“Data is the currency of the future,” many experts have predicted. The 21st century has been characterized by the astounding amount of data we’ve gained access to. But what happens if this data isn’t properly stored? A data swamp begins to develop, and accessing that data becomes difficult and sometimes impossible. 

The internet, social media, the Internet of Things (IoT), and advances in data stores and operations have provided a way to capture all sorts of data. This kind of data ranges from shopping habits to personal preferences and more. Additionally, IoT has provided the ability to gather copious amounts of data on processes, machines, weather, and almost anything you could think of. 

When processed and analyzed correctly, this information can provide key insights to improve efficiencies and increase effectiveness. However, storing data without proper organization and security leaves companies open to exploitation, data breaches, and other potential hazards and liabilities.

This article will discuss what happens when data is left unorganized. We will also explore the dangers and liabilities of improperly storing data. These topics are especially relevant as governments are tightening regulations on securing personal data. 

So what is a data swamp, and how can you avoid one forming in the first place?

What Is a Data Swamp?

In simple terms, a data swamp is an unorganized collection of data. Imagine a mailroom with a constant stream of letters and packages entering the system. These letters are typically sorted based on their destination, size, and even mail priority. This makes it easy to sort and send them on their way.

But what if there was no proper sorting or set process for handling the constant supply of packages? The mailroom would become chaotic, with letters going to the wrong destinations and unknown packages cluttering the area. A similar situation happens when your data infrastructure isn’t organized or ready to process incoming information.

On the other end of the spectrum are data lakes. These are established and adequately set-up reservoirs of data. They provide a regulated environment for data to be stored and organized so that it’s easily accessed by the proper tools and reports later on.

Data lakes are scalable central repositories that can withhold large amounts of raw data. This data can be organized and assigned the proper metadata. However, it doesn’t structure or process the data. It simply serves as an ideal environment to manage the raw data until it’s needed. This organization is what prevents a data lake from turning into a swamp.

How Does a Data Swamp Start?

The advantage of utilizing a data lake storage method is that it allows storing any type of data. Whether IoT device, social media data, or machine learning from log files, anything can go into a data lake. Mainly, the schema is determined only when the data is read.

In contrast, the schema of a traditional data warehouse is set before any data is being written. While this ensures that the information is appropriately stored and organized, it introduces restrictions and limits for data that may not have been accounted for. It also requires data to go through a data extraction process like ETL (Extract Transform, Load) or ELT.

So how does a data lake turn into a data swamp? A data lake can quickly turn into a data swamp when unorganized data without precise metadata is introduced into the system. This can quickly flood the storage system with useless and irrelevant data that clutters the system and makes retrieval difficult.

This can happen in several different ways:

1. The data lake lacks proper protocols and guidelines for organizing and filtering data. This can lead to a build-up of old and irrelevant data.
2. A data extraction process like ETL/ELT (Extract, Load, Transform) is improperly set up or integrated. ELT takes company data and essentially creates an identical copy, so if the process isn’t properly implemented, it will quickly flood the data lake with unidentifiable mirrored data.
3. The data lake becomes a dumping ground for inapplicable or unrelated data.

How Do You Dry a Data Swamp?

So, how can you clean up a data swamp and transform it back into a pristine data lake? Here are several guidelines:

Implement proper processes and guidelines: First off, be sure your existing protocols are doing their job. Without clear-cut guidelines, you will have a neverending stream of irrelevant and unorganized data. Initial protocols should serve as a filter to ensure only valuable data makes its way into the storage system. Additionally, it should be assigned the proper metadata to be located and called when needed.

Keep it relevant: Don’t hoard data if it isn’t relevant. Establish parameters to remove and clean up old and outdated data. This can be done by establishing processes to ensure the lake accepts only certain data. The obtained data should have a clear purpose and role to help prevent the hoarding of useless information. This, of course, should be balanced with the notion of a data lake storing data that may not be in immediate use but kept in a raw form for future use.

Establish necessities and goals: Just because you can collect any type of data doesn’t mean you should. Establish clear intent on what kind of data you want to collect and the goals achieved with it. Ask yourself what kind of reports and insights you are looking to gather. Then, stick to collecting the relevant data. This will help ensure your data lake doesn’t fill with irrelevant or useless information.

Know where you have sensitive data and control it: It is one thing to hoard useless performance data, but hoarding sensitive data can also be a liability in terms of legal obligations, compliance, and security risks. Therefore, you must always know where you have sensitive data within your data stores (by performing continuous data classification on it). It is then vital to have clear data access policies for sensitive data. 

Conclusion

Data plays a vital role in today’s business landscape. Data is precious, whether it’s offering insights into the consumer’s mindset or improving efficiencies through machine learning. And that’s why it’s crucial to treat it as such. Maintaining and managing a pristine environment for your data ensures it’s adequately protected and ready for use.

In other words, just because it’s (relatively) cheap to store data and then process it doesn’t mean that you can be chaotic about your data lakes and data warehouses.