8 Ways AI-Powered Data Catalogs Save Time Spent on Documentation, Tagging, Querying & More
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An AI-powered data catalog analyzes how different data assets in your data estate are connected to each other, offers suggestions to speed up recurring tasks, and continuously learns from your feedback.
Let’s look at the potential of AI-powered data catalogs, followed by some powerful use cases.
What is an AI-powered data catalog? #
An AI-powered data catalog is a collaborative workspace that uses artificial intelligence and automation to support metadata collection, processing, management, and analysis at scale.
It builds a living, breathing repository of information that acts as the context, control, collaboration, and action plane integrating your entire data universe.
Why do you need an AI-powered data catalog? #
The need for AI-powered data catalogs is already on the rise, evident from a surge in investment in AI-enabled data catalog solutions and a rise in automation technology, according to Mordor Intelligence.
That’s because, according to McKinsey, using AI technologies can help:
- Boost revenues through increased personalization of services to customers (and employees)
- Lower costs through efficiencies generated by higher automation, reduced error rates, and better resource utilization
- Uncover new and previously unrealized opportunities based on an improved ability to process and generate insights from vast troves of data
So, an AI-powered data catalog can help your data ecosystem become more personalized, productive, efficient, and valuable.
According to Gartner:
“AI can assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It’s capable of augmenting the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management and deployment.”
8 AI-powered data catalog workflows for the modern data stack #
These are 8 of many possible AI-powered data catalog workflows that will help you save time, improve efficiency, and generate insights from your data at scale:
- Automatically create descriptions of data assets
- Auto-suggest READMEs for data assets
- Automatically classify and tag data assets, especially sensitive data
- Auto-suggest data asset owners
- Get intelligent search recommendations for intuitive data discovery
- Perform bulk updates to your data assets
- Generate SQL queries with text prompts
- Debug SQL queries with assistant
Automating context creation with descriptions, READMEs, and suggestions for data tags and owners enables data asset enrichment at scale. This helps in simplifying metadata curation, improving context, and ensuring consistency across the data stack.
Let’s see how.
1. Automatically create descriptions of data assets #
With an AI-powered data catalog, you can auto-populate descriptions of data assets in seconds, thereby scaling your efforts at creating and maintaining 360-degree data asset profiles.
The catalog relies on metadata to generate descriptions for assets of the same type with similar names. For example, if you have a table called “Customers” in Snowflake with a certain description, then the catalog will suggest this description for other tables labeled “Customers” across your data estate.
2. Auto-suggest READMEs for data assets #
Similar to auto-descriptions, you can rely on an AI-powered data catalog to generate READMEs for data assets.
Once you set up README templates, the catalog can generate READMEs complete with examples and reference links by studying the metadata from earlier assets.
As a result, you can document the tribal knowledge associated with each data asset at scale.
3. Automatically classify and tag data assets, especially sensitive data #
The AI-powered data catalog will help you automatically tag and classify data by analyzing how similar assets are classified. The catalog will also continuously learn from the feedback to offer to improve its tagging accuracy.
This capability is particularly useful in classifying sensitive PII assets.
For example, if you’ve previously classified a column as PII and that column has downstream columns, the catalog will label them as PII too. It will also automatically propagate access control and anonymization or masking rules, thereby helping with data security, privacy, and regulatory compliance.
4. Auto-suggest data asset owners #
The AI-powered data catalog automatically assigns data owners to data sources, like your Finance team to Stripe data.
So, whenever you add a new asset, the catalog scans your log history and custom metadata to predict the best owner for each data asset.
Moreover, if you update the owner of an asset type, the catalog will automatically check other table assets with the same name across your data estate and provides the owner you updated as a suggestion.
The next set of workflows explores how AI-powered data catalogs can simplify data discovery and exploration. Let’s see how.
5. Get intelligent search recommendations for intuitive data discovery #
An AI-powered data catalog is a living, breathing repository of all your data assets and knowledge. It helps you look up assets — columns, databases, SQL queries, BI dashboards — with a Google-like search bar.
Just like Google, the catalog will also offer auto-completion — recommendation of search terms to help you find what you need more efficiently.
Once you run a search for a certain term, the catalog will also scour through your data universe for related assets — this can include synonyms, linked glossary terms, columns with similar features, and more.
