Enterprises need to measure the findability of information in their internal search solutions. When it's easier for employees to find needed information on the corporate intranet, increased employee productivity results. Knowledge Center contributor Sid Probstein explains how to measure findability, and offers five steps to building a ROI case for your company's internal search solution.
The
results of a recent survey on the findability of information within the
enterprise are not encouraging. Roughly half of the responding
knowledge workers stated that finding important information was
difficult and time-consuming, and that the internal search capabilities
provided by their company were "worse" to "much worse" than the
equivalent functionality offered to consumers.
Neither of these findings is truly surprising. Corporate Internet
sites tend to be directly involved in important and easily measured
activities: selling products to new customers (generating revenues) or
servicing existing customers (reducing costs). Consequently, Internet
search is usually well-funded, staffed and tends to be more successful.
Internal search, in contrast, is concerned with productivity-a fuzzier
concept that is much harder to measure. Significant investment (and
thus success) is therefore harder to achieve.
What is surprising is that roughly half of the survey respondents
stated that their company had no formal goal for internal findability.
In my view, this is a direct cause for the overall poor results.
Companies that don't measure search won't be able to invest
appropriately, let alone tune and improve a complicated system with
which they likely don't have deep internal experience.
If your organization doesn't measure search, or more precisely,
findability, you should start right away. Here are five steps to
building a return on investment case for your company's internal search
or information access solution. Keep in mind that no massive investment
is required upfront. In fact, one person working a few hours a week can
make a difference.
Step #1: Talk to your system administration team
For starters, find out if your organization is saving query
logs-hopefully they are. If not, this is the first challenge to
overcome. Talk to your system administration team and see if they can
help. You don't need to save all logs for all time; just try to get
your hands on a day or two of data. That's quite enough to get started.
Step #2: Identify and run a handful of queries
Assuming you have some data to look at, identify a handful (less
than 50) of interesting queries. Ideally, you want them to fall into a
few different categories: one-word, two-word, multiple words,
questions, and a few different business domains.
Run the queries yourself and see what comes up. Look at the first
few results and score them on a simple scale (such as 1=incoherent,
5=perfect). If you rate a result poorly, spend a few minutes trying to
find out what the better answer might be. Then see if you can infer
what's wrong.
For example, does the document that answers your question (but
doesn't appear in the results list) contain the terms you put in your
query? If it does, you have a relevancy problem; if not, you have
some sort of linguistic problem.
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