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Crucial guidelines for organizing and searching meta data in 2020

Developing a logical data perspective is one of the most complicated aspects of better data management.

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Metadata has become very important for organizations managing digital content.  Metadata makes it much easier to organize and filter content for a variety of purposes. Unfortunately, structuring metadata appropriately is still a challenge. 

There are a lot of things that you need to keep in mind when organizing metadata. Some of the most important principles are discussed in detail below. 

Familiarize yourself with FAIR principles 

In 2016, Scientific Data published a new set of principles for data stewardship and management. These were known as the FAIR guiding principles. FAIR is an acronym that stands for the following: 

  • Findable 
  • Accessible 
  • Interoperable 
  • Reusable 

The rules for applying these principles can be rather complex. However, you will be off on much better footing if you are familiar with the basic concept at first. All of your metadata creation and organization practices should revolve around the basic FAIR principles. This is important for data efficiency and data risk management

Take advantage of reusable workflows 

Reusability is one of the most important benefits of metadata utilization. There are multiple ways that you can reuse processes and content for optimal efficiencies, like digital asset management solutions

Of course, reusing metadata itself is the most obvious. There are other reusability options that you must leverage as well. This is also important for streamlining audit workflows

Reusable workflows should be taken advantage of as much as possible. Many content creators are more than happy to allow the workflows to be reused and re-purposed, provided proper attribution is given. 

Create data fields with user search intent in mind 

When you create new metadata, you can add custom data fields. More data fields both provide additional context and improve the searchability of datasets, which is one of the core functions of metadata. 

If you were to interpret this in the most reductionist way possible, it would appear that you should add as many data fields as possible. Unfortunately, there are a couple of problems with this logic: 

It takes a lot of time and effort to add new data fields. If you create lots of unnecessary data fields, then you will be putting a large burden on your content creation staff members. 

Too many data fields can push your data constraints to their breaking point. You will have a larger mass of data, which can reduce the efficiency of big data mining process. 

You need to find the sweet spot between adding enough data fields and improving the efficiency of your workflows and data extraction processes. The best way to do this is by selecting your data fields strategically. You need to consider the search options that the average user will take advantage of when they need to mine your metadata. This will help you choose the right data fields to enhance efficiency if the process. 

Build keywords around database best practices

Keywords and meta-data are two similar, yet distinct concepts that are frequently confused. Keywords (often referred to as tags) enhance the ability of users to search metadata efficiently. 

There are many advantages of keywords, but only if they are used appropriately. You need to understand database best practices to do this. This is not just important for helping users search meta-data. It is also important to ensure analytics and reporting is as accurate as possible.

Outline a logical data perspective 

Data storage and accessibility are two of the most obvious factors that need to be taken into consideration when managing data of any kind, including metadata. However, there are other considerations that must also be taken into account. The transit path of data is a frequently overlooked variable. Although it might not seem as important from a general perspective, failing to optimize the path of your data can create significant challenges later. 

This is why every organization needs a logical data perspective. This is a framework that controls for the following: 

  • Records of where data has originated 
  • Showing a trajectory path of Point A to Point B as data moves through the organizational ecosystem 
  • A clearly laid out plan for the delivery of important data from its originating point

Developing a logical data perspective is one of the most complicated aspects of better data management. However, it is something that you cannot afford to overlook.

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