KM SHOWCASE 2019 - Enterprise Search: The Key to Findability
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This document discusses the implementation of an enterprise search tool at the Federal Home Loan Bank of Chicago. It outlines challenges around poor findability of content across multiple repositories. An agile approach was taken, starting with IT support content. Key steps included analyzing content, developing a taxonomy, gathering requirements, building a proof of concept, implementing the search tool in two repositories, and plans to scale it across the organization. Success will be measured through metrics like ticket resolution time and customer satisfaction.
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KM SHOWCASE 2019 - Enterprise Search: The Key to Findability
- 1. Enterprise Search The Key to Findability Hasan Syed, FHLBC (hsyed@fhlbc.com) Todd Fahlberg, Enterprise Knowledge (tfahlberg@enterprise-knowledge.com)
- 2. “The best customer service is if the customer doesn't need to call you, doesn't need to talk to you. It just works.” – Jeff Bezos
- 3. Hasan Syed Todd Fahlberg Vice President, Knowledge Management Senior Analyst, Knowledge Management Federal Home Loan Bank of Chicago Enterprise Knowledge
- 4. About FHLBC The mission of the Federal Home Loan Bank of Chicago is to partner with our member shareholders in Illinois and Wisconsin to provide them competitively priced funding, a reasonable return on their investment in the Bank, and support for community investment activities. We’re one of 11 Federal Home Loan Banks chartered by the U.S. Congress in 1932 to promote homeownership. Our members include banks, thrifts, credit unions, insurers, and community development financial institutions throughout our district.
- 5. KM CHALLENGES ▪ Vast amounts of content are not associated with any metadata tags resulting in poor findability ▪ Important content is stored in multiple repositories making search efforts redundant ▪ Must be able to enforce strict security constraints ▪ Solution must be scalable to incorporate bank-wide systems as deemed necessary CRM, Incident Management/Helpdesk Wiki Network Drives People’s Heads Enterprise Data Warehouse SharePoint
- 6. IT Support was the logical starting point to create a scalable Enterprise Search Tool. Enterprise IT IT Support ORGANIZATIONAL CHART
- 7. CONTENT & TAXONOMY STRATEGY SEARCH REQUIREMENTS PROOF OF CONCEPT DEVELOPMENT SEARCH SOLUTION IMPLEMENTATION SEARCH SOLUTION EXPANSION 01 02 03 04 05 Cleaned up and prepared content for search ingestion. Gathered functional and technical requirements. Developed PoC and validated requirements. Performed UAT and Implemented Search & Semantic Tool MVP. Take an agile approach when implementing solutions across the organization. APPROACH
- 8. CONTENT STRATEGY Analyzed and Inventoried Content Performed Content Cleanup Developed IT Support Business Taxonomy Developed Content Types Designed Content and Taxonomy Governance Structure Task Output Discovered 765 content items across 2 repositories. IT Support Content Owners identified 488 content items for archival, 145 to maintain as-is, 125 items that need updating, and 20 for deletion. Identified 284 concepts belonging to 4 schemes. Identified and developed 7 content types. Established a common set of standards and processes that are designed to maintain and consistently improve the enterprise taxonomy and content overtime.
- 9. REQUIREMENTS GATHERING • Approached project with Agile Methodology (i.e., PoC, MVP, and Beyond) • Identified security needs for each repository • Distinguished shared programming languages between FHLBC and EK • Established hosting needs • Developed wireframe to more easily gather functional requirements
- 10. PoC DEVELOPMENT http://blink/ http://wiki/ Native Search Enhanced Search Repositories Repository Connector Enterprise Search Tool FrontEndBackEnd The Enterprise Search Tool Proof of Concept (PoC) was developed to query content from 2 repositories that were autonomously enhanced by PoolParty.
