Keeping your data clean and avoiding data swamps
Data and information prove invaluable resources for the long-term goals of any enterprise. It allows organizations to understand their demographics, plan for future expansions, market their services, and how scale infrastructure (among things). Today, however, collected data emerges from numerous points, and businesses of all sizes are swimming in this data deluge. “Big Data” has been the go-to phrase, and for good reason. There are abundant metrics, along with the means to collect and store information.
The problem, however, is saturation. Data becomes untenable when it’s stored in inaccessible formats. This disorganization becomes a “data swamp,” where data cannot be effectively analyzed, used, or managed. Avoiding a data swamp should remain a priority for any organization relying on information to conduct business services.
It starts by developing a plan.
Essential steps to keep your data lake pristine
Getting ahead of data management is the fastest way to achieve long-term success and avoid a data swamp. Doing so is a combination of internal planning, analytics, and strong data governance.
It starts with a comprehensive data management plan. What is it and how do you deploy one? There are several steps and methods to form a powerful data management plan.
Essential staff for data governance is where any effective management plan begins. You can assign as many staff as necessary, but whether it’s a single or experienced team of professionals, their goal is to oversee all steps in data management. Selecting the right person(s) for data governance relies on a few factors: their experience with data management, team building, and knowledge of data.
The responsibilities of data governance vary, but, there are always principal characteristics of the position:
- Setting the data policies to be followed
- Ensuring quality of incoming and stored data
- Data management
Outlining data policies will waterfall into every part of the data lake, so creating a strong but accessible plan benefits everyone. By accessible, we mean easy to understand. What goes into said data policy will vary, though typically includes data privileges (who can see what types of information), involvement between staff, management, and stakeholders, and data integrity (quality and value of data).
Removal of Redundant Data
Old, obsolete, and useless data all contribute to forming a data swamp. Routinely, you need to delete data and information no longer relevant to your organization. Duplicate and old data also fall under this category.
Defining and Identifying Important Data
Information most important to your organization is what needs prioritizing. Defining important data is adjacent to your business goals and what you aim to achieve. If you want to improve customer engagement, then demographic data would be prioritized. Unimportant data is information irrelevant to your enterprise goals, or, cannot actively enhance business operations.
Automated Data Management
Using automated tools to collect and analyze data is a critical way to improve performance and sort through important information and redundant data. Certain tasks should also be automated, like basic data entry. Reducing time consumption allows management and those in charge of data governance to focus on the big picture along with what they’re doing with the collected information.
One of the most important aspects of good data management is where and how it’s stored. A format that avoids saving useless data, is easy to access, and limits who can access it based on permissions prevents the formation of data swamps.
Where you store it can vary based on the needs of the enterprise. Data silos or warehouses are standard options. Sometimes third-party services for backup options are also potential solutions.
Part of avoiding data lakes is condensing and streamlining the presentation of information. Visualized data in understandable formats for all relevant parts of a business keeps everyone in the loop. It allows staff, management, and stakeholders to observe where their data is, how it’s used, where it goes, who is responsible for what, and the overall data architecture of the organization.
Lastly, securing data and shielding it from theft or loss is equally as important as other layers of data lake management. Threats facing data are numerous and growing. Increased expansion of remote services and technology, along with reliance on technology and digital solutions translates to an unpredictable threat climate.
Data governance needs to have a complete picture of where data is collected, how it’s stored, how it’s disposed of, and permissions. For example, having a thorough decommission process for staff departing from the business (terminated or otherwise) is one way to secure data. Unsecured data expands into a messy network and contributes to the creation of data swamps.
Setting up a proper data management plan with the right governance strategies is challenging, to say the least. That’s why reaching out to third parties for assistance is also recommended.
Bytagig is an experienced MSP with experts and resources to help with maintaining data lakes. For more information, you can contact us today.
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