Moreover, the catalog will support intelligent keyword recognition, i.e., display relevant search results despite typos, singular or plural mix-ups, or other errors in your search string.
Lastly, the catalog is continuously learning from your behavior and with time, it will customize the rankings of your search results as per your previous actions.
6. Perform bulk updates to your data assets #
You can use the natural language search with advanced filters to find all the data assets that you wish to update.
After that, you can set up rule-based automation to update asset metadata in bulk and at scale.
For example, if you have to transfer ownership of several data assets, you can set up a workflow to automate this process and update the metadata of your assets at scale, instead of manually updating each and every asset.
With an AI-powered data catalog, you can set up several such rule-based automation to update descriptions, certifications, owners, classifications, status, and other such metadata.
AI-powered data catalogs can also help with SQL queries — write queries using plain English prompts, explain what a query does, and find and fix bugs in your code. Let’s see how.
7. Generate SQL queries with text prompts #
Efficiently manipulating data requires you to be proficient in writing complex SQL queries. Even then, it can be tedious and time-consuming.
With an AI-powered data catalog, you can ask for the data you need with a text prompt in plain English and the catalog will auto-generate the required SQL query.
If you’ve used an AI tool like Open AI’s ChatGPT or Google’s LaMDA, you must have seen how mere text prompts and templates can generate content, perform math, and write code. An AI-powered data catalog acts the same way.
For instance, Atlan’s Trident AI uses the power of GPT-3 to turn data questions into SQL queries. Besides generating queries, it can also explain SQL queries so that you can understand what’s happening and modify the script if needed.
How an AI-powered data catalog can be used to explain SQL queries. Screenshot from Atlan.
8. Debug SQL queries with assistant #
In addition to writing SQL queries, you can also use the AI-powered data catalog to find bugs in your SQL queries and offer suggestions or recommendations to fix the code.
How an AI-powered data catalog can be used to identify errors in your SQL query. Screenshot from Atlan.
Now that you know the possibilities of an AI-powered data catalog, the next step is to understand how to look for these capabilities when shopping for data catalogs. So, let’s compare traditional data catalogs vs. AI-powered data catalogs.
Traditional data catalog vs. AI-powered data catalog: What’s the difference? #
A traditional data catalog is a static data repository. While it supports data discovery and search, data enrichment – metadata curation or documentation – is largely manual.
An AI-powered data catalog is an always-on, intelligent workspace that automates recurring tasks, analyzes asset relationships, and can offer recommendations to enrich data discovery and exploration.
Let’s explore these differences further.
| Traditional data catalog | AI-powered data catalog | |
|---|---|---|
| What is it? | A traditional data catalog is a static, passive inventory of your data assets. | An AI-powered data catalog is an always-on, intelligent workspace that acts as the context, control, collaboration, and action plane for your data estate. |
| How does it help with data discovery? | Traditional data catalogs let you search for the information you need by crawling through your entire data estate. | AI-powered data catalogs are equipped with intelligent keyword recognition and auto-completion. They can also study asset relationships to offer search suggestions/recommendations. |
| How does it help with data documentation? | Traditional data catalogs let you add or edit data asset descriptions, READMEs, ownership, and tagging. However, the process is manual and not scalable. | AI-powered data catalogs can auto-populate data asset descriptions or READMEs, and suggest data owners, tags, or labels.They crawl the metadata of related assets to offer these suggestions and continuously learn from your feedback to be more accurate. |
| How does it help with data exploration? | Traditional data catalogs integrate with query engines and let you run SQL queries directly from the catalog. However, you must be familiar with SQL to perform such activities. | AI-powered data catalogs let you generate SQL queries using plain English text prompts. They can analyze and interpret SQL queries to help you understand what’s happening. They can also study your SQL queries to spot bugs and offer suggestions to fix them. |
AI-powered data catalogs: How does life change for you? #
The 8 use cases mentioned above just scratch the surface of what an AI-powered data catalog can do — the possibilities are endless. The AI-powered data catalog is akin to the iPhone moment for the telecom industry.
Just as the iPhone paved the way for the rise of smartphones, which have now permeated every aspect of our lives, AI has introduced data teams to the next stage of the evolution of data catalogs — rich, seamless, and automatic metadata curation and enrichment.
AI-powered data catalogs also take your organization a step closer to self-serve analytics. Technical and business users are equally enabled to find, analyze, and use the data they need for their daily workflows.
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