The session will be held online on Wednesday, September 17 from 9:00am – 10:00 AM PST. For more information and to register, visit here.

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A headless CMS, one in which the content authoring “body” is separated from the content distribution channels (or heads), allows an organization to:

1. Author once and publish to multiple channels; and
2. Reuse the same content in multiple contexts.

This relatively new type of content management system offers several points of business value over its traditional cousins. When is it worth it? What business problems can a headless CMS help you solve? Why should your organization adopt a headless CMS solution?

Create More Compelling Content With Audience Segmentation

We all want to create engaging content that grabs the attention of our audiences. But the reality is, that is nearly impossible unless you define your audiences and write to their specific needs. When you try to create generic content for everyone, you end up creating dull, flat content that isn’t exciting for anyone.

That seems like common sense, but when you break it down further there are some implied technical requirements:

• You probably have more than one audience.
• If you’re going to create content which meets the specific needs of each audience, you’re going to need similar versions of the same content.

Supporting similar versions of content is a tricky content governance issue (we’ll get into governance below), but with the right content engineering, you can maximize your ability to create custom content to meet audience needs, while minimizing the burden of maintaining similar versions of the same content.

Consider this example. If you’re creating content to teach staff members in your organization how to follow a process, you’ll likely document the process step by step. If, instead of creating one single Word document that outlines the entire process, you create structured content where each step of the process is a unique field, you are then able to more effectively reuse each step for multiple audience needs.

When you break down the document paradigm and craft reusable content components, a headless CMS can deliver them for multiple audience needs.

KEY POINT:  In all of these examples, the core content (the steps) is the same. It is the context surrounding the core content which must be customized for the audience. Rather than maintaining the core content in multiple places, a headless CMS with properly engineered content models allows for the reuse of the core content to meet the needs of audience segments. This is the foundation of effective content personalization and localization. It’s the building blocks of more engaging content.

Improve the Efficiency and Accuracy of Content Updates

As noted above, effective audience segmentation, content personalization, and content localization, all require contextualizing content for a unique audience and purpose. If you’re not careful – and your CMS is not well-designed – this could result in a lot of duplicate content. Generally speaking EK recommends avoiding duplicate or similar content because it is a nightmare to maintain and govern.

Let’s look again at the content reuse example from above. We’ve created step-by-step documentation of a process and we’ve reused that process documentation for three different audiences. If we’ve done this the “old fashioned” way, this means we’ve probably created three different documents or web pages. What happens in that scenario when there’s a change to the process? We’ll have to update the content in three different places (and, inevitably, we’ll lose track and forget to update at least one of the three).

So let’s pause a second and break down the business problems here:

• Time Wasted: Content creators maintaining content in three places instead wastes time. Multiply that over all of the content your organization maintains and this is a very expensive problem.
• Inaccurate Content: When your team remembers to update content in only two of the three places, the overall perception of accuracy and reliability of your content are eroded.

KEY POINT:  A well designed headless CMS enables the reuse of content so that your content creators only have to make updates in one place when content inevitably needs to change. This improves the efficiency of your team, and improves the overall accuracy of your content.

Summary

A headless CMS enables the efficient reuse and re-contextualization of content for multiple audiences, platforms, and purposes. Reusing content can help your organization realize a ton of benefits including better audience engagement with segmentation and personalization, saved staff time and money when content updates can be made in one place instead of many, and improved accuracy of (and subsequent trust in) content. Need help architecting and deploying a headless CMS? EK’s team of engineers can help.

The post Understanding the Business Value of Headless CMS appeared first on Enterprise Knowledge.

The Solution

Alongside Enterprise Knowledge (EK), the organization embarked on a phased approach to develop a scalable knowledge, data, and information management strategy. EK began by designing a global content and data strategy in parallel with an enterprise search redesign effort that featured an information architecture overhaul. A taxonomy and corresponding content types were designed to support auto-tagging and the automated organization of unstructured content, while also allowing for the transformation of the organization’s content into a machine-readable format.

The second half of the approach included identifying scaled integration points across the organization’s content, allowing for advanced inter-content relationships to be utilized by recommendation engines in the future. Ontologies and knowledge graphs were introduced as a means of automating the application of these relationships while also optimizing the use and reuse of the organization’s data and information. To further support the management and scalability of the strategy and design efforts over time, an organizational model and governance plan were developed to support change management, implementation, and adoption.

The EK Difference

Because this large, global organization was seeking to successfully complete an initiative that traversed multiple departments, the effort required alignment and support from department leads, staff, and executives. EK leveraged our proven facilitation and prioritization approaches tailored specifically to information and data management strategy and led strategic discussions with the company’s executives, global program leadership, and staff to align on the “as-is” and “to-be” states of the effort. We developed relevant business impact and ROI measures by identifying prioritized success and performance factors that were evaluated and adjusted consistently throughout the effort. 

