We’re in an exciting position to experience continued growth during a time where other organizations are pulling back. Though there are many factors that go into this, I’ll sum it up with the two most significant. First, EK continues to be filled with some of the smartest, most conscientious, and most creative people I’ve ever worked with. This team of truly special people drives our growth and value to our clients, and is my personal primary motivation every day. Secondly, as a company, we’ve always chosen to look beyond the current trends and consider what our clients truly need to support their business. Over the years, this has pushed us to be early adopters and thought leaders in agile KM, knowledge graphs, content assembly, ontology management, and data catalogs, amongst many others, leading to better, more forward-looking solutions for our clients and continued growth for EK.

I’ll use EK’s six guiding principles to further discuss our year.

People – Our number one asset is our people. We invest in them and ensure they possess the knowledge and resources to serve our clients to the highest degree possible.

As I mentioned in my introduction, we are where we are in large part due to our team. We’ve done our best to always honor this principle above all others, and our team has rewarded us for that with amazing results and spirit. This year we began welcoming employees back to the office on an optional basis and I was thrilled to see people choosing to come back, and feel the energy and collaboration return to the office. Though we will be a hybrid organization moving forward, it was exciting to see people choose to return to the office and leverage that time to make stronger connections and learn from one another. This collaboration will help fuel the next round of EK innovations we bring to our clients.

We continued to grow our staff this year, adding new team members at all levels. Given our growth and forecasts for 2023, we’ve got more work to do there, including our existing openings. We also continued our investment in our team, successfully rolling out our year-long onboarding process called Kamp EK, which includes a mix of formal and informal learning, both live and asynchronous, arrayed over a year with measurable success criteria at each stage in the process. I’m personally enjoying teaching the “Why KM Matters,” and “Introduction to Consulting” modules. We also continued our standing professional learning benefit, where each employee received a guaranteed $3,000 to apply to their development in a way they choose. This year we had one of the highest rates of usage for this benefit, reflected in the fact that twenty team members earned promotions over the course of the year as well.

We added several new successful programs this year to supplement our benefits and reinforce the importance of lifelong learning and work. The first, ‘EK Balance,’ is about health and wellness, as well as overall work life balance. Events have included pop-up juice bars in the office, recurring yoga classes, and massage days. We’ve also been running monthly challenges, including a steps challenge in the fall and a hydration challenge this winter. Participation in these activities has been strong, and we’ve made donations to various charitable organizations (amongst more material prizes) for each participant.

‘EK Grow’ is about career development and lifelong learning. It supplements our $3,000 per year professional learning benefit and adds an additional $1,000 per year for any other type of learning an employee may choose. Thus far, employees have used this for cooking classes, wilderness rescue, woodworking, piano lessons, and language lessons, to name just a few. EK Grow also includes “Pitches and Pints,” where team members can sign up to do dinner and drinks with the leadership team and practice delivering the company pitch deck. This has been a wonderful opportunity to get to know team members in small groups and also provide coaching on public speaking and effective presentation in a fun setting. 

Though we started the year fully remote and were hybrid throughout, we found some wonderful opportunities to celebrate together, including our first live Gala since before Covid, our annual Holiday Potluck and Purple Elephant, as well as several other great team building opportunities (including movie nights, a summer Pirate Ship cruise, and painting lessons. 

Now for the fifth time, Inc. Magazine listed us amongst their best workplaces. This recognition continues to mean a great deal since it is a national competition, but moreover because it is driven by an anonymous survey of employees. You will read below of the many new wins we received this year, but the recognition driven by our own employees, to me, continues to be one of the best each year. This gives us a great sense that, though there will always be more to do, we continue to head in the right direction. 

Thought Leadership – We serve as leaders in the industry, sharing our knowledge and expertise, guiding the development of agile knowledge and information practices, and supporting the community.

This year, we broke our past record and published a total of 93 new thought leadership pieces in our knowledge base, meaning we now have nearly 500 total pieces of thought leadership, free and open to the community. The knowledge base includes blogs, white papers, case studies, slide presentations, videos, and podcast episodes. Our podcast, Knowledge Cast, was named the number one KM podcast for the second year in a row, and this year we recorded a live episode of Knowledge Cast as the closing keynote at KMWorld. Speaking of KMWorld, we broke another record there, this year delivering twelve separate presentations, including client case studies alongside our clients at NASA Jet Propulsion Lab, U.S. Department of State, and Walmart, amongst others. A highpoint of the year was being recognized at the conference alongside Walmart as the 2022 KM Reality Award Winner for the work we’ve done with them.

