Data is a tremendously valuable asset that helps organizations to get insights into their day-to-day business operations. Using organizational data to make strategic and data-driven decisions enables businesses to grow in the competitive market. However, a lot of structured and unstructured data piled up in the data warehouse leads to a lack of quality control, negating the benefits of data and costing unnecessary expenses. The best way to maintain the quality of the data structure is to drain the data swamp.
Data swamps, a large opaque pool of data that arrives in multiple formats, are the major obstacle that prevents companies from mining valuable insights and improving their decision-making process. A lack of transparent data governance can cause havoc in data management, making it hard for organizations to extract value from their data. This guide will focus on how to drain the data swamp in 2022 and organize the data lake with meaningful information.
What Is A Data Swamp?
Using big data strategically could generate $3 trillion in value annually, indicates a McKinsey Global Institute report. When organizations follow a haphazard approach to storing and managing data, it leads to the building of data swamps that slow down data analysis.
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Due to unstructured data in the data swamps, data-driven businesses find it hard to find valuable data and make strategic decisions. The effective data structure in 2022 will help data scientists analyze information without dealing with disparate formats’ tangled mess. Some of the significant drawbacks of a data swamp in an organization include the following:
- The lack of sorted and properly curated data makes it hard for data engineers to access data structures in diverse formats and systems.
- Continuous data flow in the data swamp exacerbates the problem as data analysts deal with non-functional data that increase complexities.
- Having irrelevant data affects the overall core function of an organization, leading to less strategic business decisions.
- Locating and collating data without labeling and meta descriptions is hard, and data analysts search multiple files and sources to find the correct information.
- Lack of knowledge about data stored makes it challenging to implement clear data governance rules in a data swamp environment, leading to the risk of data breaches.
Difference Between Data Swamp And Data Lake
Data lake and swamp are related to compiling structured and unstructured data in one repository without needing a particular format. A data lake is an organized data structure heap where all the relevant business data is collected, managed, and stored to get essential insights.
In a data lake, data analysts divide the business data and label it to make it searchable and prevent the build-up of duplicate or unusable data. On the flip side, a data swamp is an unorganized space with loads of replication and unwanted data, making it hard for businesses to leverage data to its total value.
An unmanaged data lake can quickly become a data swamp if businesses fail to set guidelines for data relevance. That said, it becomes a cornerstone for organizations to invest in structuring the data lake and prevent it from becoming a swamp.
Inferenz data swamp organize services can help your business avoid unmanaged and duplicate information. Data analyst experts utilize the data lake best practices to structure data and prevent data swamps. Read our case studies here.
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Data Structure: How To Drain Data Swamp And Organize Data Lake?
Following the best practices to structure an organization’s data is the best way to drain the data swamps and access valuable data to make decisions.
- Ensure The Trustworthiness Of the Data
Companies must ensure that the stored data they use for business insights is reliable, trusted, and readily accessible for data analysts and engineers to make strategic and data-driven decisions.
- Implement End-To-End Strategy
Setting standards from the start, like monitoring connections, utilizing the cloud resources, scaling and automating data pipelines, and making intentional data-design decisions, is a dynamic data structuring approach during project planning that helps drain the data swamp.
- Ensure Relevancy Of Data
Instead of hoarding irrelevant data, organizations must establish specific parameters to remove and clean outdated data. Data obtained from different sources should have a clear purpose of helping the in-house team to make profitable business decisions.
- Define Data Ownership
An organization’s vast amount of data can overwhelm the in-house team, leading to data mismanagement. Organizations must figure out who will manage the data pools so that unmanaged data lakes will not become swamps.
Leverage The New Technology To Clean Up Data Swamp
Drowning in the sea of data can affect the business’s profitability and set it behind its competitors. Only a data structure company with cutting-edge technologies can streamline the data management process. They can create the proper data structure and algorithms that help businesses succeed, regardless of how swampy the data lake is.
If you are tired of having irrelevant data in your organization, let the tech-expert team of Inferenz help you to drain the data swamp with services and tech-enabled solutions.