Accelerating Time to Value with Adobe Experience Manager


Are you just starting out with Adobe Experience Manager or years into its use still thinking it could do much more for your business?
We talk with customers regularly who are at different stages in the Experience Manager adoption process and it’s become clear – metadata management on assets, pages and everything in between is a chore (to say it nicely).
It’s become obvious during these conversations (and spoiler alert, implementation of our technology with Experience Manager) that it’s paramount to get your data structure in order. Not just the structure, but the application of the metadata across all the assets you have or intend to create.
Don’t wait to consider standardization for assets and site data …and certainly don’t wait any longer if you have a mess on your hands.
So, whether you’re at the beginning, you’re migrating to Experience Manager in the cloud, or you’re in deep, read on to see what’s at stake and what can be done about it.

How to Solve – Getting to Value More Quickly

So how does an organization address the underlying issues that make or break content automation and speed to market?
Foundationally, they need to get standardized data inputs finally solved. They can no longer rely on antiquated and slow systems that are simply reactive, “clean-up” methodologies. Data has to be right at its point of origin and that starts with a unified data model aka content taxonomy.
And that also can’t just be a conceptual diagram/plan that people only philosophically agree upon. It has to be something that is implemented and applied globally, accessible across internal teams and external agencies – anywhere that data is being created (which is pretty much everywhere in an organization now).
This “unified data model” is one of the main areas we are solving here at Claravine. We talk about the importance of a unified data model here and provide an excellent outline of building this data model, or taxonomy, here.
The key is being able to bulk manage the data associated with thousands, maybe hundreds of thousands, of assets and pages created in your DAM/CMS. There is no easy way to do this natively in most of these platforms, including Adobe Experience Manager.

The Evolution of Content Management

For a long time, content product, operations and management was manual yet somehow organizations were still able to create incredible amounts of content and creative in their DAMs and CMSs.
Because it was so manual, key information about all of these assets and pages were overlooked, input incorrectly or without any convention or rules to make sure they made sense so the data about uses was useful for discovery, automation, personalization and analysis.
But now the days of content automation, powered by machine learning and artificial intelligence are upon us – including in our digital asset and content management systems. It’s touted as a promised land where teams can be more efficient and bring content to market faster. Which suggests a faster time to value for content and creative investments.
However, success with faster content publishing/production, including automated content infused with AI/ML is not realized with the toggle of a feature. It requires planning, set-up and a foundation of quality and useful data in order to make it all work.
Let’s explore some of the challenges and risks involved along with some recommendations on how to get it right and speed up getting value from your content and related technology investments.

Challenges and Risks of Content Automation

Relying on an AI/ML system (in Adobe’s case, Sensei), has some notable risks. VentureBeat published an incredibly relevant article that covers one of the major risk factors: data quality.
In this article, “Is poor data quality undermining your marketing AI?”, the author, Louis Columbus, goes on to emphasize key issues, including:

  • “The most common reason AI and ML fail in the marketing sector is that there’s little consistency to the data across all campaigns and strategies. Every campaign, initiative, and program has its unique meta-tags, taxonomies, and data structures.”
  • “Creating greater consistency across taxonomies, data structures, data field definitions, and meta-tags would give marketing data scientists a higher probability of succeeding with their ML models at scale.”
  • “Instead of asking data scientists to solve the marketing department’s data quality challenges, it would be far better to have the marketing department focus on creating a single, unified content data model.”

We highly recommend you read the full article to understand the scope of what’s at stake not just for these changes to Adobe Experience Manager but general marketing data quality issues negatively impacting your organization’s ability to use AI/ML effectively (if at all).

The Impact of Data Quality on Adobe Experience Manager

As this relates to these innovations forthcoming in Adobe Experience Manager, if your organization is scaling content velocity via new automation, you risk losing control if you don’t trust the decisions your AI tagging is processing.
If your content producers aren’t aligned on naming conventions and standards upfront and proactively, a file may flow into your DAM that is named incorrectly, then re-used at scale with the wrong name intact – propagating hundreds (or more) of additional assets that then also get wrongly tagged by your AI/ML system.
At best, these mistakes lead to a huge clean-up project for someone. At worst, your business is delivering poor experiences to your customers. And we all know that a great digital experience is paramount today. In fact, “84% of customers say the experience a company provides is as important as its products or services” and “1 in 3 consumers will walk away from a brand they love after just one bad experience”. (Fullstory)

Delay in Content Velocity

Let’s assume Sensei works great. Your content velocity overall will still remain slow due to the many manual steps still required to build the content and define other asset attributes which are managed through workflows or content authors.
The automation provided in AEM is only effective after the content is created and data standards are applied throughout the workflow. Then you can maximize content velocity and time to value.

Transforming AEM Metadata Management

Knowing there’s great value in getting more content into market quickly, along with the ability to measure content performance with richer metadata, we built an integration with Adobe Experience Manager. Imagine being able to find missing or mis-labeled content attributes en masse and update the right information, pushing it right back into AEM? Now you can.
And not only are you cleaning up historic data, you have a system that ensures you are entering in the right data and all the data you need to correctly describe your assets and pages.
Further, if you want to add new and deeper details to your assets or pages, you can:

  • create those requirements right in our platform
  • fill out the respective values for each asset/page
  • map to a default or custom AEM field
  • Send those updated attributes back into AEM

The Data Standards CloudTM  addresses the lack of data standards and consistency being fed into DAM/CMSes and their respective AI/ML systems. It does this for other core systems in your marketing technology stack, too. Our technology lets teams and organizations manage their data standards and create data integrity globally, providing consistent and quality information to optimize marketing outcomes.

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