Another major thought leadership highpoint was the publication of ‘Making Knowledge Management Clickable,” the book I coauthored with EK’s COO Joe Hilger. Published by Springer, the book bridges the gap between knowledge management and technology and details the complete lifecycle of knowledge, information, and data from how knowledge flows through an organization to how end users want to handle it and experience it. In short, it is EK’s total experience rolled into 318 pages of very small print. 

Between the knowledge base, conference speaking, podcast, and book, we were proud to receive additional recognition from the industry. Again this year, KMWorld and Info Today recognized EK as one of the 100 Companies that Matter in KM for the eighth year in a row, as well as one of the 50 Companies leveraging AI to drive Knowledge Management for the third year in a row. 

Transparency – We communicate clearly and openly, ensuring the highest level of quality and accountability for our company’s management, in our service to our clients, and with respect to our colleagues.

When we talk about transparency we are referring to openness through all channels, between leadership and staff at EK, between us and our clients, and between each other as colleagues and teams. The last couple of years of Covid have been a major learning lesson for us. Communications that were easy in the office became much harder digitally. Even with our hybrid return to the office, we’ve learned from that experience and are working more to improve all forms of communication.

One of the primary company-wide communication techniques we use is our bi-monthly all-hands employee knowledge share, where we discuss company goals and achievements, new wins, greet all new hires, and have employees present on new learnings or innovations. We also use this time to discuss any potential issues or challenges. 

This past year, one moment of transparent communication stands out for me. We were notified by one of our clients that their executives were insourcing all consulting services due to poor financial projections and, out of their control, our work with them would unexpectedly end. This was not a crisis for EK, as we are incredibly well-diversified, but it was nonetheless bad news. The news would unexpectedly move several people to “the bench” and represented a significant future revenue loss for us. I immediately sent out an all-company email on this and we opened the topic for questions and conversations at the next knowledge share. We managed the transition gracefully and came out of it a better company for it, demonstrating the openness and trusted dialogue for which we strive.

Since we also were able to return to a live Gala this year, we had that opportunity to celebrate important achievements and milestones, including our annual CEO and COO awards and the “Jacketing” of employees that have completed a three-year tenure at EK. Each new employee, every birthday, and every promotion are also communicated and celebrated company-wide. These celebrations aren’t just about the what, but about the why, covering the impact we can have for our client partners, for our community, and for each other.

Partnership – We partner with our clients, building meaningful relationships founded on a sustained commitment to mutual success.

The word “Partnership” was chosen very carefully when we first crafted our guiding principles in 2013. We wanted to express our vision to be true consultative partners and trusted advisors to our clients, rather than generic order takers. We’ve fulfilled this promise every year of EK’s history, and 2022 was no different. 

In large part due to newly won long-term contracts and long-term partners that have renewed with us, EK will begin 2023 with the largest backlog of contracts and projects in our history. Notably, many of these projects with long-term clients have grown from prototypes, pilots, or group-based initiatives to enterprise level transformations. We’ve helped our clients position and develop these projects for global support, and we’re proud to continue the journey with them and support these hugely impactful programs. We also continue to see our clients come back to us year after year. Small projects have transitioned to large, but moreover, past clients from initial engagements continue to re-engage with us in new and larger ways. Even the aforementioned client that was forced to close our contract due to the economy worked with their leadership to identify an exception enabling us to return in 2023. This is what true EK-client partnerships look like!

One of the greatest results of EK’s client partnerships, growth, and overall success is what we’re able to do with it. Every year since our creation, we’ve actively engaged with our community, both in volunteering our time and in providing financial support to our philanthropic partners. One of the largest and most long-standing of these is with Wolf Trap Center for the Performing Arts, which delivers early arts and music education to schools in need. To date, EK has donated over $90,000 to this incredibly impactful organization, meaning  thousands of children have received access to arts and music education in their early and most critical years of development.

