(highlight:: 1. Build strong file naming and cataloging conventions
If you are going to utilize data, you have to be able to find it. You can’t measure it if you can’t manage it. Create a reporting or file system that is user- and future-friendly—descriptive, standardized file names that will be easy to find and file formats that allow users to search and discover data sets with long-term access in mind.
To list dates, a standard format is YYYY-MM-DD or YYYYMMDD.
To list times, it is best to use either a Unix timestamp or a standardized 24-hour notation, such as HH:MM:SS. If your company is national or even global, users can take note of where the information they are looking for is from and find it by time zone.)
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(highlight:: 2. Carefully consider metadata for data sets
Essentially, metadata is descriptive information about the data you are using. It should contain information about the data’s content, structure, and permissions so it is discoverable for future use. If you don’t have this specific information that is searchable and allows for discoverability, you cannot depend on being able to use your data years down the line.
Catalog items such as:
Data author
What data this set contains
Descriptions of fields
When/Where the data was created
Why this data was created and how
This information will then help you create and understand a data lineage as the data flows to tracking it from its origin to its destination. This is also helpful when mapping relevant data and documenting data relationships. Metadata that informs a secure data lineage is the first step to building a robust data governance process.)
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(highlight:: The 3-2-1 methodology
A simple, commonly used storage system is the 3-2-1 methodology. This methodology suggests the following strategic recommendations: 3: Store three copies of your data, 2: using two types of storage methods, 1: with one of them stored offsite.)
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(highlight:: 5. Commitment to data culture
A commitment to data culture includes making sure that your department or company’s leadership prioritizes data experimentation and analytics. This matters when leadership and strategy are needed and if budget or time is required to make sure that the proper training is conducted and received. Additionally, having executive sponsorship as well as lateral buy-in will enable stronger data collaboration across teams in your organization.)
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- data culture,