- 11. SEARCH IMPLEMENTATION ● Increased findability by leveraging the 284 concepts or “tags” contained in FHLBC IT Support’s business taxonomy ● Indexed 400+ content items across 2 repositories
- 12. SCALING SEARCH SOLUTION FHLBC Unified Search Tool Unified Search Business Departments Accounting Sales Risk Legal Marketing Information Technology Repositories K: H: S: X: P: Share Drives Bank-Wide Access Collection-Level Access Document-Level Access
- 13. MEASURING SUCCESS Percentage of Submitted Tickets Average Time Till Ticket Resolution Percentage of Escalated Tickets Customer Support Satisfaction Customer Perception of Search Time Customer Confidence in Content Found QualitativeQuantitative
- 14. NEXT STEPS & TAKEAWAYS • Start Small • Remain Agile • Celebrate Wins
- 15. WE’LL BE ANSWERING QUESTIONS NOW Q A& THANKS FOR LISTENING Q&A SESSION
- 16. TFAHLBERG@ENTERPRISE- KNOWLEDGE.COM LINKEDIN/IN/TFAHLBERG HSYED@FHLBC.COM LINKEDIN/IN/HASANHSYED Hasan Syed Todd Fahlberg CONTACT TWITTER/@ZHHSYED TWITTER/@THETODDOLOGY
Editor's Notes
- Enterprise Search - The Key to Findability Thursday April 4th, 2019: 10:15 a.m. - 11:00 a.m. Long-term enterprise Knowledge Management (KM) efforts run the risk of decreased focus or dwindling support because good integrated KM programs are often largely invisible, only becoming visible when there is a problem. One key area where the business can experience meaningful, tangible value is through Enterprise Search. In this presentation Hasan Syed will share the steps he took to lead his organization in the design and implementation of an enterprise search tool, called “Unified Search.” In partnership with Enterprise Knowledge, his organization is prepared to scale the enterprise search solution to include additional repositories, address differing security needs, and measure the success of this implementation.
- Todd: To break the ice and quote the richest man on earth, Jeff Bezos. “The best customer service is if the customer doesn’t need to call you, doesn’t need to talk to you. It just works.” The quote and visual on screen does a great job to describe and visualize the depth of a good integrated Knowledge Management system. From a user, client, or customer perspective, the front-end or top side to a solution is smooth sailing with sun shine and rainbows. Its user centric and does its job. When looking behind the scenes or performing a deep dive on a KM System, it becomes obvious a lot of effort and strategy went into making the solution a reality. Being in the information age, enterprises are often plagued by vast amounts of content, strict regulatory controls, and so many repositories that its unclear to users where to save anything. Today, Hasan and I are here to share a success story with you all how enterprise search can solve these problems, and when implemented, starting with a small team, can set the foundation to scale to the entire enterprise, in this case a bank with over a petabyte of data.
- Todd: Before Hasan shares the steps he took to lead his organization in the design and implementation of an enterprise search tool. Let us first briefly introduce ourselves.. Hasan, can you share a bit about your background? Hasan – Introduces himself. Todd – Introduces himself. At EK we provide end to end KM strategy and implementation. "Deep technical background but in the most recent years i've consulted to some of the largest and most recognizable organizations about their technical strategies, such as semantic technologies, cms's, and search solutions. Hasan – Thank you Todd.
- Hasan: Background info on FHLBC.
- “The reason why we started the whole thing” Hasan: What challenges that led to these KM initiatives? Hasan: Started with the IT Support team. The bank employees are the customers. Long term objective is to provide seamless service to the customer
- Hasan – Start slide with a qualifier * (add the thought process on how we got from bank-wide challenge to IT Support) So to incorporate and implement a solution to meet the bank wide needs, we decided to take on the challenge with an integrative approach. For FHLBC, IT support was the logical starting point to create a scalable enterprise search tool. Discuss how we started the project, 3 year plan (IT support with successful completion > Departments within bank > Rest of bank) Also, IT support will support the application. Usually organizations buy tool, implement, then tell helpdesk to support it. IT Support is largest consumer of knowledge in IT, and most communicated with across the bank **continue to refer to users as customers
- Todd: To expand on scaling this solution to the enterprise, presented are the steps we’ve taken for FHLBC’s IT Support team and plan on using again as we continue to scale the search tool. Keep high-level, don’t get technical What approach did we take to work towards meeting those objectives? Todd Transition: So to dive into the specifics, Hasan can you expand on the important first step of Content & Taxonomy Strategy?
- Hasan: Content strategy is not about tool. Its about the content. KM is not about “a new application” IT Support Content Owners identified 488 content items for archival, 20 for deletion, 145 to maintain as-is, and 125 items that need updating. Identified and developed 7 content types Content types will help provide structure for content creation as well as ensure end-users have the right information available in the content in a user friendly and more digestible format Taxonomy output: focus on leveraging tagging for search maybe? easiest to understand. Governance teams were identified to maintain and further the development of taxonomy and content?
- Todd Agile approach PoC > MVP Kept Backlog Security Document Level security vs collection level vs nothing at all
- Todd start, Hasan end Developed the Proof of Concept (PoC) to include 2 repositories that were enhanced by PoolParty. The content was then indexed by Elasticsearch to be queried by 1 interface. The Enterprise Search Tool Proof of Concept (PoC) could query content from 2 repositories that were autonomously enhanced by PoolParty. End: In a nontechnical tone, this interface enables users to query content across multiple repositories in one centralized location.
