The Solution

In order to deduplicate the records and associate them to identifiable, unique individuals, EK’s team of experts started from the bottom up, conducting an exhaustive exploratory data analysis of each attribute in the data. Using the completeness and uniqueness of data fields, we then prioritized fields for further exploration, allowing us to quickly deliver value by focusing on the data attributes with the highest relevance for matching individuals.

Working with our partner, we developed a data processing pipeline to take messy input data and create a graph of linked records that corresponded to real world individuals. Our pipeline focused on the following steps:

• Data Cleaning and Normalization – Our first step involved cleaning and standardizing data to maximize matching opportunities. 
• Graph Construction – To maximize our potential for matching records, we constructed a knowledge graph, allowing us to match records through the graph based on shared attributes or intermediary nodes.
• Rules-Based Matching Algorithms – After looking for explicit matches, EK developed algorithms that would link records pertaining to the same individual.
• Iterative Validation and Match Quality Improvement – Every matching algorithm developed was documented, validated, and adjusted to ensure quality results and alignment with business stakeholders. Furthermore, working in an Agile manner, EK was able to both continually build on existing algorithms and develop new rules, increasing the quantity and quality of matches.

The EK Difference

EK’s vast experience in knowledge graphs played a key role in delivering a transparent, explainable solution that outperformed the client’s existing black box legacy systems. Using an Agile approach, EK was able to maintain alignment with both our partner and the marketing firm’s business stakeholders, ensuring that we were able to deliver high value results quickly. In addition to building state-of-the-art graph models, conducting in depth data analysis, and writing detailed technical reports, our data scientists, graph engineers, and analysts collaborated to ensure that all technical terminology and decisions were documented in a business glossary that was accessible to non-technical users. In doing so, EK was able to leverage our knowledge sharing culture, facilitating discussion and collaboration with key stakeholders to ensure the end solution was a true made-to-measure system that solved our client’s unique business needs.

The Results

By implementing our data pipelines and matching algorithms on the knowledge graph, we managed to reduce the number of unique records by 70% percent, more closely aligning with the target population of 240 million marketable US adults. In doing so, we allowed our client to connect the dots between data that was previously siloed in separate systems, creating a clearer picture of customer behavior and trends. Through collaboration with our technology partner, we continue to fully automate the graph creation and deduplication process. This gives our client the ability to quickly ingest and connect new data, ensuring that the graph, and corresponding business intelligence, will continue to expand.

360-view-of-a-Consumer-Deduplicating-and-Constructing-Consumer-Data-Using-an-Identity-GraphDownload

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The Solution

To organize and typify the various categories of both the institution’s knowledge and that of its employees, EK enriched their business taxonomy, developed an ontology and a knowledge graph to create a semantic hub (colloquially referred to as “The Brain”) that, while leveraging the knowledge graph, collects organizational content, user context, and project activities. This solution uses AI to automatically deliver content to bank employees when and where they need it. The Brain was built on a graph database and a taxonomy management tool. Content from around the organization is auto-tagged (using the taxonomy management tool) and collected within the graph database. Together, these two tools, in which this aggregated information is managed and stored, power a recommendation engine that delivers contextualized recommendations via email, suggesting (in the form of links or attachments) relevant articles and information per the following scenarios:

• A user schedules a calendar event on a given topic, or 
• New content is introduced to the system that matches a user’s pre-defined interests.

Presently, the same strategy is being expanded to power a chatbot as part of the bank’s larger AI Strategy. These outputs are published to the bank’s website to help improve knowledge retention and to showcase the institution’s in-house expertise via Google recognition and search optimization for future reference.

The EK Difference

Leveraging our vast experience with taxonomy/ontology design and semantic technologies, we helped the bank model their domain through a series of workshops and stakeholder interviews. Once the domain was in place, we applied our expertise in Solutions Architecture and Big Data orchestration to develop an application that quickly and efficiently loads and tags content from multiple sources into a single repository – a Knowledge Graph – used to provide recommendations to bank staff.

We specifically applied our core competency in analysis, design, implementation, operations, and maintenance of information management systems and technical platforms for managing subject expert knowledge and topical information to ensure the bank had a solution that met their specific needs. Throughout the entire process, EK went beyond technical implementation, engaging with business users to ensure we were designing interfaces, workflows, security models, content cleanup practices, classification procedures, and governance guidelines to inform and define the long-term adoption and sustainability of the system.

EK further employed our data science and engineering experience to iteratively enable knowledge-oriented AI to train the recommendation algorithm and upstream applications to consume and “understand” the bank’s data in a manner similar to which their staff understands and uses it.

The Results

In addition to connecting people to information, the tool is providing timely content recommendations on three different web applications and in advance of important meetings, as well as via a Chatbot service.

Using knowledge graphs based on this linked data strategy enabled the bank to connect all of their knowledge assets in a meaningful way to:

• Increase the relevancy and personalization of the search experience;
• Enable employees to discover content across unstructured content types, such as webinars, classes, or other learning materials based on factors like location, interest, role, seniority level, etc.; and
• Further facilitate connections between people who share similar interests, expertise, or location.

