As artificial intelligence (AI) and machine learning (ML) continue to reshape industries, robust data management has become essential for organizations of all sizes. Central to this is metadata management, a critical component for driving future success
AI and ML need large amounts of accurate data for companies to get the most out of the technology. This means organizations must cover their bases in all areas surrounding data management including security, regulations, efficiency, and architecture.
Unfortunately, many IT teams struggle to organize and track sensitive data across their environments. According to a recent Cloudera study, almost three-quarters (73%) of enterprise IT leaders say their company’s data exists in silos and is disconnected, while over half (55%) say they would rather get a root canal than try to access all their companys’ data.
Enterprises and their IT teams need data – structured or unstructured – to have a consistent manager view, be discoverable to employees across departments, be secure and follow governance policies, and be cost-effective regardless of whether data is in the cloud or on-premises.
Let’s dive into what that looks like, what workarounds some IT teams use today, and why metadata management is the key to success.
What makes metadata management important?
A critical aspect of data management is having visibility into the entire flow of data – knowing where data came from, where it’s stored, and who has access to it. This entails a unified view of all an organization’s data. A workaround that IT teams in many organizations practice is simply moving or copying data from one source system to another.
This approach is risky and costly. It multiplies data volume, inflating storage expenses and complicating management. Worse, it compromises data integrity through unclear sources. Mishandling this expanded data can be catastrophic—a single leak could trigger reputational damage, fines, and lost customer trust. According to the Identity Theft Resource Center, there were a record 3,025 data compromises across organizations in the U.S. in 2023 – up 78% from 2022.
AI and ML lead to more data movement around an environment, which means IT teams need to have their enterprise data management practices buttoned up to avoid these risks.
Imagine the photos on your smartphone. When you want to find an old photo and make it more accessible, you might dig through your photo album and make a copy of the image to bring it to the front. While doing this once isn’t a big deal, repeatedly copying and organizing photos over many years can consume a significant amount of your phone’s storage. This might lead you to purchase an external hard drive or upgrade your phone. Additionally, you might lose track of which photos are the originals and which are edited copies.
The same concept applies to an organization’s data. When hundreds of employees duplicate data and move information to different endpoints over many years, it becomes challenging to identify the original datasets, leading to increased storage costs and untrustworthy data – a recipe for disaster when it comes to building or leveraging AI models.
This is where metadata management becomes essential. Metadata provides information about data, making it more searchable and easier to track. For example, instead of endlessly scrolling through your photo album to find a specific image, you could search by location, people in the photo, or the date range. This way, you can quickly find and organize photos that meet your criteria into a separate folder.
Metadata makes it easier to manage, secure, and track data, which results in less of a need to copy data and saves on storage costs. This benefits every wing of an organization – data scientists will have an easier time finding the data they need to work with, while the company can reduce costs and remain compliant. It also means that any department can leverage AI and ML technologies despite the added data flow they produce.
Putting metadata management into action
Enterprise metadata management requires a solution with unified data visibility for both on-premises and cloud environments, automation capabilities to scale across an environment, and the ability to connect to multiple data sources. No matter where data lives, IT teams should have the same management controls so they can follow the same policies and regulations.
Cloudera is making significant investments in metadata management and open interoperability through its open data lakehouse. A data lakehouse offers a centralized repository of various data types using low-cost, scalable cloud infrastructure. It allows anyone in an organization to access the data they need while IT teams can manage data without moving or copying it to another location, guaranteeing a consistent view of data sets.
Cloudera recently invested in upgrading its capabilities with its Shared Data Experience (SDX). Cloudera’s SDX is a set of embedded security and governance technologies that tracks metadata across environments. With SDX, security policies apply no matter where data moves to, so IT teams know that only the right people can access the right datasets. This helps minimize breach risks by consolidating security functions and supports single-pane-of-glass management across cloud and on-premises data.
As organizations invest more in AI and ML, they will need metadata management solutions to optimize the efficiency and reliability of these technologies. This will enable them to reduce overall cost, remove data silos, prevent duplication of data, and simplify data flows to make it easier for employees to work with enterprise data no matter where it resides.
Learn more about metadata management and SDX, and join Cloudera at EVOLVE24, our premier data and AI conference series.
