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.

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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.

• “The narrator’s voice sounds familiar, who is it?”
• “What is that actor’s name? I think I might have seen him in another movie.”
• “Isn’t she the actress from this other show I watched some years ago?”

A few years ago, these questions might have gone unanswered if neither you nor any of the people with you knew the answer, or you might have had to wait until the credits appeared. However, now, all it takes is a simple google search to find all of the answers to those questions. The information that you might find on the internet about the series could be the cast, number of seasons, number of episodes in the season, airing dates, episode summaries, episode length, and production details, among others. This relationship between the TV series and the information you found about it on the internet brings us to the concept of metadata.

Metadata

As you might notice from the example above, metadata is simply data about data. In this particular case, it would be the data on the internet about the videos you watched. The primary use of metadata is to provide context and information about data, as well as enhance findability and describe data, all of which are especially helpful when dealing with unstructured data.

• Structured data: These data follow a defined framework with a set number of fields. Think of a well-formatted spreadsheet where every column contains one specific type of data. An example of this would be a table with personal information, such as name, address, telephone number, and age of multiple people.  
• Unstructured data: This data is not able to be stored in a traditional column-row database or spreadsheet. Think of photos, videos, audio, text documents, and websites. Unstructured data is also the most common type of data, and because of its unstructured nature, its metadata is particularly useful to help us find it and make sense of it. How would you be able to find a movie without being able to search by its title, who stars in it, or what it is about?

Amazon Prime Video Meets IMDB

IMDB is a database designed to provide TV watchers and cinephiles information about millions of TV shows and films, including cast biographies and reviews. In 1998, Amazon bought IMDB to acquire its lucrative user base and give its Amazon Prime Video streaming service a marketing push. This strategic acquisition allowed Amazon to promote its video streaming service to an already targeted user base. 

Amazon Prime Video and IMDB kept growing in both content and active users. The streaming service not only got its marketing push, but also integrated its user-generated data (such as user behavior and preferences) with IMDB’s database to boost its recommendation systems across platforms. So, how could these two successful products be further integrated? Fast forward about a decade, and Amazon Prime Video added a new feature called X-Ray.

Remember those questions that many people have while watching a video? Well, X-Ray takes care of that. Now, when pausing your favorite show, X-Ray will display information about what you are currently viewing. This includes cast information, filmographies, facts, trivia, character backstories, photo galleries, bonus video content, and music. By leveraging metadata from IMDB, Amazon Prime Video can add structure to unstructured video content, enabling users to answer those nagging questions.

Building More Informed Recommendation Systems

Recently, due to the COVID-19 crisis, video streaming services have been experiencing a surge in demand. As people catch up on their favorite series, streaming service firms need to keep their customers engaged by recommending related material that would keep them active.

At Enterprise Knowledge, we had the opportunity to work with a prominent client in the telecommunications industry. We improved their recommendation systems, leveraging the power of metadata on its unstructured content. The enhanced recommendation system takes the viewers’ input based on a specific scene the viewer is currently watching. The engine would ingest information pulled from the closed captioning file and internal and external databases containing information about the tv series, episode, and scene. The resulting recommendation system would not only work on general information about the tv series such as genre, recurring cast, summaries, and network, but it would also take specific details about the scene, such as sentiment inferred from the subtitles, non-recurring cast appearances, and particular music in the scene, improving the recommendations provided to the viewer.

Beyond the media or telecommunications industries, metadata has an equally crucial role in making unstructured data usable and accessible. It allows enterprise applications to link unstructured content based on assigned attributes included in the metadata. As another example, in the pharmaceutical industry, a recommendation system would take research papers, formulations, and experiment reports and link them based on related chemical compounds, illnesses, or authors. These links in the data power up recommendation systems and enterprise search engines that provide content at the users’ point of need. The resulting enterprise applications are as powerful as the quality and completeness of the metadata used to derive the results.

The benefits of including metadata as an integral part of an organization’s strategy include:

• Content findability, reuse, and sharing: Metadata ensures that complex content is easily understood and processed by people other than the content creator. Hence, it allows anyone in the organization to find the content they need to do their jobs regardless of content type, knowledge of its existence, who owns it, or where it is located. This results in increased productivity and higher quality of work. 
• Data Governance: Metadata can also serve as an annotation tool that denotes content ownership and temporality since some data may be deemed irrelevant after a specific timeframe. This also makes it easier to identify who is responsible for the timeliness and the quality of the content. Furthermore, it can be used to trigger workflows that ensure the content is accurate and up to date, if necessary. As a result, organizations have greater control over their content and data, ensuring the right people are finding and acting on the right information.
• Innovation and Service: When employees spend less time asking coworkers for content, looking for information, recreating information, and waiting for answers, they have more time for innovation and customer support. This, in turn, results in greater employee and customer satisfaction, which leads to higher employee and customer retention.

Conclusion

In conclusion, metadata provides structure to unstructured content, making it machine-readable and ready to work with machine learning and artificial intelligence applications. In my specific example, Enterprise Knowledge enhanced the client’s unstructured video content using internal and external sourced data to provide a metadata rich environment. This environment gave the recommendation system access to new information on which to drive its decisions, culminating in better recommendations that keep TV watchers engaged. Similarly, we can help your organization connect your data, content, and people in ways to enhance your corporate knowledge, resulting in the benefits I discussed above.

Does your organization need assistance in leveraging metadata to enhance its unstructured content? Feel free to reach out to us for help!

The post Metadata Use Case: IMDB in Amazon Prime Video appeared first on Enterprise Knowledge.

What is Unstructured Content? 

Unstructured content (also called unstructured data, and used here interchangeably), is content that does not have any data model or infrastructure applied to it. This makes it difficult to be ingested and managed by information systems, rendering search applications less accurate than they could be. Unstructured content is typically textual in nature, but can also include names, dates, and other data.

