Doug Cornelius .com

Jack Vinson is teaching a class at Northwestern on knowledge management. One of the assignments is flogging (forced blogging).

In his post on the Initial impression of Blogs, one of the key issues his students raise is the privacy factor. Most of students seem to operating under pseudonyms. One even added security (blah blah blog).

A few point out the Wall Street Journal article: How Blogging Can Help You Get a Job. They generally seem to be contemplating what to do with their blog. I assume this stems from the forced nature of the blog.

I found it interesting to compare this experience with Enterprise 2.0 blog issues raised by Andrew McAfee‘s class.

Andrew McAfee‘s MBA class raised a new issue in The Pursuit of Busyness. Would people who use the new Enterprise 2.0 tools (posting to a blog, edit a wiki) be perceived as not spending enough time working?

One of the concerns about most knowledge management projects is that the users will be too busy to contribute. Lawyers are always take the position that they are too busy. In part, because the unit of measurement is the billable hour. The more you work, the more you bill and the more revenue you produce.

I am planning to deploy some Enterprise 2.0 tools in the next version of our intranet using Sharepoint 2007. So I will continue to explore objections to these to tools, as well as the reasons for using them.

I found Stan Garfield‘s description of Knowledge Advisors at HP to be a great definition

“Knowledge advisors perform the following tasks:

1. Help users learn about and use the available people, process, and technology KM components. Provide consulting on processes and tools.
2. Facilitate collaboration. Connect people to others who can help them or whom they can help.
3. Direct users to the right knowledge sources based on their specific needs. Locate relevant knowledge resources.
4. Assist users in searching for content and knowledge. Find reusable content.
5. Actively offer assistance to work teams. Engage by contacting users, not just waiting for requests to arrive.
6. Review content submitted to repositories for compliance to quality standards, and follow up as required to improve quality.
7. Solicit user feedback. Direct feedback to the right person within the KM team.
8. Conduct training. Create and record self-paced courses.
9. Search for information to help meet deadlines. Send search results to users who are not connected to the network.
10. Network with other knowledge advisors. Back each other up. Help respond to requests. Take over open requests at the end of the work day based on being in different time zones.”

Stan also published a report by Knowledge Street “Advisors at Hewlett-Packard: Connecting People with Information” that provides insight to the mix of people, technology and process.

It is the mixing of these three that are the core of a knowledge management project.

Lucas McDonnell updated his list of essential knowledge mangagement sites and blogs.

I updated my KM Sites Search, based on the Google Custom Search to add his updates, and a few of my own.

Using my KM Sites Search, you can search all of those blogs and sites at the same time, and just those sites. The box below will run the search. You can go to full page to see the list of sites an blogs being searched.

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A “precedent” search is a search for a model document.

Generally, the key to finding a good precedent is knowing the context in which a document was previously used, rather than text in the document itself.

An example is: ” a purchase and sale agreement for a retail shopping center in Florida”. “Purchase and Sale Agreement” will be in the text of the document and the name of the document. But “Florida” and “retail shopping center” may not appear in the text of the document. If they do appear, they would appear infrequently.

They key to making a precedent search working is leveraging the document metadata against other systems. For instance, we require users to assign a document to a particular matter. We plan to use that matter identification to pull information from other sources and impute that information on the document.

The other key to a precedent search is using a faceted search to narrow the search results using the additional metadata.

A “research” search is when the user is looking for documents on a topic. The user may not know if any documents on the topic even exist. The search is typically for keywords in the document.

An example is: information on “arms-dealing”.

A user will expect a list of documents displayed by relevancy.

An enterprise search tool excels at this type of search. The user is looking for terms in the document. The enterprise search tool can use its algorithm to identify which documents have the most treatment of the search terms.

A typical DMS will fall short on a “research” search. A typical DMS does not rank searches based on relevancy. If a search yielded dozens or more results, the user would have no reference as to where to start a review of search results. A typical DMS also has an inferior text search engine.

It is the frustration when running a “research” search that users cry out for an enterprise search tool.

A “recall” search is when the user knows the document exists and has some specific information about the document that the user can distinguish it from other documents.

Examples are: documents edited in the last five days, all the documents for a particular matter, all of the purchase agreement for a client.

The user will expect a a list of documents that will be over-inclusive, but the list will have information to distinguish the particular document the user is looking for from the rest of the documents.

A DMS excels at this type of search and is core functionality for a DMS. The enterprise search will generally not perform well at this type of search. For the DMS search to be successful, user input is required to make sure the metadata/profile of the document is accurate.

A “fetch” search is when the user has a document ID (with a Document Management System) or a file name (with a file server system).

The user would expect the single document to be returned. There should be no need for relevancy rankings.

The “fetch” search is the most basic of the four types of searches. It is such a basic part of a DMS and works so well in a DMS that most users do not even think of it as a search. Nonetheless, it is the most common search and the most important. A user expects to be able to get a specific document back instantly for editing or reuse, without having to interpret search results.

A “fetch” is core functionality of a DMS. An enterprise search tool would fall short in this type of search. The DMS is keyed to find specific metadata from the document profile. The enterprise search tool typically uses the metadata to influence the relevancy rankings of a particular document.

In reviewing user behavior, I have identified four different types of searches for documents:

• Fetch
• Recall
• Research
• Precedent

Separate posts will follow with more information on each type of search and user expectations for search results. The posts will also discuss how well a document management system (DMS) or enterprise search tool will handle the different types of searches