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Data Management

How Stale Metadata Causes Data Projects To Fail

|Content Specialist

As your organization grows, so too does your database. But even when you’re careful, it’s all too easy for the amount of data to balloon out of control. Part of the issue is letting your records’ metadata go stale.

In this post, we will examine what metadata is, how it becomes stale, what problems that cause, and how to fix them.

What is Metadata?

Metadata is, put simply, data about data. While records store information about a subject with relevant details, metadata stores information about the records themselves, such as:

  • record creator
  • date created
  • date last modified
  • last modified by the user
  • file size
  • file type

Metadata can also include organizational keywords for simplifying, querying, storing, and using data. It’s also helpful for various algorithms consumer-facing databases use. For example, Spotify compiles weekly playlists based on song metadata; or your favorite TV streaming service suggests new shows or new seasons based on your viewing history metadata.

No matter its use, metadata is just as important as the data it describes. With that in mind, keeping it fresh will keep your database working as effectively as possible.

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What Causes Metadata to Become Stale?

As it implies, metadata becomes stale when left alone for too long. There are two major ways this can happen:

1. Infrequent Updates

If records aren’t updated frequently, the information may become inaccurate, along with the metadata. Outdated records can be correctly identified by outdated metadata. While, records that have changed without updating the metadata may be flagged incorrectly, and end up being excluded from or included in the wrong queries, reducing the effectiveness of the data itself.

2. Data Structure Changes

Sometimes new tables or columns are added to a database, yet not every record has been updated to fit this new structure. When new queries rely on the updated data structure, metadata that doesn’t fit that structure gets lost in the void. This is especially true for semi-structured data where some queries might catch those records, indicating that all of the metadata is updated, but in reality, the majority of the metadata is stale.  It’s not just metadata either, these unexpected schema changes can create data reliability issues downstream in your pipeline. 

Stale Metadata Causes Many Problems

As you can imagine, stale metadata can wreak havoc on a database for its organizational users and external consumers relying on its proper function. We discuss some of the most common issues stale metadata creates:

Broken and Useless Queries

The largest problem stale metadata causes is query malfunction. If important queries rely on metadata to run properly, it goes without saying that faulty metadata will make those queries useless. If the queries are useless projects that rely on these queries will lack accurate and up-to-date information and are therefore likely to fail.

Insufficient Metadata

Especially with data structure changes, insufficient or outdated metadata can become an issue when manual input is required – like keywords or user assignments. If queries rely on this kind of metadata and these records get lost, it can result in delays and low-quality projects while increasing the chances of more data becoming stale.

Security Policy Violations

Queries that rely on metadata for security checks might not find sensitive data whose metadata is outdated, insufficient, or otherwise stale. Conversely, new sensitive data may be added but fall through security policies due to stale metadata (for instance, not having a proper mask or encryption). Failure to identify sensitive data can increase the likelihood of a data breach.

Compliance Issues

In the same vein, if security-related queries and checks cannot identify records properly due to stale metadata, this can cause a mess of compliance problems. Depending on your locality and the severity of the issue, sensitive data found in audits that slipped through your organization’s checks can result in legal penalties and fines.

Lost Productivity and Wasted Resources

All of these issues amount to wasted time, money, and energy. In addition to fixing the stale metadata itself, all the other problems that stem from stale metadata must also be addressed. While this is happening, other data projects are put on the back burner, reducing productivity and money in the process.

Keeping Metadata Fresh

While all these problems can cause more than just a headache, avoiding them is relatively easy: just keep the metadata from becoming stale in the first place. There are two ways to keep metadata from becoming stale:

1. Automation

Checking metadata frequently is the most obvious solution although frequent scans can cause a lot of operational overhead. Instead of handling these checks manually, find ways to automate the process with a happy balance between frequency and resource use, to keep your organization’s budget under control.

2. Continuous Updates

While catching stale metadata in automated queries is a great way to react to the problem, it’s even better to be proactive about it through continuous updates. In other words, whenever data is accessed, be sure to check its metadata and update that information as necessary.

Continuously Updated Data Inventory with Satori

Having stale metadata is one way to cause data projects to fail. This could be due to failing to update metadata or by having multiple data structures and metadata updates falling through the cracks. 

Satori provides easily implemented processes to continuously discover and classify sensitive data, therefore ensuring that your metadata doesn’t become stale. 

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About the author
|Content Specialist

Lisa is a content specialist with an academic background, blending strong analytical and communication skills, to develop engaging instructional content. Lisa has held positions in higher education and public policy and environmental think tanks.

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