We’ve also created our own No Shave November tradition, which we’ve dubbed ‘Know Shave Knowvember,” of course. In our version, all EK’ers are invited to participate in the way that they choose, either by growing out their beards for the month, or by choosing another activity. This year, some non-beard-growing choices were letter writing, giving up screens outside of work, and volunteering extra time. For every participant, EK donated $200 to the charity of their choice. This year, EK donated over $4,000 in total with recipients including American Foundation for Suicide Prevention, Wounded Warrior Project, Girls Who Code, National Alliance on Mental Illness, and World Central Kitchen, amongst many other. It was a joy for me not just to have EK support these wonderful causes, but to see all of the diverse choices of organizations the team selected. 

Integration – We provide our customers with the full range of EK’s expertise, integrating all of our services and resources to ensure the most significant business value.

Integration has a few meanings at EK. For one, our definition of KM means that we’re helping our customers enhance and integrate all of their knowledge objects, including structured and unstructured information, people, products, and other concepts. It also means that we help organizations integrate their disparate systems, processes, and groups to function more cohesively. Finally, it means that we seek to integrate our own services and bring together the individual talents of our diverse staff to deliver the greatest results for our customers.

Where we find the greatest success is when these separate services combine to form comprehensive solutions. This year, we’ve seen a great trend of organizations hiring us to build Knowledge Portals, Intelligent Learning Systems, and Content Assembly Tools, each of which leverage the power of knowledge graphs alongside an organization’s content and data to drive customized and integrated solutions. These are business critical, transformative tools that for many organizations are helping them rapidly adjust to remote or hybrid work environments. 

Just in the course of the last two months, in fact, we’ve won a major multi-year framework agreement with a European banking organization, a multi-year multi-million dollar contract with DAU, and the USALearning contract, with a potential ceiling of $1.76 Billion. This same period has seen an uptick in commercial wins, with a common thread between all of them; each are seeking EK not for a single service, but for an integrated collection of our Knowledge Management, Learning, and Advanced Technology services. 

Our ability to deliver truly integrated services and solve real business challenges for our customers, typically resulting in measurable return on investment, has helped us to grow and perform again this year. In fact, this year we hit a major milestone, being named to the Inc. 5000 fastest growing companies in the U.S. for the fifth year in a row! 

Energy – We share our enthusiasm with our clients and colleagues, leveraging our excitement to achieve meaningful change.

With growth on the horizon and new team members joining us with their spirit for collaboration and innovation, I am incredibly excited about the year to come. As has been a theme of this review, we are a company that celebrates our successes and our people, and I anticipate a lot of energy and many celebrations in the year to come.

One of the other ways we sought to better express our energy is through a major company rebrand that took place over the course of the year. We shifted our colors and associated iconography, in fact, to express that energy in all of our materials.

I anticipate 2023 will be a year of more time together, more opportunities to work alongside our wonderful clients, and trusted partners, and more time infusing our energy into our work and our community. This year, we’ll be celebrating our ten-year anniversary as a company and I imagine we’ll do it in style.

On behalf of Enterprise Knowledge and the EK Group, I thank you for your partnership, and wish you a wonderful 2023!

The post EK’s Year in Review – 2022 appeared first on Enterprise Knowledge.

• Returning contextual relationships with search results;
• Displaying relevant topics in a knowledge panel; or
• Powering an expert finder.

These advanced use cases enable an organization to provide more domain context and organizational information to users, reducing user time spent searching and improving a user’s ability to discover new content through recommendations. The five steps that EK recommends to implement search with a knowledge graph are as follows.

1. Analyze the Search Content
2. Develop an Ontology for the Knowledge Graph
3. Design the User Search Experience
4. Ingest the Data
5. Implement and Iterate

Depending on your workflow, these steps may not occur in a waterfall order, so keep in mind that, for example, step 3 could be started while step 2 is still in progress. Also, these steps are analogous to the steps necessary to implement a semantic architecture.

Step One: Analyze the Search Content

The first step to a successful knowledge graph search implementation is to analyze the information available for users. If you are just starting a search effort, start small and analyze a handful of data sources that contain key information that end-users always need. This step often involves interviews with business and technical data source owners as well as users to answer the following questions. At the end of this step, you will have a collection of information about data source content with answers to what, where, and how the information can be leveraged.

What information is available?

We want to identify each type of information available from a data source. If we are analyzing a content management system, it may contain deliverables and reports. However, do not stop there. Continue asking questions to dive deeper into what is available.

