The difference between a data swamp and a data lake? 5 signs

In short, a data lake equips companies to retrieve and use their data effectively. But, data swamps can make both those tasks exceptionally difficult and perhaps impossible. Here are five signs that what you think of as a data lake is actually a data swamp:

1. A lack of metadata

Metadata is information that describes other data. When appropriately used within a data lake, it acts as a tagging system that enables people to search for different kinds of data. Metadata can also create a tiered storage structure that stops a data lake from turning into a data swamp. Companies might organise their data with metadata tags denoting the source of the data or how it relates to a company event.

It’s also worthwhile to depend on metadata to help describe time frames or the age of the data. If an organisation made a metadata tag titled “2018 User Feedback Forms,” that metadata describes both the type and age of the information. Some metadata tags are less specific, such as “Twitter.” Even in that case, the people working with the data can use more than one metadata tag for a piece of information, thereby adding context to it.

Data swamps don’t have metatags. Then, the people accessing the data run into a problematic scenario where they may know exactly what kind of information they want to find but have no idea how to go about doing it.

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2. It contains irrelevant data

Some company leaders get so excited about the fact that it’s now relatively easy to collect data that they start doing it without a clear goal in mind. A data lake can transform into a data swamp when companies don’t set parameters about the kinds of data they want to gather and why.

When enterprises can’t or won’t set limits on data amounts, they could find that what was once a well-organised data lake is now a data swamp flooded with information they may never need. Corporate silos can exacerbate the common problem of gathering data without rhyme or reason.

Perhaps departments have differing opinions about which kinds of data are most useful to a company at a given time. For example, the marketing department would likely want a different type of information than what’s most prized by the human resources department. Bringing relevance to data and ensuring it goes into a data lake instead of a swamp means getting everyone on the same page about when, why and how to acquire data.

Companies leaders should also adopt future-oriented mindsets data collection. But, when doing that, they must be careful not to fall into the trap of gathering data “just in case.” Making clearly defined goals about data usage helps prevent overeagerness when collecting the information.

3. No data governance

Data governance defines how to treat data, who should handle it, where the data goes, how long companies retain the information and more. Excellent data governance is what equips your organisation to maintain a high level of data quality throughout the entire data lifecycle. Data swamps lack data governance.

The absence of rules stipulating how to handle the data means that everything gets dumped in one place with no thought of how that practice negatively affects the future use of the data. Failing to implement data governance also puts organisations at risk for landing in regulatory hot water, especially if they get audited.

Data governance also means assigning roles that give designated people access to and responsibility for data. One of the advantages of leading software systems that allow database access is that they let users access content without writing complex queries. That ease of use is an excellent characteristic that makes using data more straightforward. But, data governance should still entail only allowing certain people to work with the data.

Making data governance a priority as soon as companies start collecting data is crucial. Thanks to data governance, the data has systematic structure and management principles applied to it. Then, it’s easier to use the data in valuable ways that fit a company’s needs.

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4. No automated processes in places

If your organiation hasn’t even entertained the idea of applying automation to help maintain a data lake, it could become a data swamp before people realise what’s happening. Automation is becoming increasingly crucial for data lakes. It can do things such as standardise data usage practices across platforms and process all raw data in the same ways.

However, bringing automation into the equation does not excuse company leaders from ironing out a plan for how to use data. They need to settle that aspect first, then figure out how automation can help them achieve the identified goals.

5. Failure to make a data cleaning strategy

No company intends to make a data swamp. The problem is that data lakes can deteriorate and become data swamps unless enterprises make and stick to plans for regularly cleaning their data. If the data has errors, or there are duplicates in the database, it’ll be difficult for company leaders or stakeholders to trust the information.

And, dirty data can cause companies to come to incorrect conclusions. Then, data contributes to poor decision-making. Even worse, years or even months could pass before someone realizes that the data was not as accurate as it seemed — if they ever do. Building a data governance strategy as suggested above should reduce many data quality issues.

But, companies must also take a further step and decide what specific things they should regularly do to keep the data lake clean. Data becomes murky without that kind of forethought. People quickly become overwhelmed by the thought of trying to restore order to a previously pristine data lake that morphed into a swamp. Prioritizing data cleanliness avoids issues and makes the information maximally useful.

You Can Avoid the Hassles of a Data Swamp

This list gives five common characteristics of data swamps, but it also provides ways to steer clear of problems that could create them. A data lake allows straightforward access to meaningful data.

With that in mind, you have compelling reasons to recognise the differences between data swamps and data lakes and strive to maintain the former.


Kayla Matthews

Kayla Matthews, is a tech journalist and writer.

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