A case study in Wikis, Facets and KM

It has been a while since I posted on my blog. I have decided to write again to share some of the interesting topics I have come across. I recently read a very interesting write up from Pete Bell from Endeca published on KM World: Acmepedia: a case study in Wikis, Facets and KM. Pete makes some very interesting observations.

Regarding the key differences between Wikis and traditional KM:

• Wikis are an addition to KM, not a rip-and-replace. They will become yet another silo if they’re not designed to complement your existing enterprise packaged applications.
• Faceted navigation and information access can be the key to crossing different content silos.
• Although facets are part of the solution, they also introduce new requirements: how do you categorize everything so it can be found again through faceted navigation? With enhancements, tagging and folksonomy provide an answer.
• Authority and trust impose different constraints in the enterprise than in the public Wikipedia. Facets offer dramatic changes here too.

Best practices for Wikis:

• “Curated” content: Only a utopian would expect white papers, legal documents and HR forms to be produced by the proverbial million monkeys banging on a wiki. These would continue to live in a CMS, complete with version control, workflows and the like.
• Packaged applications: Data and notes from CRM, HRM and ERP packaged applications were some of our most valuable content. We would sensibly leave it where it was, but use information access to integrate it.
• Wikis and blogs: These group collaborations proved best at capturing the conversational nature of emerging topics, discussions, threads, opinions, ephemera and niches. And they required some business
process changes to align them with communities of practice.

Folksonomy and Taxonomy:

In our own experimentation, we found that the purists’ folksonomy can be amended very successfully with a pragmatic approach. It can succeed by blending elements of tops-down and bottoms-up organization. Key techniques:
• Controlled vocabulary: instead of free-form document tagging, first prompt users to select common terms from a controlled vocabulary, like names of industries, products, customers and geographies.
• Enter tags in facets: Instead of prompting for tags in a single field, offer faceted fields—again, like industries, products, etc. The name of the facet itself adds valuable structure.
• Auto-complete terms: Prevent vocabulary drift by using a type-ahead search to suggest known terms as the user types.
• Put an editor in the workflow queue: Actively prevent vocabulary drift with an expert. It takes less effort than you’d expect. You won’t catch everything, but you can add common synonyms and hesaurus
terms, and promote frequently used terms to the controlled vocabulary.
• Auto-tag: Supplement user tagging with some based on rules, like tags derived from an author’s department or LDAP profile.

Internet Users and Tagging

The Pew Internet & American Life Project just published a report on tagging. Some very interesting findings from the report:

• A December 2006 survey by the Pew Internet & American Life Project has found that 28% of internet users have tagged or categorized content online such as photos, news stories or blog posts. On a typical day online, 7% of internet users say they tag or categorize online content.
• Taggers look like classic early adopters of technology. They are more likely to be under age 40, and have higher levels of education and income.
• Taggers are considerably more likely to have broadband connections at home, rather than dial-up connections. Men and women are equally likely to be taggers, while online minorities are a bit more likely than whites to be taggers.
• The act of tagging is likely to be embraced by a more mainstream population in the future because many organizations are making it easier and easier to tag internet content.

More details on the demographics of taggers are provided below:


In his upcoming book, Everything Is Miscellaneous: The Power of the New Digital Disorder,
Weinberger describes how radical it is for people to move away from hierarchical classifications. His prediction:

We'll also undoubtedly figure out how to intersect tags with social networks, so that the tags created by people we know and respect have more “weight” when we search for tagged items. In fact, by analyzing how various social groups use tags, we can do better at understanding how seemingly different worldviews map to one another.

The Cost of Ineffective Search

Great article in Network World on the Cost of Ineffective Search. The author starts with a strong punch line:

A company that employs 1,000 information workers can expect more than $5 million in annual salary costs to go down the drain because of the time wasted looking for information and not finding it, IDC research found last year.

You think that's bad. think again:

A survey conducted by Accenture this month of 1,000 middle managers found that more than half of the information they find during searches is useless.

• The Butler Group estimates that 10% of a company's salary are wasted on ineffective searches.* Susan Feldman at IDC found that 3.5 hours each week are wasted on search that don't turn up the right information.
• People spend about 9-10 hours a week on average looking for information and don't find the information they are looking for 1/3 to 1/2 of the time.

So why is search so ineffective?

1. Enterprises are not investing much in search
Most companies are under-investing in search compared to other systems such as portals, dashboards, databases and other enterprise systems and are not using the latest search technology. The best search applications use concept searches and very few companies have adopted such technology.

2. 90% of the documents that are created have no useful metadata
Companies for the past 10 years have found it extremely difficult to get employees to add metadata to content.

3. The right data sources are not getting indexed
Companies need to figure out what search is used for (customer service, eDiscovery, etc.) and adapt their search strategy.

The Answer?
The semantic web is viewed as an answer to ineffective search. Recent research from MIT Sloan shows that using semantic search technology will turn up most desired results about 80% of the time compared to 50% of the time with search technology used by most companies today.

Improved metadata through a non-intrusive tagging process will definitely contribute to improved search as well, further indicating the value of social tagging.