• What metadata fields exist on a report?
• Can we segment the deliverables at all? i.e. Can we retrieve or link to the pages separately?
• What users worked on this document?

As you dive deep into the content, you will surface key pieces of information that can be put together to solve user needs.

Where is the information and how do we get it?

These two questions inform the development process later on and ensure that information is actually available for use. We find it key to meet with data source technical owners as they will be able to figure out where information lives within a system, how it is generated, and, most importantly, how we can extract the information for use in the knowledge graph. It is best to start this conversation early as often there may be security concerns or development steps that need to be taken in order to build out an integration point.

How is the information related to other information?

Once you know what information is available, facilitate a conversation with the business owners to determine where the information originates and how the information relates to other data sources. With this question, we are hoping to surface concepts like

• Content lifecycle processes that could be tracked to add more context to search;
• Opportunities to combine information from multiple data sources together; or
• New data sources that we should analyze in the future.

Knowledge graphs are great at representing and querying interconnected data as well as providing means to infer additional relationships. We want to take advantage of this feature as much as possible since it helps drive the search user interface design (that we will talk about later).

By collecting the answers to these questions, you are making it easier to take the next steps in implementing search. If step one is still unclear, think of it like designing a content type and consider that our main goal is to create custom search results that utilizes all information at your organization. Understanding not only where information is but where it comes from and how it will change over time is crucial to the next step of modeling the information.

Step Two: Develop an Ontology for the Knowledge Graph

At the end of the first step, we have a large amount of data describing the information contained in all of our data sources and how they relate to each other. The next step is to figure out how we can leverage the information to answer user questions and build a model to support them. This model, academically referred to as an ontology, is the data model of the knowledge graph that we will be piecing together in step four.

Define the User Questions

We strongly believe that the best way to ensure any solution’s success is to gather requirements from the users. EK usually runs a search workshop to facilitate a session with end-users and business stakeholders to elicit feature requirements and determine what information users find helpful. In step one, you collected a lot of data describing the types of information available. Use this data to ask pointed questions, gauging user interest in the data you uncovered. Work with the group to determine how they would like to see information displayed and what questions they would ask of the data. This is an opportunity for users and stakeholders to think outside the box and come up with their ideal solution, no matter how out of scope it may seem at the time. Every idea may be used later while iterating on the solution or to influence the creation of similar features.

Determine the Classes, Attributes, and Relationships

Almost all information can be represented using classes, their attributes, and the relationships between them. Once you know the questions that users want to ask and the requirements for the solution, you can begin to break down the data from step one into classes. For this process, you can follow the following questions.

• What types of information does search need to display?
  (e.g. employees, deliverables)
• For each type, what properties are necessary to display the information in an intuitive way for users?
  (e.g. do end-users need to see the employee’s email?)
• For each type, what relationships exist to other types of information?
  (e.g. are employees related to deliverables at all?)

A majority of these questions will leverage the data collected in step one, but the data is now tuned to match the needs of the users and stakeholders. Use ontology design best practices to validate the reusability and scalability of the data model. The selected classes (types), properties (attributes), and relationships form the initial ontology.

Map the Data Sources to the Ontology

It is critical to keep a mapping of the data source information to the ontology so that you can maintain and upgrade the ontology in future iterations. Keep track of where each type of information originates, how attributes are calculated, and what steps are taken to extrapolate relationships within the information. While developing the mapping, pull a sample set of information from the data sources and mock up some data. Use this mocked data to validate the data types that should be used for each attribute with a technical member of the team. This ensures that the mapping has realistic inputs and outputs that can be leveraged when creating the data pipelines in step four.

Use the knowledge you already have to create complete views of your organization’s information, including people and clients.

Step Three: Design the User Search Experience

In steps one and two, we put our full attention on the data sources, interpreting the available information into a data model that will enable us to populate a knowledge graph. In this step, we want to shift our focus to the end-users and make sure we build a search solution that will solve user needs through an intuitive interface, leveraging the full capabilities of a knowledge graph.

Define the Search User Stories

Work with the application stakeholders and users to define user stories that will help guide the user interface design. Here’s a blog we have written about three key benefits of user stories.

