Data is the raw material for today’s business processes. Developing an effective data storage and processing it has become more than a back-office or IT challenge. Businesses flourish when the acquired data is clean, resilient, and easily flowing. However, if it is unmanaged, sluggish, or difficult to release, businesses might suffer.
What is a Data Lake
A Data Lake is a space where massive amounts of data of various sorts and structures can be consumed, stored, evaluated, and analyzed. Data Lakes, in particular, make it simple for Data Scientists to mine and analyze data through less processing, facilitating automatic pattern identification, and serving as a useful online repository.
What is a Data Swamp
Compared to a Data Lake, a Data Swamp lacks organization or structure. The absence of curation leads to almost no active management throughout the data life cycle, as well as less contextual information and Data Governance.
Data Lakes and Data Swamps are both data storage methods that combine structured and unstructured data into a single repository. Many businesses have developed a Data Lake to handle data storage, access, and usage concerns. Unfortunately, unmonitored Data Lakes can easily degenerate into Data Swamps or dumping sites, making it difficult to locate, analyze, or use data.
But what makes the Data Lake into a Data Swamp? Here are a few clear indicators that our experts, at Saransh, believe you can look out for, to see if your Data Lake is turning into a Data Swamp.
Untidy Data
The distinction between a Data Lake and a Data Swamp, is cleanliness and uninterrupted data flow. Dirty data tends to complicate every other downstream process, and it’s a clear indicator that the Data Lake is turning into a Data Swamp. As data ages, it not only becomes outdated, but it can also become faulty, duplicate, or misleading, due to unreflective changes. This corrupted data then damages evaluation and forces errors. Strategy for cleansing data on a regular and recurring basis is called Data Auditing.
Lack of Metadata
Metadata describes and provides information about other data. When implemented correctly within a Data Lake, it functions as a tagging system that allows users to search for various data sources. A lack of Metadata hinders data curation, preventing active data management, also inhibits accurate data governance. Metadata can also be used to develop a tier system storage structure, which prevents a Data Lake from becoming a Data Swamp.
Irrelevant data
When businesses are unable or unwilling to impose limitations on data amounts, they may discover that a well-organized Data Lake turning into a Data Swamp filled with information, that may be useless. Data Lakes benefit from a determined effort to block them from becoming a dumpsite for all and any data, just as lakes benefit from the filtering capacity of surrounding rocks, plants, and soil to sieve out incoming contaminants.
Ungoverned data
Data governance ensures that data quality is maintained and that data training efforts are on track. Poor or non-existent data governance, on the other hand, results in data that is exploited, retained for an inordinate amount of time, or otherwise corrupts data-driven business operations. As the Data Lake expands, it becomes vital to adopt robust data governance procedures to prevent it from becoming a Data Swamp.
Lack of automation
Automation is especially useful in preventing Data Lakes from devolving into Data Swamps. It can standardize data usage techniques across platforms and handle all raw data in the same manner. Without the use of automated data management and cataloging procedures, business efforts are unlikely to keep up with the developing Data Lake.
A Data Lake is the most effective approach to provide rapid, efficient, and impactful analytics, visualization, insights, machine learning, and other functions from your massive data warehouses.
Talk to our experts, by writing to us at info@saranshinc.com, to learn how you can keep your Data Lake from becoming a Data Swamp.