Best practices for data documentation

Data is the lifeblood of an organization, but without proper documentation, it can be difficult to find the data you need. The lack of comprehensive data documentation has been a major roadblock for organizations that want to derive value from their data. Fortunately, it's easier than ever before to document your data using metadata management tools and data catalogs. In this post, we'll explore how you can use these tools as well as some best practices for managing and documenting your data sets over time.

In your quest to document data, you may find yourself wondering: “What is the point of documenting data anyway?”

You aren't alone. Data documentation can feel like an unnecessary endeavor that takes up time, resources and money. However, if you want to get the most out of your data—if you want it to be useful and meaningful—you need a strategy for documenting it that ensures its integrity and longevity. But what exactly does this mean? And why is it so important?

The data documentation process allows everyone in your company to find, understand and trust the data they have in front of them.

Without good documentation, it's impossible to know whether the data you're looking at is accurate or trustworthy. If you're not sure how often a field should be updated, what kind of information should be present in it, or where a field comes from, how can you trust that it's accurate?

Documentation helps people use data effectively. It lets them know what kind of information they should expect to see in a field and when it should be updated or changed. It also makes it clear what types of changes are allowed (for example: adding extra columns).

Documentation makes it easier to find fields within your database tables. Without good documentation, it's difficult for someone unfamiliar with your database to figure out which fields are relevant to their work — especially if they have no prior experience with that particular database system. Without good documentation, even experienced users may have trouble figuring out where some fields are located or what information they're supposed to contain because they don't know what other fields exist within that table!

You may be wondering, “What kind of a person would want to document data?” The answer is simple: a person who wants to be responsible for the quality of their work. If you are working on projects that require data documentation, it's your job to make sure that they are well documented and easy to read. This will enable your team members and future readers of your work to understand what you've done with the data and why you did it in the first place—and that's important because it helps keep everyone on the same page (or spreadsheet)

Before we dive into the nitty-gritty, there are a few things to consider:

• Think about what you want to accomplish with your data documentation. Ideally, it should be used throughout all aspects of your work—not just as a one-off project at the end of a research cycle. The more thought and effort that goes into creating a robust system for tracking and archiving information, the better off everyone will be in terms of efficiency and accuracy.
• Assess what type of documentation is needed before beginning any project. While many fields require standardized formats (think SPSS), every field has its own particular requirements when it comes to recording data and results; take these into consideration when deciding how best to proceed with your documentation process.
• Keep track of what's going on! This seems obvious but sometimes even small details can easily slip through the cracks when multiple people are involved with different parts of an investigation or experiment; having an organized system will help ensure nothing gets lost along the way (or worse—that someone accidentally uses something twice).

Documenting data is synonym with enriching the metadata linked with your data.

Metadata is data about data. It can be used to find, understand and manage data, which is important for business owners as well as researchers. Metadata helps you find the right information so you can make better decisions about your company and its future. Metadata tied to a data asset includes: who created it, when it was created, how to interpret it, who owns it, and many other details.

There are different types of metadata:

• Descriptive metadata includes information about the source or purpose of a dataset, such as when it was created or who created it.
• Administrative metadata includes publisher contact details or how many times a dataset has been downloaded.

You can create metadata manually or automatically. For example, if you create a new document in Microsoft Word then this automatically creates some metadata about who created it and when they created it. You may also choose to add more descriptive information, such as adding descriptions for each section of your report or marking up keywords in the text so that search engines can find those words easier.

A data catalog is a central repository of all the structured metadata that your organization collects, stores, and uses. It provides a single, searchable location for finding out about all of your data assets.

Why is it key for data documentation?

A good data catalog makes it easier for anyone in your organization to find and access the information they need to do their jobs. It also helps you keep track of what data you have so you don't accidentally delete or misplace it. And if something goes wrong with one of your systems, having an up-to-date inventory of all your data will make fixing it much easier.

It also makes sure that any changes made to the metadata are reflected across all relevant systems so that everyone works with accurate information at all times. This helps avoid errors during analysis or reporting as well as unnecessary repetition of workflows such as uploading files or sharing documents across teams - saving time on repetitive tasks while reducing potential sources of error due to inaccurate information being used in multiple places simultaneously (like when two people merge datasets without knowing if they're using different versions!).

Data documentation should be an ongoing process, not a one-time exercise.

Like any other important aspect of your business, data needs to be documented in a way that makes it easy to update and find. If the data isn't documented in such a way that you can easily find it, then it doesn't matter how well it's structured—you'll never be able to use what you've created. Similarly, if the structure of your data doesn't allow for easy updating, then any time you want to make changes or additions (which will happen), you'll have to go through some serious pain and suffering just to get something done properly. If there is one thing to remember from this article, it is: document at the ROOT.

In short, data documentation is a crucial step in the process of ensuring that your data can be easily discovered and used. Data can be reused to generate insights, but only if it’s well-documented and accessible. Data documentation helps ensure that your organization has a solid foundation for further work with its data assets, without relying on one person's memory or knowledge base.