16 May Data Swamps: When Info Gets Murky

Understanding Data Swamps

Everyone saves files, media, and programs to a set of folders on their PC system or network. Anything from documents to videos makes up these files. Over time, the more files you have, the need for organization rises, accomplished by separating said files into their own folders. The idea, of course, is to increase accessibility and avoid clutter.

But, even with the best intentions and documentation, digital clutter is a reality. This is truer for networks that handle thousands of files on a routine basis. How are they saved? Where do they go? Quickly, file management and tracking turns into a wild goose chase, and thus storage of data shift into the dreaded data swamp.

What is a data swamp?

This term means a lot of things in the IT world. Primarily, it’s the ungoverned, unmoderated storage of “data,” where data means all files stored on a network/system. In a typical scenario, data starts out as a “lake,” where it’s accessed, saved, analyzed, and moved. But without proper moderation and governance, it can quickly flood into a swamp.

Lakes are good for archives and data analyzation, and proper storage translates to agility across a network. After all, if you can find a needed file/data node in a few seconds, the better right?

Over time, by years, enterprise data can get harder and harder to manage. Or, older solutions which once worked (like warehouses) are no longer viable. Without transitioning the info into something meaningful, you get the data swamp.

Data swamps present these core problems:

• No administrative properties and lack or organizational governance
• Disorganized information, lack of file management, corrupted files
• No metadata present in files
• Metadata is incorrect in file formats

When these problems stack up, they create serious friction in production and access management.

What happens with an expanding data swamp?

• Files and data are increasingly difficult to find, if not impossible
• Information is greatly reduced in overall quality, especially without proper metadata
• Redundancy and loss of time, cutting into capital and slowing down overall enterprise tasks
• Loss of data and/or unusable data

Even without touching on the finer details of data swamp problems, you can understand the problem. Think about traditional and physical file management and how much more difficult it is when everything is disorganized room. A simpler metaphor is a cluttered room versus a cleaner room.

It also costs everyone time and money. Time spent searching for files and data is time away from mission-critical tasks. It adds up and prevents staff and management from focusing on their objectives.

Ironically, swamps can result as a consequence of data lakes, where there is some form of data storage, transfer, and organization strategy. But as enterprise needs expand and incoming data greatly increases, it can devolve quickly.

How to avoid data swamps?

Isn’t that the question? No easy answer exists for this, and it primarily involves building out an organizational architecture. Doing so requires an understanding of what data is coming in, how it’s processed, and where it should go.

Like any IT philosophy and architecture, third-party help is recommended when in-house solutions are not enough. If you want assistance with handling data and avoiding data swamps, contact Bytagig for additional information.