Perspective		Define the search and interface requirements (not features!) from the view of a user. What does a user need from the search solution?
Purpose		Determine why requirements are needed and the benefits they bring. This enables the team to brainstorm and build the best feature to meet the requirements.
Priority		Work with users and stakeholders to order the requirements. A prioritized backlog of requirements ensures the team delivers high interest items first.

When defining the user stories, keep an eye out for use cases that could be solved through an action-oriented search result. We want to note what data points are important to users so that we can best leverage them in the design process to enable users to take immediate action.

Design using Search Best Practices

Start simple and include your basic search features, the search bar, results, and facets. These basic features ensure that anyone, regardless of their background, can find and discover information within search. Facilitate design workshop sessions with users and stakeholders to design search results for types of information and include search best practices.

Use a consistent view when displaying the same content on multiple pages.

Determine which attributes and relationships in the data need to be highlighted in search results versus those that should be only displayed in spots requiring an additional click, like an accordion dropdown or an entirely new page. When designing the interface, standardize how users will interact with the interface and different content types. The consistent interactions build trust with users and ensures that interaction with search is intuitive.

Innovate with Knowledge Graph Search Features

Up until now, step three has been all about designing the search solution using search design best practices. Now that we have that baseline, we want to include knowledge graph specific use cases like the below.

Identify the Search Subject

Use named-entity recognition (NER) or a knowledge graph entity lookup to identify what a user is looking for and present the user with all relevant compiled information about that entity. For example, imagine the search information includes people, documents, and projects. If a user searches for the id of a project, design a project page that the user is redirected to that includes all of the project metadata, links to the documents associated with the project, and all team members that worked on the project. Creating these encyclopedic-like pages for an organization’s content can greatly improve the user’s ability to find the information they are looking for.

Extend the Search Results

Along the same line as the above, surface additional information, properties and relationships, about the search query and search results from the knowledge graph. If a specific term or entity is recognized in the search query, use that to populate a knowledge panel on the right hand side with all relevant information about that term or entity. A knowledge panel provides users with a snapshot of information based on their search query. When displaying the knowledge panel and search results, pull the most up-to-date contextual information about a search result from the knowledge graph. For example, contextual information could include project statuses, most recent documents, or most similar content within the knowledge graph by metadata.

A knowledge panel collects and highlights project details in one place for a user search.

Natural Language Search Across Data

One of the most powerful resources for a knowledge graph search is natural language processing (NLP). NLP enables search to recognize entities in the graph as well as user intent. In one of our knowledge graph projects, EK developed an NLP-based search that recognized what entities a user was asking for and used that context to automatically collect and prioritize big data in a tabular format for analysts to review. This gave business analysts quick access to the data insights they needed from multiple large datasets. The ability to recognize the intent behind a user’s query enables the search interface to adapt and provide specialized answers to the most important questions.

Step Four: Ingest the Data

Steps one, two, and three focus on analyzing, prepping, and designing the knowledge graph search solution. Now that we have our initial plan, we can pull the data together through extract, transform, and load (ETL) pipelines and populate our knowledge graph.

Index the Data Source Information

Using the data collected in step one, build out the integrations with each of the required data sources. When possible, use application programming interfaces (APIs) or other feeds to extract content from the source systems. If this is not possible, database connections or temporary data exports may be required in order to proof out the integrations. Next, determine how content will be indexed from the sources by answering the following questions.

• What amount of content should be extracted each time the pipeline runs?
• How often should the content be indexed?
• Does the content from this data source need to be combined with any other data source?

There are numerous types of indexing techniques in order to ensure that the knowledge graph and search data are kept accurate and up-to-date without overloading the search indexing or data pipelines.

Transform Information using the Ontology Mapping

When designing the ETL pipelines, reference the ontology and ontology data source mapping to ensure that all information is transformed into the expected format. In most cases, this involves using transformation techniques like object mapping (i.e. Entity Framework) or XSLT to transform information from the source format into a graph data format (i.e. RDF) or into a document format for search (i.e. JSON). This is the first time that all information from a data source is being transformed so expect some data quality issues. Required fields may not always be present in the data, the values may not match the expected type, and data standardization issues may need to be addressed. Work with your stakeholders and data source owners to determine where and how issues should be addressed.

Transform and enrich your knowledge with a consistent vocabulary to populate a knowledge graph and display that information to users.

