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Four Tips for Evaluating Search Tools.

The word "intelligent" is used so often in describing KM applications, it can be challenging to evaluate search tools to determine whether they are best for a particular set of needs. Here are four useful ways to evaluate a toolset's ability to understand, reason and learn, and the types of interactivity it can enable:

1. Understanding--The ability to process user context and input in rich, actionable ways.

The more relevant inputs a search can process, the more targeted and precise its results are likely to be. The search query itself can be processed using word stemming and synonym association, as well as deeper natural language understanding of important language patterns. In addition, other elements of context can be applied to quickly and accurately narrow the domain of interest that the query applies to. Categorization metadata, user identity and recent behavioral data can all bring the right responses into sharp focus.

Look for: Tools that can process both context and query input in rich ways, and allow for the efficient evolution of these definitions such that knowledge always stays in synch with evolving questions, terminology and user scenarios.

2. Reasoning--The search tool's techniques and capabilities to collate, assess and prioritize the best response to user input.

There are multiple reasoning mechanisms layered into any sophisticated search algorithm, including word/term frequency and proximity, weightings of specific information sources, and linguistic identification of word patterns, etc. Each search technology uses its own approaches and prioritization of what drives best results. Often, basic search testing can be the best way to confirm the results of reasoning against a particular set of content, given what users are likely to do and expect.

Look for: A clear expression of reasoning mechanisms, and how they can be evolved over time. "Black box" search approaches may be powerful but need to be validated in testing and maintainable against large, evolving collections of information in real-world scenarios.

3. Learning--The mechanisms by which the search system can self-optimize based on user behavior.

Machine learning has increasingly broad applications as it is applied to more scenarios in which calculations of behavior can apply to future responses. Standard "ML" in search refers to the ability to associate knowledge objects with specific query contexts, and promote these likely results for future similar requests.

ML also has more sophisticated application in mapping user behaviors as inputs, such that user behavior patterns before or during their knowledge interaction can be used to calculate the best information needed.

Look for: How machine learning is applied, and how deeply it can be leveraged. Can machine learning be applied to contextual inputs to drive deeper responses to next-best actions? Is there visibility into--and some control over--where ML is applied within a toolset, to tailor and evolve the fit to user needs?

4. Interactivity--How knowledge tools generate and process a sense of a "session" to progressively refine and develop the best expression of a request and potential range of answers.

The proliferation of voice-activated tools, mobile apps and interactive technologies has created a new generation of request/answer experiences far beyond the traditional search bar. Users are expecting to have more conversational-style interactions with search tools. They now ask questions in natural language, are prompted for clarifying information, and can be guided to the best set of resources or knowledge through their responses. These capabilities frame a new paradigm that continuously develops user context in actionable ways, to deliver information in text, database or article form depending on the expressed need.

Look for: Tools that interact in clear and flexible ways. Interactive voice assistants have become the vogue for this model, but they're not all the same. Can an interactive tool process a wide variety of expressed user needs/questions? Can it process the context implied by the request to reason deeply, support extended conversational interaction, and deliver highly accurate responses?

While the intelligence of various types of search technologies has developed exponentially over the past decade, the core mission and evaluation criteria remains fundamentally the same. Can users express their needs to a knowledge tool, as they understand it, and quickly navigate to the best possible answer efficiently and intuitively? Strong toolsets will respond effectively to these two basic goals, which have always been at the core of KM: the ability to express an information need, and get a fast, accurate and useful response.

By John Chmaj, Sr. Practice Director, Knowledge Management, Verint Global Consulting Services

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[c] 2019 Verint Systems Inc. All rights reserved worldwide.

John Chmaj has worked for over 30 years in the field of knowledge management for Support & Service and has had KM-focused roles in many organizations, including KANA, Microsoft, and Lotus. He has worked in all phases of the customer support process, including telephone and online support, technical writing, knowledge management, applications development, and worldwide knowledge systems design.
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Author:Chmaj, John, Sr.
Date:Jul 1, 2019
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