23 November 2022

data swamp vs. data lake

Data Swamp vs. Data Lake: Everything You Need to Know

The value of enterprise data is undeniable. Data is the real issue for any sales or marketing strategy. It should be noted, however, that there are various types of data and storage methods: including data swamp and data lake. But what exactly are the differences between the two? Let’s dive in.

Impact of data swamp vs. data lake on data collection and analysis

Data lake definition

A data lake is a centralized storage location. Gartner defines a data lake as “a concept consisting of a collection of storage instances of various data assets. These assets are stored in a near-exact, or even exact, copy of the source format and are in addition to the originating data stores.” It contains raw (unprocessed and unanalyzed) data and unstructured data from multiple sources. As a result, there is no hierarchy or organization among the various data elements.

Once collected, each data element is assigned a unique identifier. Later, the data lake can be queried to generate more relevant and accurate data to answer a business problem.

It is important to note, however, that this accumulation of unstructured data can be difficult for the company to manage. This can ultimately affect the reliability and quality of the data.

Data swamp definition

When a data lake is not properly controlled, it can become a data swamp. A data swamp is a data lake containing unstructured, ungoverned data that has gotten out of hand.

A data swamp is usually the result of a lack of processes and standards. Data in a data swamp is difficult to find, manipulate, and—inevitably—analyze.

When it comes to data swamp vs. data lake, data lakes are much more preferable to data swamps. And for good reason! It is easier to benefit from organized, easy-to-use data rather than being confronted with a plethora of unusable data that could be the source of incorrect insights.

Data swamp vs. data lake: main differences

There are some major differences between a data swamp and a data lake. In this case:

  • Unlike a data lake, a data swamp lacks metadata, making searching for information difficult.
  • Unlike a data lake, a data swamp contains irrelevant and unusable data.
  • A data swamp lacks governance. However, precisely, this data governance (i.e., who processes the data, where the data goes, and so on) allows organizations to maintain a high level of data quality.
  • A data swamp typically lacks any data cleansing strategy for removing errors or avoiding duplicates.

Preventing data lake from becoming a data swamp

Now that you understand the basics of data swamp vs. data lake, there are a few ground rules to follow to avoid turning a data lake into a data swamp. One option is to collect less data or to focus only on data that can truly add value to the company.

Another solution is to use automation to extract relevant data or perform cleanup operations. Finally, it is critical to specify the problem to be solved using this data well in advance. This makes it easier to remove irrelevant data in order to collect only the data that is truly valuable.

The importance of keeping data clean and meaningful cannot be overstated. If you can’t find what you need, it doesn’t matter how much data you have because it won’t be useful. Metadata enables users to discover the information they need without having to ask for help from IT or risk errors during self-service analysis. Metadata was once costly and time-consuming to build, but these days with new database technology it is becoming easier than ever before to keep your data clean and usable without having to spend a small fortune on it.

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