EK further leveraged our expertise in ontology and enterprise knowledge graphs to design an information architecture that defined the relationships across disparate content and built the foundation for advanced capabilities, such as automated tagging, content governance, natural language search, data analytics, and future AI and Machine Learning (ML) capabilities.

The Results

The knowledge and information management program allowed the organization to better understand and capitalize on their market insights and, as a result, discover and utilize otherwise inaccessible data. Connections between knowledge assets are now defined and the information architecture and content strategy benefit from a taxonomy and metadata design that account for both structured and unstructured data. 

EK also revamped the company’s internal search experience by redesigning indexing processes and leading Design Thinking sessions to inform both UI and UX search design decisions, ultimately integrating action-oriented results across the intranet. Consequently, users found that returned results were more relevant to their queries and a user-friendly interface personalized for the organization’s staff facilitated system access and ease-of-use.

The KM organizational structure will ensure that stakeholders are enabled to make informed investment decisions about their data and content management systems and will better understand the relationships required to bring them all together. As AI capabilities become more advanced and accessible on a global scale, the organization will not only be operating ahead of the curve, but will be able to adapt and apply these capabilities on a regular basis.

A-Global-Knowledge-and-Information-Management-SolutionDownload

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With our focus and expertise in knowledge, data, and information management, Enterprise Knowledge (EK) developed this proprietary Enterprise Artificial Intelligence (AI) Readiness Assessment in order to enable organizations to understand where they are and where they need to be in order to begin leveraging today’s technologies and AI capabilities for knowledge and data management. 

Based on our experience conducting strategic assessments as well as designing and implementing Enterprise AI solutions, we have identified four key factors as the most common indicators and foundations for many organizations in order to evaluate their current capabilities and understand what it takes to invest in advanced capabilities. 

This assessment leverages over thirty measurements across these four Enterprise AI Maturity factors as categorized under the following aspects. 

1. Organizational Readiness

The foundational requirement for any organization to undergo an Enterprise AI transformation stems from alignment on vision and the business applications and justifications for launching successful initiatives. The Organizational Readiness Factor includes the assessment of appropriate organizational designs, leadership willingness, and mandates that are necessary for success. This factor evaluates topics including:

• The need for vision and strategy for AI and its clear application across the organization.
• If AI is a strategic priority with leadership support.
• If the scope of AI is clearly defined with measurable success criteria.
• If there is a sense of urgency to implement AI.

With a clear picture of what your organizational needs are, your Organizational Readiness assessment factor will allow you to determine if your organization meets the requirements to consider AI related initiatives while surfacing and preparing you for potential risks to better mitigate failure.

2. The State of Organizational Data and Content

The volume and dynamism of data and content (structured and/or unstructured) is growing exponentially, and organizations need to be able to securely manage and integrate that information. Enterprise AI requires quality of, and access to, this information. This assessment factor focuses on the extent to which existing structured and unstructured data is in a machine consumable format and the level to which it supports business operations within the enterprise. This factor consider topics including:

• The extent to which the organization’s information ecosystems allow for quick access to data from multiple sources.
• The scope of organizational content that is structured and in a machine-readable format.
• The state of standardized organization of content/data such as business taxonomy and metadata schemes and if it is accurately applied to content.
• The existence of metadata for unstructured content. 
• Access considerations including compliance or technical barriers.

AI needs to learn the human way of thinking and how an organization operates in order to provide the right solutions. Understanding the full state of your current data and content will enable you to focus on the right content/data with the highest business impact and help you develop a strategy to get your data in an organized and accessible format. Without high quality, well organized and tagged data, AI applications will not deliver high-value results for your organization.

3. Skills Sets and Technical Capabilities

With the increased focus on AI, the demand for individuals who have the technical skills to engineer advanced machine learning and intelligent solutions, as well as business knowledge experts who can transform data to a paradigm that aligns with how users and customers communicate knowledge, have both increased. Further, over the years, cloud computing capabilities, web standards, open source training models, and linked open data for a number of industries have emerged to help organizations craft customized Enterprise AI solutions for their business. This means an organization that is looking to start leveraging AI for their business no longer has to start from scratch. This assessment factor evaluates the organization’s existing capabilities to design, management, operate, and maintain an Enterprise AI Solution. Some of the factors we consider include:

• The state of existing enterprise ontology solutions and enterprise knowledge graph capabilities that optimize information aggregation and governance. 
• The existence of auto-classification and automation tools within the organization.
• Whether roles and skill sets for advanced data modeling or knowledge engineering are present within the organization.
• The availability and capacity to commit business and technical SMEs for AI efforts.