- Hasan “So from a user standpoint, our customers, we wanted the new search tool to function both like Google and Amazon” All are familiar, helps adoption rate Physically point to the facets as you talk about them What makes this like google = for search, enhanced content with robust metadata, strengthens the relevancy score of queried content enabling more accurate results “Facets and metadata” Amazon = Point to the facets. Drill down by repository, content type, and tools etc From a customer perspective, they don’t have to worry about the efforts going on behind the scenes, they just utilize this user-centric design to query their content. Talk how this benefits IT Support…. As the solution matures, other departments will start to notice the success of the search engine and how powerful its features and functionalities are. Not only does it help IT Support internally, but also enables them to better support their customers, externally. As a properly implemented KM Solution, it will begin to market itself.
- Hasan: Original objective was to scale, Todd, what does scaling entail Todd Unified Search Tool to meet every bank users needs. So from a technical standpoint, we know the users content is being surfaced. However, from a business perspective, these efforts need to be validated.
- Hasan To prove its value, we need to measure the search tools success. Knowledge and Confidence before doing something(behavior) Once you have enough behavior, then you have organizational impact. Quantitative Qualitative Now, at the close of our presentation we wanted to address what is always on everyone's mind: "...but how are you measuring return on investment". ROI is measured and shared through a combination of numbers with narrative. We find if you just collect hard metrics, but there is no context placed around them, the numbers don't mean anything. You get a "so what?" from your audience. If you just tell a story about progress without any numbers to back it up you get a "show me the proof" response from your audience. This is why you want to collect quantitative and qualitative metrics; so you have numbers and narrative and you can tangibly demonstrate the value of the investment made. And for those of us who haven't been in a stats class for some time, as a reminder, quantitative metrics are metrics that result in hard numbers and qualitative metrics are perception-oriented metrics. So, as you see here on the slide, the quantitative metrics we are collecting can tell the story and prove that the investment in search is saving the organization money. Every time a ticket stays open or needs to be escalated it costs our organization money and a significant amount at that. We need to reduce the time tickets are open and reduce the percentage of tickets that are escalated to a higher tier of service. These metrics give us a compelling case to put forward regarding the value of search in the organization. Over on the qualitative side, you'll see that we are collecting metrics that tell us how search impacts our customer base. We don't want frustrated customers who can't find what they need, who do not feel confident in what they have found and whose satisfaction with our service is lacking. Search can improve findability of content, findability of accurate and up-to-date content, and overall satisfaction with the search experience. So with these qualitative metrics, we can tell the story of a happy customer who has the content they need to take decisive action. Now what you see on the slide are just examples, this is not an exhaustive list, but hopefully, this gives you a sense of direction on best practices for collecting measurements and how you can weave measurements into a compelling case.
- Hasan To prove its value, we need to measure the search tools success. Knowledge and Confidence before doing something(behavior) Once you have enough behavior, then you have organizational impact. Quantitative Qualitative Now, at the close of our presentation we wanted to address what is always on everyone's mind: "...but how are you measuring return on investment". ROI is measured and shared through a combination of numbers with narrative. We find if you just collect hard metrics, but there is no context placed around them, the numbers don't mean anything. You get a "so what?" from your audience. If you just tell a story about progress without any numbers to back it up you get a "show me the proof" response from your audience. This is why you want to collect quantitative and qualitative metrics; so you have numbers and narrative and you can tangibly demonstrate the value of the investment made. And for those of us who haven't been in a stats class for some time, as a reminder, quantitative metrics are metrics that result in hard numbers and qualitative metrics are perception-oriented metrics. So, as you see here on the slide, the quantitative metrics we are collecting can tell the story and prove that the investment in search is saving the organization money. Every time a ticket stays open or needs to be escalated it costs our organization money and a significant amount at that. We need to reduce the time tickets are open and reduce the percentage of tickets that are escalated to a higher tier of service. These metrics give us a compelling case to put forward regarding the value of search in the organization. Over on the qualitative side, you'll see that we are collecting metrics that tell us how search impacts our customer base. We don't want frustrated customers who can't find what they need, who do not feel confident in what they have found and whose satisfaction with our service is lacking. Search can improve findability of content, findability of accurate and up-to-date content, and overall satisfaction with the search experience. So with these qualitative metrics, we can tell the story of a happy customer who has the content they need to take decisive action. Now what you see on the slide are just examples, this is not an exhaustive list, but hopefully, this gives you a sense of direction on best practices for collecting measurements and how you can weave measurements into a compelling case.