Enrich the Information with Context

One key piece to developing relationships between information from various sources is to leverage NER or an existing taxonomy. As content is pulled into the knowledge graph and search, the metadata fields provided with each type of information may not be enough. Combining information together from multiple sources builds a better picture of each information type, but some of the best sources of similarity reasoning and clustering will come from associating content with entities through auto-tagging a taxonomy or NER and topic modeling of terms. When designing the ETL pipelines to bring in data, consider how content may be enriched by adding in auto-tagging and NER techniques to the pipelines.

Step Five: Implement and Iterate

At this point, the indexed information is within the knowledge graph and search platform. In this step, build out a prioritized feature set based on the search designs from step three. Depending on the selected development stack, it may be beneficial to build an API layer on top of the knowledge graph and search platform and leverage these APIs to pull data into the user interface. In order to get the interface in front of stakeholders quickly, you may need to leverage some of the sample data you created in step 2.

Developing a user-centric product requires feedback early and often. Validate the designs with both stakeholders and users through demos and user testing. Demos allow stakeholders to give instant feedback on the solution as soon as it is available. For user testing, provide users with tasks to perform and observe how users perform the task. It is important to note where users click, where their eyes are drawn to first, and how design choices impact the flow of the website navigation.

Prioritize and iterate on the user interface based on user feedback and testing.

Make sure to continue to explore the unique features of knowledge graphs. Incorporating new relevant sources with relationships to existing data can help cover edge cases of your search queries that are not yet answered. Highlighting inferences made through traversing the knowledge graph at query time, can bring users previously undiscovered steps.

 Always remember search is a journey.

Finally, iterate on the solution and respond to feedback. Steps one through five are meant to be repeated over and over as

• New data sources and information is considered for search;
• Users ask different questions that requires updating the ontology;
• Designs adapt to feedback and testing to provide a more intuitive user experience;
• Pipelines are extended to extract more information from the data sources; and
• Features and design changes are required to the end solution.

Additionally, don’t forget to start small–EK recommends building out an end-to-end system, completing steps 1-5 in order for a subset of content and prioritized use cases. Use this first iteration to test capabilities and identify any integration risks or concerns.

Conclusion

These steps ensure that your organization builds the right search solution, creating a knowledge graph that answers your users’ questions and surfaces the results in an intuitive interface. Building search on top of a knowledge graph enables your organization to provide tailored, advanced search features as well as create a foundation of organizational knowledge that can be leveraged for other use cases such as chatbots, recommendation engines, and data analysis.

Interested in expanding your organization’s search to leverage the capabilities of a knowledge graph? Contact us and let’s work together to build a search solution that fits your organization’s needs.

The post Five Steps to Implement Search with a Knowledge Graph appeared first on Enterprise Knowledge.

Interested in learning more about knowledge graphs and how they can transform your organization and help you achieve your business goals? Contact us. We are here to help.

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I work in Knowledge Management and have increasingly leveraged Knowledge Graphs to address my clients’ needs. However, it’s not often I see a commercial about the technology I work with. It’s an even bigger surprise when the commercial is featured during the Super Bowl, and focuses on the people who support our country.

Why do I think this commercial is about a Knowledge Graph? The commercial shows portions of different Military Occupation Classification (MOC) codes pulled from various  forms and ID cards. These nine-digit codes represent a primary job function and include information on the service member’s position, skill set, and specialty within their branch of the military. The commercial is showing one example of what is likely hundreds of codes in each instance. Even with all of this complexity, Google makes it simple to search for jobs based on any one of these codes, making it easier for veterans to navigate their transition from the Armed Forces.

Google can provide a simple search interface most likely because behind the scenes they are using their Knowledge graph to map MOC codes to military branches to positions to specific skills and training. While I do not work for Google, I would imagine this mapping would look something like the image below.

Knowledge Graphs excel at this type of complex mapping. Once the ontology above is in place, Google can take a simple search query and then infer a position and skill set that translates to jobs outside of the military. In this case, Google’s knowledge graph handles the translation so that employers can continue to speak in their terms and veterans can search using the terms/codes that they understand.

Congratulations to Google for using this powerful technology to match Veterans with organizations that can use their help.

If your organization’s search  has similar complexity around codes and mapping of information, you should consider developing a knowledge graph to support the same kind of interface that Google presents.

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