Understanding the current gaps and weaknesses in existing capabilities and defining your targets are crucial elements to developing a practical AI Roadmap. This factor also plays a foundational role in giving your organization the key considerations to ensure AI efforts kick off on the right track, such as leveraging web standards that enable interoperability, and starting with available existing/open-source semantic models and ecosystems to avoid short-term delays while establishing long-term governance and strategy. 

4. Change Threshold 

The success of Enterprise AI relies heavily on the adoption of new technologies and ways of doing business. Organizations who fail to succeed with AI often struggle to understand the full scope of the change that AI will bring to their business and organizational norms. This usually manifests itself in the form of fear (either of change in job roles or creating wrong or unethical AI results that expose the organization to higher risks). Most organizations also struggle with the understanding that AI requires a few iterations to get it “right”. As such, this assessment factor focuses on the organization’s appetite, willingness, and threshold to understand and tackle the cultural, technical, and business challenges in order to achieve the full benefits of AI. This factor evaluates topics including:

• Business and IT interest and desire for AI.
• Existence of resource planning for the individuals whose roles will be impacted. 
• Education and clear communication to facilitate adoption. 

The success of any technical solution is highly dependent on the human and culture factor in an organization and each organization has a threshold for dealing with change. Understanding and planning for this factor will enable your organization to integrate change management that addresses the negative implications, avoids unnecessary resistance or weak AI results, and provides the proper navigation through issues that arise.

How it Works

This Enterprise AI readiness assessment and benchmarking leverages the four factors that have over 30 different points upon which each organization can be evaluated and scored. We apply this proprietary maturity model to help assess your Enterprise AI readiness and clearly define success criteria for your target AI initiatives. Our steps include: 

• Knowledge Gathering and Current State Assessment: We leverage a hybrid model that includes interviews and focus groups, supported by content/data and technology analysis to understand where you are and where you need to be.This gives us a complete understanding of your current strengths and weaknesses across the four factors, allowing us to provide the right recommendations and guidance to drive success, business value, and long-term adoption.
• Strategy Development and Roadmapping: Building on the established focus on the assessment factors, we work with you to develop a strategy and roadmap that outlines the necessary work streams and activities needed to achieve your AI goals. It combines our understanding of your organization with proven best practices and methodologies into an iterative work plan that ensures you can achieve the target state while quickly and consistently showing interim business value.
• Business Case Development and Alignment Support: we further compile our assessment of potential project ROI based on increased revenues, cost avoidance, risk and compliance management. We then balance those against the perceived business needs and wants by determining the areas that would have the biggest business impact with lowest costs. We further focus our discussions and explorations on these areas with the greatest need and higher interest.

Keys to Our Assessment  

Over the past several years, we have worked with diverse organizations to enable them to strategize, design, pilot, and implement scaled Enterprise AI solutions. What makes our priority assessment unique is that it is developed based on years of real-world experience supporting organizations in their knowledge and data management. As such, our assessment offers the following key differentiators and values for the enterprise: 

• Recognition of Unique Organizational Factors: This assessment recognizes that no Enterprise AI initiative is exactly the same. It is designed in such a way that it recognizes the unique aspects of every organization, including priorities and challenges to then help develop a tailored strategy to address those unique needs.
• Emphasis on Business Outcomes: Successful AI efforts result in tangible business applications and outcomes. Every assessment factor is tied to specific business outcomes with corresponding steps on how the organization can use it to better achieve practical business impact.
• A Tangible Communication and Education Tool: Because this assessment provides measurable scores and over 30 tangible criteria for assessment and success factors, it serves as an effective tool to allow your organization to communicate up to leadership and quickly garner leadership buy-in, helping organizations understand the cost and the tangible value for AI efforts. 

Results

As a result of this effort, you will have a complete view of your AI readiness, gaps and required ecosystem and an accompanying understanding of the potential business value that could be realized once the target state is achieved. Taken as a whole, the assessment allows an organization to:

• Understand strengths and weaknesses, and overall readiness to move forward with Enterprise AI compared to other organizations and the industry as a whole;
• Judge where foundational gaps may exist in the organization in order to improve Enterprise AI readiness and likelihood of success; and
• Identify and prioritize next steps in order to make immediate progress based on the organization’s current state and defined goals for AI and Machine Learning.

The post Enterprise AI Readiness Assessment appeared first on Enterprise Knowledge.

If you are exploring ways your own enterprise can benefit from implementing AI capabilities, we can help! EK has deep experience in designing and implementing solutions that optimizes the way you use your knowledge, data, and information, and can produce actionable and personalized recommendations for you. Please feel free to contact us for more information.

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