Data is an essential asset for any business, with comprehensive efforts made to generate, source, and prepare it for analytical use. But just as important as collection and cleaning is ensuring its accessibility for users across the organization.
This highlights the need for an organized data inventory—a directory that makes it possible to easily sort, search, and find the data assets required. In other words, you need a data catalog, a core component of master and meta data management.
Let’s talk about why such a system is important, plus how machine learning can help your organization fully optimize the processes involved.
Data cataloging, explained
As enterprises scale, there will come a point when they will have acquired a massive amount of data from various sources. Usually this stack of databases is siloed, with different kinds of sizes and technologies—from RDBMS to MongoDB—that have evolved over time.
To eliminate silos and create a unified view of products, customers, and operations, enterprise data is usually housed in a shared data resource such as a unified data warehouse or master data repository. But even with a one-stop shop like this, organizations find that they still have challenges in accessing the data assets they need.
Without enough visibility into the content and context of existing databases, too much time will be spent on finding and understanding data. And the data management process is already lengthy as it is. This is why a data catalog, a powerful tool that lets you “order” the data you need from the relevant databases, is essential.
Essentially, a data catalog involves the following aspects of data management:
- Data acquisition – the process of bringing data into a database using ETL or streams
- Data searchability – the process of making data accessible so users can easily search for the data they need.
- Data visibility – the process of providing a relevant view of enterprise data assets, such as a 360-degree view of the customer or product.
- Data dictionary – a collection of the different features and attributes of data assets, also called a metadata repository.
For example, a customer in your organization might have different data elements and accounts across departments. A master data management solution groups this customer as one asset and will then feed this into a data catalog. With this set up, a customer service representative can just input any of the customer’s information into the catalog (a unique ID, an email address, etc.) and get access to the single source of truth.
However, this enterprise-wide view can be overwhelming. Different users have different data needs, after all. So, a data catalog will also enable the quick access of relevant and meaningful information that’s fit for the purpose of the search. In the case above, the representative will be able to understand who owns the different data elements and whether it’s relevant to them.
Data catalogs will also come in handy when organizations decide to implement cloud infrastructure. With the recent focus on global collaboration and remote workforces, the fast migration of assets is critical. A data catalog will help accelerate this process by ensuring data readiness capabilities in a modern cloud environment.
What does ML bring to the data cataloging process?
Data catalogs enable efficiency and productivity, so it will be counterintuitive if the actual process is done manually. Fortunately, the use of artificial intelligence technologies has become widespread to automate functions that were previously manually handled. Below are a few ways ML is utilized to create a better data catalog.
1. Auto cataloging capabilities
Machine Learning can be used to automate various aspects of the data cataloging process. For example, you can build an algorithm to group a customer’s accounts and their identifiers automatically for a golden record. This enables efficient deduplication, schema detection, tagging, and even outlier detection.
2. A more powerful way to search
Organizations can also leverage Natural Language Processing to enhance searching capabilities in a data catalog. This way, you can extract meta information from various unstructured datasets such as images, videos, and audio. NLP can also help when dealing with corrupted or dirty data.
3. Intelligent recommendations
Much like the product recommendations you see on retail websites, data catalogs can also provide users with ML-powered recommendations about other data elements and datasets that might be relevant to the search criteria. This is particularly useful for sales specialist when they’re trying to upsell or cross-sell products to customers, or even to technical experts when they’re dealing with constantly evolving products.
4. A strong foundation for data governance
The digital economy has numerous rules and regulations surrounding data, and it can be challenging to comply to all of them. Data catalogs can be used to fully understand each data element, what it’s being use for, and what kind of protection it needs. To take this a step further, machine learning can address consistency issues in data definitions and quality.
5. Ready for analytics
Often, machine learning specialists take too much time deciding which data to use for a data modeling project. This is usually because of a siloed approach in creating ML/AI assets such as data sets, feature sets, and models. A ML-powered data catalog can improve traceability between data, experiments, pipelines, and code.
Simplify and streamline data discovery
By incorporating machine learning capabilities, your data catalog can become even more powerful and scalable. It can help organize your enterprise’s various business assets efficiently, implement effective meta data management, and empower decision-making. With this tool, users across the data pipeline can easily search, evaluate, and apply the data they need for analysis and other uses.
