The Art of Content Data Management

What is content data?

We all know what content is, though we may not realize or be responsible for all the forms it takes. Video, audio, images, and articles are the most basic. However, our digital world has proliferated new types and variants of this list that could be uniquely described as interactive content, social media posts, digital products, recipes, native ads, and so on. 

All of this content has respective data attached to it – often referred to as metadata. There are a large collection of fields attached to each piece of content and that number and the fields themselves vary depending on the content type. 

Here’s a sample list of metadata fields that could be associated with your most common content:  Asset ID, Title, Content Type, Description, Category, Media Format, Content Creator, Content Owner, SEO Keywords, Agency, Client, File Size, Language, Rights/Usage, Season, Geo, Industry/Vertical, Job Number, Product Line or Brand, Region/Location, URL, Usage/Rights, Video Length, Word Count, Publish Date, Expiration Date, Copyright, Copyright Year, Version, Tags and so forth. 

We have a more comprehensive list of content data attributes here that include multimedia content, digital campaigns, product catalogs, and digital coupons. If you oversee any of these areas, I would check out this checklist

Why is content data management important?

Getting everyone involved in the content creation and publishing process aligned with content data management is essential in today’s fast-moving and competitive digital-first world. 

We’ve identified a number of issues negatively affecting your content performance when your content metadata is missing or incomplete:

  • Decreased visibility and key information available on external search engines and social platforms
  • Incomplete indexing and findability in your enterprise search functionality
  • Poor accessibility for people with disabilities 
  • Sub-optimal content display within your owned digital experiences
  • Lost opportunities within recommendation engines and dynamic related content 

Learn more in this in-depth article, “The Importance of Complete Content Metadata”

Areas of major impact

We’ve also outlined 6 key areas where you can optimize your content data that can dramatically improve revenue performance and efficiencies across teams. If you’re Head of Content or a related role, you may not directly work on these endeavors so you may not realize the additional value you and your team would deliver if you improved your content data management process:

  • Internal DAM search: Thorough metadata makes it easier for your users to search and find the digital assets they need
  • Content/audience matching: If your digital assets are tagged sufficiently, and your campaign data is as well, you can match the right piece of content with the right audience
  • Site search/filter applications: Thorough metadata also makes sure that customer-facing filtering and search utilities are as effective as possible
  • Customer-facing presentation layer: The metadata creates a more comprehensive presentation layer, e.g. metadata on product listings
  • Data layer optimization: If you make use of the data layer for passing values to execution and analytics systems, metadata gets stored in the data layer
  • Search engine optimization: If your digital asset is web crawlable, the metadata enriches the SEO value

We go deeper on all of these opportunities here, “Strategic Guide to DAM Metadata”. The details of this guide span into your CMS as well. 

Where is this process generally broken?

There are a few obstacles that organizations bump into when dealing with content data/metadata:

  • No standardized language or naming conventions for metadata values
  • No workflows or governance to complete metadata requirements
  • Inefficiencies within CMSes/DAMs themselves for bulk management of metadata

Again, the details of these issues are found in the Strategic Guide linked above.

How can content data management be improved?

There are a number of areas where you can improve your content data management process and they are ultimately addressing the 3 main areas that are mentioned in the previous section.

Standardize language and naming conventions for your content data. 

Before you do this, however, you may need to do a cross-team alignment on a comprehensive marketing taxonomy. This project is the foundation for standards and conventions. This requires serious collaboration across a number of teams who may already have fragments of taxonomies that are relevant to their work. All of these fragments need to come together to create a holistic taxonomy that can then be reapplied uniformly everywhere it’s required. 

Implement workflows and governance around the taxonomy you’ve created and agreed upon.

Often organizations create a theoretical marketing taxonomy but have difficulty putting it to actual use. Traditional taxonomy management solutions help build the overall framework/model but they don’t actually support collaborative data management: creating and managing the thousands upon thousands of pieces of content large companies create annually.  As with any new overhaul within an organization or department, you’ll need to also create a successful change management program. 

Improve the efficiency of your existing content management solutions. 

This is sometimes difficult to do within the technology itself. Perhaps there are plugins/add-ons/extensions native to the platform that may assist. It’s likely these utilities don’t work well with that comprehensive marketing taxonomy your organization has ultimately created. We recommend finding a solution that works well with both – manages your taxonomy and can be used to create and sync the content data to the appropriate destinations at scale. 

The challenges listed above are what Claravine solves for the Fortune 1000 and more. Want to learn how we do it? Get started with a consultative call.

Resources

Get started with a 30-minute discovery call so we can help understand your current content data management challenges: