The LavaCon Conference on Content Strategy and Technical Communication Management took place October 27-30 in Portland, Oregon. The theme of this year’s event was Content as a Business Asset: Reducing Costs, Generating Revenue, and Improving the Customer Experience Through Better Content.

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The Challenge

In support of the KM strategy, the organization had identified several core needs that offered opportunities for better alignment with the full network and a more streamlined process for content and system management. They sought to more clearly communicate the value and purpose of two of their major platforms to the broader network: a content management system/intranet and a learning management system (LMS). The organization’s network members did not always understand the value of these systems, the purpose of each, and where they should go to find certain types of information. In addition, the organization wanted to update their platforms’ terminology and structure to be more intuitive to employees in the larger network, as they often use different nomenclature than the organization’s staff members.

To do so, the organization was now seeking a comprehensive information architecture (IA) strategy to: 

• Define purpose statements and strategy for the intranet and LMS, providing clarification for end users on the specific uses for each platform; 

• Develop an IA site map for the intranet to support discoverability, usability, and design of content within the site, integrating functional areas with related services; and

• Create a scalable IA plan that outlines the steps needed to implement and configure the defined intranet site map, ensuring it can effectively accommodate future growth and evolving user needs.

The Solution

EK worked with the organization to develop an IA strategy that provided clarification for users on where to find different information between the intranet and their LMS platform through System Purpose Statements and issued High-Level Information Architecture recommendations for the findability of, accessibility to, and navigation through information in the intranet. EK also worked with the organization to develop a taxonomy and metadata to support the proposed site map, clickable Site Prototypes to visualize what changes would look like, and an IA Plan for Scale to help the organization understand the next steps on a long-term implementation roadmap.

EK leveraged a top-down and bottom-up discovery approach during the first 4-6 weeks of the project to ensure that all recommendations were rooted in user-centered design. Before beginning work, EK conducted a persona refinement session to better understand the organization’s user groups, detailed walkthroughs of the systems and prioritized sites, and three focus groups with representatives from across the network. Once the initial recommendations were delivered, EK facilitated four validation sessions with end users and content owners to collect feedback and make updates accordingly.

The EK Difference

Throughout the project, EK emphasized user-focused research, engaging in constant review and iteration. EK incorporated changes based on discovery work and asynchronous feedback received via multiple facilitation tools such as visual boards. This iterative process ensured that the solutions developed were aligned with the organization’s user needs and expectations. By leveraging insights from the former KM Strategy engagement that EK conducted with the organization, the EK team crafted detailed system purpose statements and developed a scalable plan, ensuring a robust framework for future growth.

EK facilitated a crucial conversation with the organization’s IT and intranet stakeholders to ensure alignment and smooth implementation. To meet the organization’s needs, EK conducted comprehensive technology research focusing on its intranet functionality and its integration with existing systems. EK provided detailed recommendations for future technology considerations, ensuring the organization could make informed decisions as their needs evolved.

EK’s work with the core team was tailored to the organization’s current capabilities, providing a comprehensive how-to guide for validating prototypes through click testing, along with sharing sample questions for conducting these validation sessions. This guidance was shared to ensure the organization could continue to successfully validate prototypes and further work on this project independently.

The Results

As a result of this engagement, the organization received an IA site map for its intranet, which will significantly enhance discoverability, usability, and content design. Common nomenclature was suggested to improve findability for all users, accompanied by guidance on updating, managing, and maintaining IA governance to ensure long-term consistency.

Validated Purpose Statements for the LMS and Intranet were defined, clarifying the type of content that was appropriate for each system. The purpose statements were short vision statements for each system that differentiated it from the others and co-created with the organization’s team. These will help minimize user confusion about where to contribute and find information. Specific strategies were also provided to communicate the purpose of each system to users, fostering a common understanding. 

To support ease of navigation and findability, clickable prototypes were designed, showcasing the necessary framework and metadata aligned with a new IA. These interactive prototypes helped the organization visualize the suggested changes and encourage buy-in for the redesign of the intranet. By demonstrating clear pathways and organized content, the prototypes illustrate how the new IA will enhance the user’s experience. This tangible representation allows stakeholders to see the practical benefits and improvements firsthand, facilitating a smoother transition and greater support for the redesign initiative. 

Finally, a scalable plan was developed for implementing additional site design changes following the delivered site map. This roadmap included actionable steps across short-term, mid-term, and long-term timelines, addressing technical and content recommendations to improve efficiency and user experience. The plan ensures that the IA recommendations are effectively integrated, setting a solid foundation for ongoing improvement and scalability within the intranet and LMS.

 

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The semantic layer’s feature of connectivity of content and data and content sources requires consistent naming/labeling of concepts and entities and of relationships between concepts and entities. This consistent naming and alignment of resources is what a taxonomy and other controlled vocabularies provide.

Both taxonomy and information architecture also help provide context for other components of a semantic layer. This article describes the relationship between IA and taxonomy and their importance and varied roles in the semantic layer.

The Semantic Layer

A semantic layer is a standardized framework that organizes and abstracts organizational data (structured, unstructured, semi-structured) and serves as a connector for data and knowledge. It supports improved data management, content management, information management, and ultimately knowledge management enterprise-wide. It’s a method to bridge content and data silos through a structured and consistent approach to connecting instead of consolidating data. It connects all organizational knowledge assets, including content items, files, videos, media, etc. via a well defined and standardized semantic framework. The semantic layer comprises a combination of solutions, knowledge organization systems, and software systems, rather than any single one.

A semantic layer framework provides various benefits, including consistent metadata, support for open Semantic Web (W3C) standards, intuitive user interactions, high quality data and governance, efficient data analysis processes, and a semantic context for reliable AI responses. More specifically, a semantic layer allows users to:

• Connect sources of structured and unstructured information to search for data and content at the same time;
• Conduct a single search for information from multiple disparate solutions and locations
• Access and analyze data without extensive technical knowledge;
• Access relevant insights faster, abstracting the complexity of underlying data
• Understand the business meaning of data;
• Scale analytics capabilities to adapt to changing business needs and data sources; and
• Incorporate new data sources easily, especially for faster development and deployment of AI models and technologies.

It is called a “layer” because in the larger framework, it’s a middle layer between the content/data repositories and one or more front-end applications for users to search, browse, analyze, or receive recommendations of information. In a sense, it cuts across systems and repositories horizontally. It’s called “semantic” because it provides standardized meaning and labels to entities and business objects and their relationships, often based on W3C standards. 

Taxonomies and Information Architecture Defined

Taxonomies and information architecture overlap and are often somewhat intertwined, even though they may be discussed separately due to different perspectives.

A taxonomy is a knowledge organization system that supports information retrieval though its controlled and structured set of terms. More specifically, a taxonomy is a controlled vocabulary, based on unambiguous concepts, not just words, which are structured into a hierarchy or hierarchies of broader-narrower concepts, and are used primarily to tag content to support its findability and retrieval through searching or browsing by users.

The definition of information architecture (IA) in a broad sense is the structural design of shared information environments, which includes organization, labeling, search, and navigation systems. Specifically, this includes the organizing and labeling of web sites, intranets, online communities, software user interfaces, and information products (Rosenfeld, Morville, and Arango, Information Architecture, 4th ed., O’Reilly, 2015, p. 24). Thus, when IA is identified as a need or asset, taxonomy development is often a part of it.

When we look at how taxonomy and IA are connected, we can then see more broadly how other knowledge organization systems can also be connected with them. Navigation menu labels can match search refinements and top-level content categories. Navigation structures may align with other hierarchical taxonomies. The taxonomy tagging concepts may integrate with various metadata schema to provide the controlled values for different metadata properties. Glossaries may align to taxonomies, by bringing in full definitions for concepts. Ontologies can connect various taxonomies and metadata properties together with semantic relationships, and they also connect to data extracted from databases. Data field types may be converted to ontological relationships and attribute types. Knowledge graphs combine taxonomies, ontologies, and instance data. 

Information Architecture as a Part of a Semantic Layer

Although IA is not a system or resource, as a taxonomy or ontology is, and thus not a “component” of a semantic layer in the same way, it is an important design element of a semantic layer, just as IA is also a core element of knowledge management.

IA organizes how taxonomies and ontologies are applied to the user experience / presentation layer. A taxonomy has both a front end, accessed by its users, and a back end, where it is tagged to content and linked to data. While all concepts in a taxonomy are tagged or linked, not all concepts (or all labels of concepts with multiple labels) are directly displayed to the users. Selected high-level taxonomy concepts may be displayed in hierarchies, frequently tagged concepts may be displayed in search refinement filters, concepts that match users’ search strings may be displayed as search suggestions listed under the search box, or a user’s search may directly serve up content without indicating what the matching concepts were. How best to display the taxonomy or features of it to the users and how to support user interaction with the taxonomy are decisions of IA best practices. IA also supports the use of ontologies in the user interface of applications, such as by determining the best way to display related data or suggesting related content.

IA, however, is not limited to the front-end presentation layer. In fact, IA transcends all layers of information and data management. Thus, IA comprises multiple layers itself, as the following diagram illustrates. 

IA comes into play at different levels within solutions pursued by an organization. IA provides structural context by illustrating the overarching model/design of the semantic layer. It links content and data through shared and linked metadata and knowledge organization systems, which in turn are implemented in one or more front-end applications. 

Taxonomy as a Part of a Semantic Layer

Key components of a semantic layer are knowledge organization systems, which include business glossaries, controlled metadata, a data catalog, taxonomies, thesauri, ontologies, and knowledge graphs. A semantic layer does not need to include all of these, but it should include a combination to define both terms and semantic relationships to a sufficient extent needed. Some semantic layer components, such as glossaries, focus on defining terms, while others, such as ontologies, focus on modeling classes, semantic relationships, and attributes. Taxonomies, which are very flexible and scalable in their design, include at least some of both features with concepts that may have notes and definitions and “semi-semantic” relationships that may be hierarchical or non-hierarchical (associative).

Taxonomies provide consistent naming of and alignment of concepts. For example, a standard naming and hierarchy of industries is needed for the industries of customers in the account management system, industries associated with customer and lead companies and individuals in the contact management system (CRM), and industries in the knowledge base content management system (CMS) that includes employee areas of expertise. An industry taxonomy and a product taxonomy provide consistency, alignment, and the ability to search/query across multiple systems and content/data repositories. In addition to contextualizing data and content through consistent naming, a taxonomy also provides subject domain context for concepts. Finally, a taxonomy is also a critical building block for preparing data for AI use cases, by providing disambiguation and context.

Since taxonomies are one of several components of a semantic layer, a dedicated taxonomy (or taxonomy/ontology) management system is one of several system components of a semantic layer. It’s essential that the taxonomy be managed and governed outside any individual system (CMS, DAM, CRM, etc.) that has a taxonomy management feature within it, and that the taxonomy be based on open W3C standards, namely SKOS (Simple Knowledge Organization System).

Learn more about EK’s taxonomy and ontology consulting services and how a taxonomy strategy can align with a larger semantic layer strategy. Contact us for more information and how we can be of service to you.

The post Taxonomy and Information Architecture for the Semantic Layer appeared first on Enterprise Knowledge.

In their presentation, O’Brien-Scott and Cakici focused on what Enterprise AI is, why it is important, and what it takes to empower organizations to get started on a search-based AI journey and stay on track. The presentation explored the complexities of enterprise search challenges and how IA principles can be leveraged to provide AI solutions through the use of a semantic layer. O’Brien-Scott and Cakici showcased a case study where a taxonomy, an ontology, and a knowledge graph were used to structure content at a healthcare workforce solutions organization, providing personalized content recommendations and increasing content findability.

In this session, participants gained insights about the following:

• Most common types of AI categories and use cases;
• Recommended steps to design and implement taxonomies and ontologies, ensuring they evolve effectively and support the organization’s search objectives;
• Taxonomy and ontology design considerations and best practices;
• Real-world AI applications that illustrated the value of taxonomies, ontologies, and knowledge graphs; and
• Tools, roles, and skills to design and implement AI-powered search solutions.

The post IA Fast-Track to Search-Focused AI Solutions: Information Architecture Conference 2024 appeared first on Enterprise Knowledge.

With the explosive popularity of ChatGPT, organizations are throwing massive budgets and executive attention at the implementation of AI technologies. Making these solutions work for the enterprise can deliver competitive advantage and open up new solutions and business opportunities that were never before possible. However, without the right Information Architecture (IA) foundations, these projects are bound to fail. In this presentation, Marino and Galdamez provided practical, actionable steps around IA that organizations can take in preparation for future AI solutions. 

In this session, attendees:

• Reviewed key elements of IA and discovered how their successful design and implementation can lay the foundations for AI;
• Learned basic terminology surrounding AI, as well as different techniques and applications of AI in enterprise environments;
• Gained a deeper understanding of the feedback loops between IA and AI and the corresponding implications on user experience; and
• Received practical advice on IA design to facilitate its implementation and the success of AI efforts.

The post Designing IA for AI: Information Architecture Conference 2024 appeared first on Enterprise Knowledge.

Cakici and O’Brien-Scott will deliver a presentation titled “IA Fast Track to Search-Focused AI Solutions,” which will empower organizations to navigate the complexities of AI integration with solid IA foundations. Marino and Galdamez will deliver a presentation, “Designing IA for AI – From Design to Implementation: Practical Tips to Ensure the Success of Your IA,” which will provide attendees with actionable steps to future-proof their content and data for AI applications by leveraging their existing IA.

IAC24, the premier international forum for professionals in IA, UX design, and content strategy, celebrates its 25th anniversary this year. The conference, held in Seattle from April 11-13, promises to be an enlightening gathering for industry leaders and innovators.

Follow the link for more information and to register. 

The post Four EK Experts Speaking at IAC24 appeared first on Enterprise Knowledge.

“I am most excited to hear from our speakers on how human-centered design techniques can be applied at the cross-section of Knowledge and Information Management to harness the power of context.” – Taylor Paschal

All are welcome to attend a portion or all of the event from 10:00 AM-6:00 PM ET for free, virtually, by registering at Richmond World IA Day. In addition to the Conference Program below, further details may be found on LinkedIn.  

The post Taylor Paschal to Facilitate World IA Day 2024 Richmond appeared first on Enterprise Knowledge.

At EK, we strive to find a balance between the top-down and bottom-up perspectives during the scoping process. Our approach seeks to anticipate the most common needs of users while leaving the door open to meet the dynamic situations that emerge from the content or systems when users interact with them. There have been cases in which the information needed to spec out a solution is not available in business databases, policies, or content, and stakeholders don’t have any insights into user journeys due to the lack of concrete information emerging from the regular conduction of the business. 

When the organization doesn’t have representative business content to leverage, a resource that might provide just the right information to launch your scoping journey is architecture diagrams. In a previous blog we enticed the idea that Entity Relationship Diagrams (ERD) can be used as blueprints to navigate and leverage the data stored in relational databases. In this entry, we will dive a bit deeper into the information represented in diagrams, specifically in UML diagrams, and discuss some of the advantages and pitfalls of utilizing them in the early stages of your solution design. We will also share examples of the types of UML diagrams that we’ve found most helpful during the solution modeling process.

What are UML diagrams and how can they supplement your solution scope?

UML, shorthand for Unified Modeling Language, is a specification that allows IT professionals to represent the design and functionality of software systems through standard graphical elements: UML diagrams. There are fourteen UML diagram types grouped in two main categories: structural and behavioral. Structural diagrams show a system’s structure, behavioral diagrams show how a system should function.

Given their capability to convey information in a syntactic and conventional format, these pictorial representations are some of the most popular tools in every software engineer’s toolkit. For the business owners and information managers, UML diagrams allow teams to visualize how a process is working, the sequence of tasks, how the data flows from one platform to another, and the systems that produce it. Below you will find a summary of the notation for a class diagram.

As you embark on your scoping journey, consider stress testing your solution’s requirements, functionalities, and assumptions against the organizational ecosystem illustrated in the UML diagrams. You can compare your design to the design of systems elsewhere in the organization, identifying points of alignment as well as the unique features of your own solution. This iterative exercise will let you construct a more accurate and complete picture of the agents, processes, scenarios, behaviors and data that you scoped.

A piece of advice before you move further down the page. It is important to warn you against the temptation to simply take any existing UML model and replicate it in your scope. Instead, you should analyze UML diagrams just as you would any other piece of content that you receive: assessing their shortcomings in their current state as well as their capabilities to support your envisioned “end state” solution.

Where do I start?

Now that we know that there are business artifacts that can function as charts to navigate the organizational information ecosystem, we can go back to the original question of where to start. In order to capitalize on the information contained in a UML diagram, first and foremost, you need to set a baseline that represents what you expect from your solution, whatever this might be. A simple way of visualizing your thoughts is to draw a diagram. You can use pen and paper, a whiteboard, or a diagramming software. For your drawing, think of the “things” that you want your model to represent and how they relate to each other.

In the initial phase, your drawing doesn’t have to be complete or accurate. What is important is that you capture the information that reflects your current view and understanding of the environment you will be operating in and the outcomes you would like to see. You should expect challenges, questions, and expansions to your original drawing. Changes are part of the development cycle, particularly during the initial stages.

While you can start your definition process at any level of abstraction, try to think in “concrete” objects. As you grow in your modeling competency and knowledge of the environment, you will be able to incorporate more abstract concepts in your model. Some relatable examples might be customers, orders, and products. These will be your concepts and can be represented as labeled circles.

To represent relationships between concepts, you can simply draw a line from one circle to another and add a word or a short phrase that describes the connection.

Congratulations! You’ve successfully completed the first step in your scoping journey.

Talk to your stakeholders

At this point, you may want to show your diagram to stakeholders. The goal is to gather sufficient information that allows you to develop a functional definition for each one of the concepts and relationships that you originally came up with. This is particularly important if you are thinking about using your solution to support interoperable applications. 

In our example above, the definition of what a product is may differ by department, team, or source of revenue. For instance, does product encompass services? Who is a customer? Can both people and organizations be customers? What information do we collect about them? Is a customer the same as a client, a patron, or a shopper (all terms currently in use by different teams)?

It doesn’t matter if your solution is centered around taxonomies, ontologies, or knowledge graphs. They all are about meaning. Explicit meaning allows people and machines to collect, process, and exchange information more efficiently, and reduces the risk of misinterpretation and downtime due to data incompatibility. It is worth the time and effort you spend clarifying and documenting the variations and nuances of each piece of content that you analyze.

Ask the DBAs for their UML diagrams

It is now time to use your knowledge of UML diagrams. In addition to your business owners, database administrators (DBAs) are a group you will want to reach out to during the early phases of your scope definition. Since their function is to design databases and structures for new and existing applications, DBAs usually have a grounded perspective of the overall application ecosystem and infrastructure of an organization. You should definitely seek their expertise to inform your solution design.

You need to be strategic and determine the type of diagram that could provide the information that you need to consolidate your initial design. When you meet with the DBAs, ask them to walk you through their UML diagrams. Do they validate the concepts or relationships  that came out of your initial “pen and paper” drawing?

If you go back to the types of diagrams listed at the beginning of this post, you will notice that there is a diagram that allows software engineers to represent classes and relationships: the Class diagram. The following image is representing the same concepts from our example above: customer, order, and product.

By studying these drawings, you can start filling gaps in your knowledge, refining or reassuring your initial assumptions and understanding. Once again, you must make the extra effort not to replicate what currently exists. The goal with this analysis is to identify reusable elements but, more importantly, to make note of any missing pieces that are critical to the success of your solution. In our original drawing for instance, we had considered concepts for customer, product, and order. By inspecting the UML diagram we realize that it might be worth considering adding the concept of payment to our ontology or breaking down the product concept into more specific subconcepts. We can also reach the conclusion that our solution does not require distinguishing between domestic and international customers.

Another common situation is when a solution demands that you specify the causes for an event. In those cases, an Activity diagram might be a more appropriate artifact to analyze since they denote the actions, decision points, flows, start and end points of a given process.

In the diagram below, we are showcasing the standard notation for Activity diagrams and specifying what happens when a customer places an order. Once again, compare against your “pen and paper” drawing. Do you need a concept for the shipping company or the agent that processes, fills, and closes the order?

As a final word, we would like to reiterate that it is extremely easy to get caught on the details of what currently exists. Try to steer clear from discussing particulars such as the names of tables, columns, and data types, or to try to pin down the specifics of any existing platforms and deployments. At the onset, you should strive to define your solution in system-agnostic terms and detach it from specific implementations. Your ultimate goal when consulting UML diagrams is to help you determine what’s in and out of scope as well as the essential components of your desired solution. This is how you start assessing information gaps, prioritizing systems, clarifying processes, and identifying key partners and stakeholders to collect additional input. Not a bad place to start!

Conclusion

Many of our projects have benefited from incorporating UML diagrams as part of the initial information gathering activities. This is no surprise. UML diagrams are concise sources of information that can augment, validate, prove or disprove the assumptions, preconditions, and requirements established in preliminary scopes or solution designs.

For more information on case studies where we’ve leveraged these techniques to scope solutions or to get help doing so for your organization, contact us at info@enterprise-knowledge.com.

The post Jumpstarting Your Semantic Solution Design with UML Diagrams appeared first on Enterprise Knowledge.

Every organization has content/information that needs to be treated as confidential. In some cases, it’s easy to know where this content is stored and to make sure that it is secure. In many other cases, this sensitive or confidential content is created and stored on shared drives or in insecure locations that employees could stumble upon or hackers could take advantage of. Especially in larger organizations that have been in operation for decades, sensitive content and data that has been left and forgotten in unsecured locations is a common, high-risk problem. We call hidden and risky content ‘Phantom Data’ to express that it is often unknown or unseen and also has the strong potential to hurt your organization’s operations. Most organizations have a Phantom Data problem and very few know how to solve it. We have helped a number of organizations address this problem and I am going to share our approach so that others can be protected from the exposure of confidential information that could lead to fines, a loss of reputation, and/or potential lawsuits.

We’ve consolidated our recommended approach to this problem into four steps. This approach offers better ways to defend against hackers, unwanted information loss, and unintended information disclosures.

1. Identify a way to manage the unmanaged content.
2. Implement software to identify Personally Identifiable Information (PII) and Personal Health Information (PHI).
3. Implement an automated tagging solution to further identify secure information.
4. Design ongoing content governance to ensure continued compliance.

Manage Unmanaged Content

Shared drives and other unmanaged data sources are the most common cause of the Phantom Data problem. If possible, organizations should have well-defined content management systems (document management, digital asset management, and web content management solutions) to store their information. These systems should be configured with a security model that is auditable and aligns with the company’s security policies.

Typically we work with our clients to define a security model and an information architecture for their CMS tools, and then migrate content to the properly secured infrastructure. The security model needs to align with the identity and access management tools already in place. The information architecture should be defined in a way that makes information findable for staff across business departments/units, but also makes it very clear as to where secure content should be stored. Done properly, the CMS will be easy to use and your knowledge workers will find it easier to place secure content in the right place.

In some cases, our clients need to store content in multiple locations and are unable to consolidate it onto a single platform. In these cases, we recommend a federated content management approach using a metadata store or content hub. This is a solution we have built for many of our clients. The hub stores the metadata and security information about each piece of content and points to the content in its central location. The image below shows how this works.

Once the hub is in place, the business can now see which content needs security and ensure that the security of the source systems matches the required security identified in the hub.

Implement PII and PHI Software

There are a number of security software solutions that are designed to scan content to identify PII and PHI information. These tools look at content to identify the following information:

• Credit card and bank account information
• Passport or driver’s license information
• Names, DOBs, phone numbers
• Email addresses
• Medical conditions
• Disabilities
• Relative information

These are powerful tools that are worth implementing as part of this solution set. They are focused on one important part of the Phantom Data issue, and can deliver a solution with out-of-the-box software. In addition, many of these tools already have pre-established connectors to common CMS tools.

Once integrated, these tools provide a powerful alert function to the existence of PII and PHI information that should be stored in more secure locations.

Implement an Automated Tagging Solution

Many organizations assume that a PII and PHI scanning tool will completely resolve the problem of finding and managing Phantom Data. Unfortunately, PII and PHI are only part of the problem. There is a lot of content that needs to be secured or controlled that does not have personal or health information in it. As an example, at EK we have content from clients that describes internal processes, which should not be shared. There is no personal information in it, but it still needs to be stored in a secure environment to protect our clients’ confidentiality. Our clients may also have customer or product information that needs to be secured. Taxonomies and auto-tagging solutions can help identify these files. 

We work with our clients to develop taxonomies (controlled vocabularies) that can be used to identify content that needs to be secured. For example, we can create a taxonomy of client names to spot content about a specific client. We can also create a topical taxonomy that identifies the type of information in the document. Together, these two fields can help an administrator see content whose topic and text suggest that it should be secured.

The steps to implement this tagging are as follows:

1. Identify and procure a taxonomy management tool that supports auto-tagging.
2. Develop one or more taxonomies that can be used to identify content that should be secured.
3. Implement and tune auto-tagging (through the taxonomy management tool) to tag content.
4. Review the tagging combinations that most likely suggest a need for security, and develop rules to notify administrators when these situations arise.
5. Implement notifications to content/security administrators based on the content tags.

Once the tagging solution is in place, your organization will have two complementary methods to automatically identify content and information that should be secured according to your data security policy.

Design and Implement Content Governance

The steps described above provide a great way to get started solving your Phantom Data problem. Each of these tools is designed to provide automated methods to alert users about this problem going forward. The solution will stagnate if a governance plan is not put in place to ensure that content is properly managed and the solution adapts over time.

We typically help our clients develop a governance plan and framework that:

• Identifies the roles and responsibilities of people managing content;
• Provides auditable reports and metrics for monitoring compliance with security requirements; and
• Provides processes for regularly testing, reviewing, and enhancing the tagging and alerting logic so that security is maintained even as content adapts.

The governance plan gives our clients step-by-step instructions, showing how to ensure ongoing compliance with data protection policies to continually enhance the process over time.

Beyond simply creating a governance plan, the key to success is to implement it in a way that is easy to follow and difficult to ignore. For instance, content governance roles and processes should be implemented as security privileges and workflows directly within your systems.

In Summary

If you work in a large organization with any sort of decentralized management of confidential information, you likely have a Phantom Data problem. Exposure of Phantom Data can cost organizations millions of dollars, not to mention the loss of reputation that organizations can suffer if the information security failure becomes public.

If you are worried about your Phantom Data risks and are looking for an answer, please do not hesitate to reach out to us.

The post The Phantom Data Problem: Finding and Managing Secure Content appeared first on Enterprise Knowledge.

Before going “all in,” we often advise our clients to first understand and quickly validate the value proposition for adopting advanced Artificial Intelligence (AI) and Machine learning (ML) solutions within their organization by engaging in a beyond AI project prototype or pilot. Conducting such targeted experimentations not only provides the enterprise with a safe way to validate that AI and ML solutions will solve real problems, but also provides a design foundation for key AI elements required for their roadmap and supports long-term change management by showing immediate incremental benefits and developing interest.

Without the appropriate guidance and strategy, AI efforts may still get stalled right after a prototype or proof of concept, regardless of how successful these initial efforts may have been. 

Although 84% of executives see the value and agree that they need to integrate and scale AI within their business processes, only 16% of them say that they have actually moved beyond the experimentation phase.

Mainly informed by the diverse set of organizational realities and AI projects we have delivered, below I will explore the common themes I see when it comes to potential roadblocks in moving from prototype to enterprise, and provide a selection of approaches that I have found helpful in scaling enterprise AI efforts.  

1. Understand that AI projects have unique life cycles

In software delivery, Agile and DevOps continue to serve as successful frameworks for allowing iterative delivery, getting the product closer to the end user or customer and ultimately delivering immediate value. However, Enterprise AI efforts have surfaced the need to revisit Agile delivery within the context of AI and ML processes. What this means for the sponsoring organization and the teams involved is that any project management or delivery approach that is employed will need to balance the predictable nature of software programming with facilitation and ongoing education about expected machine outcomes for the end-user and subject matter expert (SME), while balancing the random number of experimental data ingestion and model refinement required for AI deliverables.

Enterprise AI projects typically have a number of workstreams or task areas that need to be at play, in parallel. These include use case definition, information architecture, data mapping and modeling, integration and pipeline development, the data science side of things where there are multiple Machine Learning (ML) processes running, and, of course, the software engineering aspect that is required to connect with downstream or upstream applications that will render the solution to end users. With all these variables at play, the following approaches help to build a more AI-centric delivery framework: 

• Sprints for data teams are different: While software programming or development is focused on predefined applications or features, the primary focuses for data science and machine learning tasks are analysis, modeling, cleaning, and exploration. Meaning, the data is the center of the universe and the exploration process is what determines the outcome or the features being delivered. The results from the machine and data exploration phase could result in the project having to loop back to the planning phase. As such, the data workstream doesn’t necessarily need to be within or work through the same sprint as the development team.

AI Project Delivery Iterations 

• Embed research or “spike” sprints to create room for understanding and data exploration: Unlike humans, machines need to go through diverse sets of data to understand the context within which it is being applied at your organization (a knowledge graph significantly helps in this process) and align it to your expected results. This process requires stages of understanding, analysis, and research to identify relevant data. Do your AI projects plan for this research? 
• Embrace testing and quality assurance (QA) from the start: Testing in AI/ML is not limited to the model itself. Ensuring the data quality stays sufficient to serve use cases and having the right entry point checks in place to detect potential data collection errors is a foundational step before starting the model. Additionally the QA process in AI and ML projects should take into account the ability to test integration points as well as any peripheral systems and processes that serve as inputs or outputs to the model itself. As time goes by, having a proven integration and automation process to continue updating and training the model is another area that will require automation itself. 
• Prepare for organizational impact: When it comes down to implementation, some projects are inherently too big. Imagine replacing legacy applications with AI technology and models, for instance. There needs to be supporting organization-wide processes in place to ensure your model and delivery is supported all the way throughout strategy, implementation, and adoption. There are more players that need to be involved in addition to the project team itself. 

2. Know what is really being delivered

For machine learning and AI, the product is the algorithm, or the model, not necessarily the accuracy of the results. Meaning, if the model is right, with the right data, it will deliver the intended results. Otherwise, garbage in, garbage out. Understanding this dynamic is key when defining acceptance criteria and your minimum viable product. Additionally, leveraging UI/UX resources and wireframing sessions facilitates the explanation of what the AI tool really is and sets expectations around what it can help stakeholders achieve before they test the tool.