Common Unstructured Content Dilemmas 

At EK we see two common dilemmas that users often encounter when dealing with unstructured content. 

The “Search Again” Dilemma

Imagine that you’re trying to find your organization’s process for submitting a travel request. After searching through a shared drive of company information, you find an HR PDF called “Employee Handbook.” You open the file and see that it is 40 pages long, so you use Ctrl + F (or Edit -> Find) to search for the phrase “travel request.” This takes you to the portion of the handbook that you needed. In this scenario, you had to search twice: once for the document, and again for the actual information you needed.  If you were using a system that was underpinned by deconstructed content, your initial search for “travel request” would have rendered what you needed, saving you time and effort. This is because the content pieces would each be tagged with metadata and indexed by a search application, as opposed to just the bigger, longer document. When this happens search is able to treat each content chunk as one search result, surfacing more specific answers. 

The “I Didn’t Know I Was Looking for That” Dilemma

Users often embark on their search for content with a certain document or type of content in mind. They may think “I need the manual for this procedure,” or “I need this specific form.” However, users don’t always have access to, or awareness of, the full breadth of content an organization has stored in its systems. For example, imagine that you’re a lawyer looking for an example of a Licensing Contract that you can use for the project you’re working on. You may enter your company’s intranet and search for “Licensing Contract,” which returns dozens of contracts that you can scroll through. Further down in the search results you see a document titled “Licensing Contract Template” and realize this is what you need, as opposed to a completed example. In this instance, you had to scroll through search results only to realize that what you were looking for was actually a template. When data is unstructured, search results become unstructured. Systems cannot derive meaning from volumes of text, so they cannot reflect search results back in a meaningful way. 

These scenarios should be familiar to almost anyone working in an organization with lots of unstructured data. Many users become accustomed to stumbling through virtual stacks of documents until they strike the right piece of information they need. However, this doesn’t need to be the status quo. 

Creating Structure

There are two different practices that, when combined, result in a robust, efficient system for managing and searching for unstructured content. Before I talk about them, though, I should mention a critical part of information architecture: taxonomy. A precursor to more complex content management efforts should be the design of a user centric business taxonomy that satisfactorily encompasses the range of information being stored in a system. Taxonomy terms will be the glue that holds together the solutions I talk about moving forward. Once a taxonomy is in place, content deconstruction and a knowledge graph can be used to create a sophisticated content management solution. 

Content deconstruction, which is explained in more depth in this blog, breaks longer documents into smaller chunks to apply more pointed metadata relevant to each section. This creates more relevant search results, consumable by both systems and users alike. In the context of the “Search Again” problem, a deconstructed approach would eliminate the need to dig through longer documents to get to the right piece of information. Applying a knowledge graph to content “chunks” results in an even more sophisticated solution in which these chunks can be related to each other.

Knowledge graphs create and manage meaningful relationships between content, breaking the constraints of keyword search and generating advanced discovery. Creating knowledge graphs is a complex endeavor, one which my colleagues at EK have written about extensively here. For this particular use case, knowledge graphs can relate structured content and data associated with content (like author, business area, and topic), so that relevant information can be quickly surfaced in search results. This drives users to discover content they may not have been aware of, preventing the second dilemma I discussed above and applying significantly more value to an organization’s content.  

Putting it All Together 

To give an example of how these two solutions can work together to create a seamless content consumption experience for users, take the example of a project I worked on for an international grocery store chain. This organization had an intranet that stored all employee handbooks and HR policies, amounting to long lists of links to download even longer pdfs on topics like Time Off, Dress Code, Pay, and Travel. If an employee wanted to find information about what uniform they are required to wear, they would first have to search the intranet using the term “uniform,” which would return, among other things, a 30 page pdf titled “Employee Dress Code.” Then they would have to download that pdf and take the extra step of scrolling or using Ctrl + F to find information specifically about uniforms. This should sound familiar, as it is an example of the “Search Again” dilemma. 

What we did in this scenario was take each of the long policy documents and “chunk” them, breaking each into segments that addressed one topic or subject. A taxonomy was designed so that each segment was tagged with topical and departmental information. For the “Employee Dress Code” document, there were segments like “Store Uniform,” “Office Uniform,” and “Warehouse Uniform,” each with specific rules and expectations around these policies. Now, when an employee searches for the keyword “uniform,” they will be able to quickly assess which segment they need based on its content and tags. 

To take this one step further, the use of a knowledge graph surfaces content related to the segment a user is viewing. For example, if the user searched for uniform and clicked on the “Store Uniform” segment, they might be shown the related content: “Uniform Order Form” and “Dress Code Violations.” In the course of finding this information, the user may realize that they need to place an order, and be able to efficiently do so because the order form link is readily available to them. This demonstrates a solution to the “I Didn’t Know I Was Looking for That” Dilemma.

Parting Thoughts

Deconstructing content and creating a knowledge graph for that content is no small feat, but it is a realistic and achievable approach to content management. At the end of the day, the goal is to build a system that stores and manages deconstructed “chunks” of tagged content that are related using a knowledge graph. If you would like guidance on where to begin, here is how Enterprise Knowledge experts can help. 

The post Structuring Unstructured Content: The Power of Knowledge Graphs and Content Deconstruction appeared first on Enterprise Knowledge.

This infographic is a visual introduction to how facets can improve item, document, and content findability, regardless of the form and structure of that content. Other factors, like customized action-oriented results and an enterprise-wide taxonomy, allow for an even more advanced search experience. EK has experience in designing and implementing solutions that optimize the way you use your knowledge, data, and information, and can produce actionable and personalized recommendations for you. If this is something you’d like to speak with the experts at EK about, reach out to info@enterprise-knowledge.com.

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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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