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Companies are increasingly relying on business data to improve business processes. Data is a crucial part of any modern business. Companies must set the proper data management practices to ensure they acquire the correct data, protect it, store it, and validate it properly.
Even if you use personal data enrichment services or any data cleaning method, you might disrupt your data, lose it, or misuse it if you don’t have the correct data management practices. With good data management, process organizations can ensure they’re using data correctly and delivering the correct information to their customers.
Today we’ll share some of the best data management practices you should adopt to ensure you get the most out of this resource.
- Invest in some storage
You should have reliable storage even if you don’t rely on data or produce large volumes of information. Storage helps you keep your data safe while providing access at any moment. There are many options available for storage, and you need to choose the right one.
For example, getting enterprise-grade storage means you will have a lot of unused space you’re still paying for. Here are some of the storage options you can use:
- USB flash drives;
- Cloud storage;
- Optical solutions;
- External hard disks;
Some general storage rules are always to have three data copies to restore it in case of failure, use at least two storage methods, and store one of your storage options offsite.
Outsource your data gathering process
Data growth in the business world has sprouted several companies specializing in scraping data on the web. These organizations know the best resources for specific data, how to gather it quickly, and how to overcome scraping challenges.
Gathering data is a complex process that needs to be done correctly if you want to acquire the information you can use to get actionable insights. Most companies can’t answer a simple question –
what is firmographic data? Not to mention some other alternative data practices.
In other words, it’s best to outsource the gathering process to someone else and reduce your data management complexities. Just make sure to align your practices with your service provider.
Perform data documentation
One of the most critical data management practices is documentation. Companies often implement multiple documentation levels to get a clear picture of all the data, its context, how you can use it, and why it exists.
Everyone in your organization can find the information and definitions related to some data when there’s documentation. Data documentation also keeps local requirements and rules in check, ensuring comparability and interoperability.
Documenting prohibitions, restrictions, and authorizations related to data use also ensures the security and accountability of your organization. Ultimately, all data definitions and similar content can improve regulatory compliance.
Documentation levels you should implement:
- Context documentation;
- Software-level documentation;
- File-level documentation;
- Project-level documentation.
Metadata are descriptive tags you can add to the data you are using within your organization. These tags include user permissions, data structure, and contents. All of this information lets users within the organization discover and search for specific pieces of data.
If you don’t create metadata for your datasets, you won’t be able to use data pieces years later, and discovering content will be difficult. Data management is all about having the resources you need at your disposal and finding and using them efficiently.
Companies usually catalog things like:
- Names of data authors;
- The contents of a dataset;
- Field descriptions;
- The date of data creation;
- The place of data creation;
- The reason for creating the dataset;
- How this dataset was created.
Make data quality a priority
All collected data needs to be reliable and clean as data becomes outdated over time and might be completely relevant two years later, which means that your marketing and sales teams won’t be able to use it. Perform data purging regularly to prevent poor analytics, automation, and other similar processes.
On the other hand, it’s also important to train all your employees who access and use data to input and collect data properly. Everyone should learn the best practices to prevent mistakes, whether your data is added manually or with some automated software.
At the same time, clean and check all of your data before using it for reporting or analytics. You can also perform personal data enrichment to enhance existing data by adding incomplete or missing information.
Use a good data management tool
A quality data management tool is central to your data management process. Adding all of the data into a single platform makes finding information easy. Once you’ve done that, it’s possible to extract data scheduling and create datasets that work best for your needs.
Data management tools work great for external and internal data helping you configure that whole data governance process. These tools have robust analytics and cleaning features that make data management a breeze.
Companies also use these solutions to govern, catalog, and build data to improve quality overall. However, as mentioned earlier, you should invest in employee training to ensure everything uses the software with the correct practices.
Data management is a key part of the whole data process. Companies must know how to use data responsibly and accurately to prevent mistakes and avoid counter-productive effects. In other words, managing quality data poorly could lead to severe problems even if you have quality data at your disposal.