Knowledge-AI_-Content-Recommender-and-Chatbot-Powered-Auto-Tagging-and-an-Enterprise-Knowledge-GraphDownload

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Enterprise Knowledge has again been named one of the “100 Companies That Matter in Knowledge Management” by KMWorld Magazine, recognized for their global leadership in Knowledge Management Consulting services. This is the seventh consecutive year that KMWorld has recognized EK with this honor.

EK is uniquely recognized as one of the only KM Consulting firms on the list. As in past years, EK has been distinctly noted for offering the complete range of KM Consulting services, including strategy, design, implementation, and operations. EK has also been recognized as a global leader in Knowledge AI, leveraging ontologies and knowledge graphs, amongst other advanced technologies for a wide range of global organizations. 

With its consistent growth, EK is the largest dedicated knowledge management consulting firm in the world. This is the 22nd year KMWorld Magazine has published the list of Global Consultancies and Solution providers in the knowledge and information management field.

“Thanks to KMWorld for their continued recognition of EK’s leadership in the Knowledge Management community,” said Zach Wahl, CEO of Enterprise Knowledge. “With the global challenges of the last year, organizations are realizing the critical role that KM plays in enabling effective work from home, remote learning, and connected data. Leveraging our proprietary KM Benchmark and Enterprise AI Readiness Assessment, we’re working with organizations to understand where they are, help them define a vision for where they need to be, constructing detailed road maps and strategies to attain their goals, and then working side-by-side with them to transform their organizations and implement they KM systems.”

The complete list of awardees can be viewed here. 

EK CEO Zach Wahl’s View from the Top article regarding this recognition can be viewed here. 

“Flexibility, agility, and the ability to pivot are attributes that have become critical to forward-thinking companies—and that is particularly the case now. Successful organizations don’t want to merely survive; they want to dominate their market sectors. But to do that, they need the right tools and products,” said Tom Hogan, Group Publisher at KMWorld. “Amidst the dramatic changes taking place today, innovative organizations are seeking new approaches to improve their processes. The 2021 KMWorld 100 is a list of leading-edge knowledge management companies that are helping their customers to expand access to information, leverage new opportunities, and accelerate growth.”

About Enterprise Knowledge

Enterprise Knowledge (EK) is a services firm that integrates Knowledge Management, Information Management, Information Technology, and Agile Approaches to deliver comprehensive solutions. Our mission is to form true partnerships with our clients, listening and collaborating to create tailored, practical, and results-oriented solutions that enable them to thrive and adapt to changing needs.

About KMWorld

KMWorld is the leading information provider serving the Knowledge Management systems market and covers the latest in Content, Document and Knowledge Management, informing more than 21,000 subscribers about the components and processes – and subsequent success stories – that together offer solutions for improving business performance.

KMWorld is a publishing unit of Information Today, Inc. 

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The benchmark was developed by EK’s internal team of experts, considering the disciplines of data management, knowledge management, taxonomy/ontology design, knowledge graphs, machine learning, natural language processing, data science, and data governance to develop a comprehensive gauge of whether an organization is prepared to implement Knowledge AI in their organization, and pinpoint where their deficiencies may be.

Whereas EK’s clients generally leverage the complete benchmark in order to ensure they can successfully deliver on prototypes and pilots, design solutions, and implement enterprise scale advanced systems, the Enterprise AI Readiness Assessment is a free and open survey that walks individuals through an abridged self-guided and self-scored set of questions, generating a customized summary report.

This free and open report has been created by EK as part of our commitment to thought leadership in the industry. It provides scores and recommendations across four key factors: 1) Organizational Readiness, 2) State of Enterprise Data and content, 3) Skill sets and Technical Capabilities, and 4) Change Threshold and Readiness. The assessment gauges an organization’s current capabilities across these four factors. The score and report provides the insight needed for an organization to avoid common pitfalls and effectively move towards advanced Enterprise AI.

The Assessment is available to complete at https://s.enterprise-knowledge.com/ekaiassessment. After completing the assessment, participants will receive a free downloadable report.

_______________________

About Enterprise Knowledge

Enterprise Knowledge (EK) is a services firm that integrates Knowledge Management, Information and Data Management, Information Technology, and Agile Approaches to deliver comprehensive solutions. Our mission is to form true partnerships with our clients, listening and collaborating to create tailored, practical, and results-oriented solutions that enable them to thrive and adapt to changing needs.

Our core services include strategy, design, and development of Knowledge and Data Management systems, with proven approaches to deliver advanced capabilities such as Machine Learning and AI, Project Strategy and Road Mapping, Brand and Content Strategy, Change Management and Communication, and Agile Transformation and Facilitation. At the heart of these services, we always focus on working alongside our clients to understand their needs, ensuring we can provide practical and achievable solutions on an iterative, ongoing basis.

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“Knowledge graphs, presently, are one of the keys to successful implementation of Knowledge AI. I was happy to share EK’s experience putting these exciting concepts and technologies into practice for our clients,” said Hilger.

Database Trends and Applications is a magazine covering data and information management, big data, and data science. In addition, their website connects visitors with white papers, webinars, and other learning opportunities in the field. The magazine and website deliver advanced trends analysis and case studies serving the IT and business stakeholders of complex data environments.

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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.

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