increasing transparency and enabling people to find and comprehensively understand your organization’s data builds trust in it, which is a must-have for data-driven organizations.
2. Automation is Key for Data Discovery
While data discovery can be performed manually, it’s not ideal for organizations with a vast data landscape — it’s simply not scalable since discovering data across a broad range of sources can be an extremely complex, costly and time-consuming process.
Luckily, organizations can use artificial intelligence (AI) techniques, such as classification and clustering, to facilitate data discovery. When data scientists and analysts don’t have to spend time manually performing data discovery, they can spend more time doing their jobs, saving organizations time and money.
Read more about AI-based discovery techniques in our eBook, Extract Value from Your Data with AI-Powered Data Discovery.
3. AI and Human Intelligence Are a Winning Combination
While organizations can rely on AI for scalable data discovery, human intelligence can enhance the process. Having a human review and curate data assets can augment and improve AI-driven data discovery. For example, within a data catalog, individuals have the ability to provide additional business context, and certify, rate and review AI-curated assets, helping users understand more about the data.
When human involvement occurs, utilizing approaches such as bulk operations help to expedite repetitive tasks and optimize efficiency. Additionally, in an intelligent data catalog, AI can be trained using human input and feedback to further refine and improve the accuracy of the automated data discovery processes mentioned previously (i.e., classification, etc.).
4. Data Discovery Helps Organizations Extract Value from Their Data
Organizations can utilize data discovery to locate and classify personally identifiable information (PII) across data assets, which is extremely valuable and necessary to ensure compliance with external data privacy regulations that require organizations to know where this information is located, how it’s used and who has access to it.
While discovering sensitive data is a significant use case for data discovery, discovery can also enable organizations to fuel analytics programs, including customer experience and loyalty initiatives, and accelerate cloud modernization, driving value creation opportunities for organizations.
(Learn how data discovery helped Freddie Mac navigated the surge in demand during the Covid-19 pandemic.)
5. Data Discovery is Critical for Data Governance
Data discovery is an integral part of any organization’s data governance journey. Organizations need to 1) understand what data they have, along with its context, 2) determine what data needs to be governed and 3) ensure that relevant data is being governed appropriately. Data discovery supports each of these requirements by helping organizations locate their data, facilitating metadata enrichment and classification, advancing data intelligence and much more.
Register for the Remaining Episodes of the Back to Basics: Data Catalog Webinar Series
Be sure to join us for the third episode of our Back to Basics: Data Catalog series, where you’ll learn the basics about data lineage, a fundamental element of data cataloging. You’ll also have opportunity to have the data catalog experts answer your questions.
Our next episodes in the series will cover data asset analytics and data and analytics governance. Join us for one or all of the upcoming episodes. Register today!
