Profiting from the next level of knowledge management

Usage isn’t a bad way to start. Under this approach, you analyze which content is currently used the most, select the 20 percent highest use and create an intelligent assistant, with that 20 percent most carefully repurposed into detailed components. That may help, but it can also miss the point. Current usage patterns can reflect engrained behaviors, and users tend to stop searching for content they can’t find. You could select content that is easy to find, but not select content that solves your problem or achieves your targeted benefit.

A better way to start is to analyze barriers to your business objective. We strongly suggest beginning your project with a clear, measurable objective in mind. Then, analyze at the task level where 80 percent of the potential time or cost is embedded. Once you’ve found the fewest tasks that comprise 80 percent of the performance gap, take a look at information usage, research time, reading time and searching multiple content stores.

As an example, more detailed benefits analysis leveraging Pareto techniques found the following detailed issues that intelligent assistants could resolve for a leading global electronics manufacturer:

• Fifty percent of their time was spent on research.
• Getting the research right and complete the first time meant only having to suit up once to enter the clean room, then solve the issue.
• First-shift solve was important. Getting the right parts identified and ordered before the shipping cutoff the next morning was key to minimizing customer downtime.
• Making sure the information could be printed on clean room paper as well as made available electronically was key. Not all customers would allow a smart phone/laptop/tablet into their high-security fabrication facility/clean room.

Get the information architecture right.

Two primary barriers usually must be overcome to deliver intelligent assistant applications that hit targeted results:

• Define smaller, more precise units of content.
• Integrate across multiple, fractured content stores.

So, having identified the content that is needed to solve your Pareto analysis, you must define a taxonomy and metadata that adequately identify your more detailed components. The best way to do that is at the task and role level.

Defining your content in smaller chunks around roles, tasks and concepts unlocks your ability to move to intelligent assistants. When procedures, bulletins and training material are in smaller chunks, they can be more accurately tagged and made searchable. Those smaller chunks are also easier to summarize and present as answers, eliminating the need to read through long documents looking for the paragraphs that matter.

Frequently, an intelligent assistant must also integrate information across separate data stores and content types. For example, the intelligent assistant that drove significant productivity improvements for a major electronics manufacturer had to integrate results from multiple sources of technical publications, ERP systems, parts systems and several service procedure systems. In total, more than 15 systems were integrated to present answers to technicians, versus returning sets of documents to read.

The benefits possible through intelligent assistants often unlock executive approval to get started on a refresh of your enterprise information architecture. The right information architecture allows you to leverage component content and integrate information across disparate sources of information to present answers. Answers lead to hard-dollar benefits. Using Pareto analysis focuses the team on smaller, bite size projects that deliver benefits. Benefits build momentum. You gain the right to refresh your enterprise information architecture (EIA) one step at a time. If you don’t have an EIA, intelligent assistants are powerful ways to unlock executive commitment to create a high-value EIA.

Leverage DITA approaches and technologies.

The Darwin Information Typing Architecture (DITA) standard and related technologies are opening up a much more productive way to represent component content and create that content with simple DITA-based authoring and management tools. Using DITA as the basis for component content has a number of advantages:

• DITA is component-oriented, so it is natural to create content components at the task or concept level.\
• The latest generation of DITA editors provides simple-to-use tools, with which businesspeople can create DITA content, with no special training. You don’t have to be XML experts to create ?DITA content.
• Component-oriented review and approval speeds time-to-delivery for new content. You only review and approve new or revised components—not entire manuals.
• DITA single-source content can be formatted for print or electronic delivery. That includes text-to-speech applications.
• DITA uses semantic markup—a step in a procedure is actually tagged as a step versus a numbered paragraph. That improves search precision—for example, you can search for all the steps that refer to a specific error code.
• As an XML format, DITA is machine-readable, so an application can interact with DITA content in intelligent ways. For example, DITA allows you to build an application that walks through procedures a step at a time to prevent users from getting lost in long, complex tasks.

Don’t forget governance

Your new intelligent assistant application needs governance on an ongoing basis. The more detailed components must be maintained and refreshed. Often the first step unlocks ideas for intelligent assistants in new areas of the organization, so you’ll need careful governance over your evolving EIA to maintain the value of existing solutions. Governance over the content and metadata will be required to ensure ongoing usefulness.