• • AI scope is mostly driven by two factors, use cases and available data: Combining top-down discovery and ideation sessions with end-users and subject matter experts (SMEs) with bottom-up mapping and review of content, data, and systems is a technique we use to narrow down AI/ML opportunities to define the initial delivery requirements. As the project progresses, there will almost always be new developments, findings, and challenges that arise. The key to successful definition of what is really being delivered is building the required flexibility into iteration cycles and update loops for end-users and SMEs to review exploratory results from the data and ML workstream regularly and provide context and domain knowledge to refine the model based on available datasets. 
  • Plan for diverging stakeholder opinions: Machine learning models are better than a human at browsing through thousands of content items and finding recommendations that organizational SMEs may not have thought of. However, your current data may not necessarily capture the topics or the “aboutness” of how your data is used. Encouraging non-technical stakeholders to provide context by participating in the ideation and the acceptance criteria development process is key. You need SMEs to help create a rich semantic layer that captures key business facts and context. However, your stakeholders or SMEs may have their own tacit knowledge and memory of your organization’s content to say what’s good or bad when it comes to your project results. What if the machine uncovers better content for search results that everyone may have forgotten about? And remember, missing results are not necessarily bad because they can help identify the content or data your organization is currently missing.
  • Defining KPIs or ROI for AI projects is an iterative process: It is important to create the ability to ensure the right solution is being developed and is effective. The definition of the use case, acceptance criteria, and gold standard typically serve as introductory benchmarks to determine how to measure impact of the solution and overall success and return. However, as more training data is added, the model is continually updated and can change significantly over time. Thus, it is important to understand that the initial KPIs will usually have assumptions that are validated and updated as the solutions are incrementally developed and tested. It is also critical to have baseline data in order to successfully compare outcomes with ML/AI and without. Because setting KPIs is a journey, it really boils down to planning for and setting up the right governance and monitoring processes to support continuous re-training of the model and measure impact frequently. 

3. Plan for ancillary (potentially hidden) costs

This is one of the primary areas where AI projects encounter a reality check. If not planned for, these hidden costs can take many forms and cause significant delays or completely stall projects. The following items are some of the most common items to consider when planning to scale AI efforts:  

• Size and quality of the right data: AI and ML models learn from lots of training data. The larger the dataset, the better the AI model and results will perform. This required size of data introduces challenges including the need to aggregate and merge data from multiple sources with different security constraints, diverse formats (structured, unstructured, video files, text, images, etc.). This affects where and how your data and AI projects teams spend most of their time i.e., preparing data for analysis as opposed to building models and developing results. One of the most helpful ways to make such datasets easier to manage is to enhance them with rich, descriptive metadata (see next item) and a data knowledge graph. 
• Data preparation and labeling (taxonomies / metadata): Most organizations do not have labeled data readily available for effective execution of ML and AI projects. If not planned for or staffed properly, the majority of your resources will be spent in annotating or labeling training data. Because this step requires domain knowledge and the use of standards and best practices in knowledge organization systems, organizations will have to invest in formal and standardized semantic experts and hybrid automation in order to maintain quality and consistency data across sources.
• Licenses and tools: The most common misconceptions for Enterprise AI implementations and why many AI projects fail starts with the assumption that AI is a “Single Technology” solution. Organizations looking to “plug-and-play AI” or who want to experiment with a variety of open source tools need to reset their expectations and plan for the requirements and actual cost using these tools as costs can add up quickly. AI solutions range from data management and orchestration capabilities to employing a solution for metadata storage, and depending on the use case, the ability to push ML model results to upstream or downstream applications. 
• Project team expertise (or lack thereof): Experienced data scientists are required to effectively handle most of the machine learning and AI projects, especially when it comes to defining the success criteria, final delivery, scale, and continuous improvement of the model. Overlooking this foundational need could result in even more costly outcomes, or wasted efforts after producing misleading results or results that aren’t actionable or insightful.

Closing

The approach to enable rapid delivery of AI and its adoption continue to evolve. However, the challenges with scale still remain attributed to many factors including selecting the right project management and delivery framework, acquiring the right solutions, instituting the foundational data management and governance practices, and finding, hiring, and retaining people with the right skill sets. And ultimately, enterprise leaders need to understand how AI and Machine Learning work and what AI really delivers for the organization. The good news is that if built with the right foundations, a given AI solution can be reusable for multiple use cases, connect diverse data sources, cross organizational silos, and continue to deliver on the hype. 

How’s your organization tracking? Find out if your organization has the right foundations to take AI to production or email us to learn more about our experience and how we can help.

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