Update on The Enterprise Search and Findability Survey

A quick update on the status of the Enterprise Search survey.

We now have well over a hundred respondents. The more respondents the better the data will be, so please help spreading the word. We’d love to have  several hundred more. The survey will now be open until the end of April.

But most important of all, if you haven’t already, have a cup of coffee and fill in the survey.

A Few Results from the Survey about Enterprise Search

More than 60% say that the amount of searchable content in their organizations today are less or far less than needed. And in three years time 85% say that the amount of searchable content in the organisation will increase och increase significantly.

75% say that it is critical to find the right information to support their organizations business goals and success. But the interesting to note is that over 70% of the respondents say that users don’t know where to find the right information or what to look for – and about 50% of the respondents say that it is not possible to search more than one source of information from a single search query.

In this context it is interesting that the primary goal for using search in organisations (where the answer is imperative or signifact) is to:

  • Improve re-use of information and/or knowledge) – 59%
  • Accelerate brokering of people and/or expertise – 55%
  • Increase collaboration – 60%
  • Raise awareness of “What We Know” – 57%
  • and finally to eliminate siloed repositories – 59%

In many organisations search is owned either by IT (60%) or Communication (27%), search has no specified budget (38%) and has less than 1 dedicated person working with search (48%).  More than 50% have a search strategy in place or are planning to have one in 2012/13.

These numbers I think are interesting, but definitely need to be segmented and analyzed further. That will of course be done in the report which is due to be ready in June.

Enterprise Search Stuffed up with GIS

When I browsed through marketing brochures of GIS (Geographic Information System) vendors I noticed that the message is quite similar to search analytics. It refers in general to integration of various separate sources into analysis based on geo-visualizations. I have recently seen quite nice and powerful combination of enterprise search and GIS technologies and so I would like to describe it a little bit. Let us start from the basic things.

Search result visualization

It is quite obvious to use a map instead of simple list of results to visualize what was returned for an entered query. This technique is frequently used for plenty of online search applications especially in directory services like yellow pages or real estate web sites. The list of things that are required to do this is pretty short:

– geoloalization of items  – it means to assign accurate geo coordinates to location names, addresses, zip codes or whatever expected to be shown in the map; geo localization services are given more less for free by Google or Bing maps.

– backgroud map – this is necessity and also given by Google or Bing; there are also plenty of vendors for more specialized mapping applications

– returned results with geo-coordinates  as metadata – to put them in the map

Normally this kind of basic GIS visualisation delivers basic map operations like zooming, panning, different views and additionally some more data like traffic, parks, shops etc. Results are usually pins [Bing] or drops [Google].

Querying / filtering with the map

The step further of integration between search and GIS would be utilizing the map as a tool for definition of search query. One way is to create area of interest that could be drawn in the map as circle, rectangle or polygon. In simple way it could be just the current window view on the map as the area of query. In such an approach full text query is refined to include only results belonging to area defined.

Apart from map all other query refinement tools should be available as well, like date-time sliders or any kind of navigation and fielded queries.

Simple geo-spatial analysis

Sometimes it is important to sort query results by distance from a reference point in order to see all the nearest Chinese restaurant in the neighborhood.  I would also categorize as simple geo-spatial analysis grouping of search result into a GIS layers like e.g. density heatmap, hot spots using geographical and other information stored in results metadata etc.

Advanced geo-spatial analysis

More advance query definition and refinement would involve geo-spatial computations. Basing on real needs it could be possible for example to refine search results by an area of sight line from a picked reference point or select filtering areas like those inside specific borders of cities, districts, countries etc.

So the idea is to use relevant output from advanced GIS analysis as an input for query refinement. In this way all the power of GIS can be used to get to the unstructured data through a search process.

What kind of applications do you think could get advantage of search stuffed with really advanced GIS? Looking forward to your comments on this post.

Why Web Search is Like a Store Clerk

When someone is using the search function on your web site, your web search, it tells you two things. First of all they have a specific need, expressed by their search query. Second, and more importantly he or she wants you to fulfill that need. If users didn’t care where the service was delivered from, they would have gone straight to Google. Hence, the use of your search function signals trust in your capabilities. This means that even if the majority of your website visitors doesn’t use the search function, you know that the ones who do have a commitment to you. Imagine you are working in a store as a clerk; the customer coming up to you and asking you something is probably more interested in doing business with you than the ones just browsing the goods.

This trust however, can easily be turned to frustration and bad will if the web search result is poor and users don’t find what they are looking for. Continuing our analogy with the store, this is much like the experience of looking for a product, wandering around for a few minutes, finally deciding to ask a clerk and getting the answer “If it’s not on the shelf we don’t have it”. I certainly would leave the store and the same applies for a web site. If users fail when browsing and searching, then they will probably leave your site. The consequence is that you might antagonize loyal customers or loose an easy sale. So how do you recognize a bad search function? A good way to start is to look at common search queries and try searching for them yourself. Then start asking a few basic questions such as:

  • Does the sorting of the search results make sense?
  • Is it possible to decide which result is interesting based on the information in the result presentation?
  • Is there any possibility to continue navigating the results if the top hits are not what you are looking for?

Answering these questions yourself will tell you a lot about how your web search is performing. The first step to a good user experience is to know where your challenges are, then you can start making changes to improve the issues you have found in order to make your customers happier. After all, who wants to be the snarky store clerk?

Bridging the Gap Between People and (Enterprise Search) Technology

Tony Russell-Rose recently wrote about the changing face of search, a post that summed up the discussion about the future of enterprise search that took part at the recent search solutions conference. This is indeed an interesting topic. My colleague Ludvig also touched on this topic in his recent post where he expressed his disappointment in the lack of visionary presentations at this year’s KMWorld conference.

At our last monthly staff meeting we had a visit from Dick Stenmark, associate professor of Informatics at the Department of Applied IT at Gothenburg University. He spoke about his view on the intranets of the future. One of the things he talked about was the big gap in between the user’s vague representation of her information need (e.g. the search query) and the representation of the documents indexed by the intranet enterprise search engine. If a user has a hard time defining what it is she is looking for it will of course be very hard for the search engine to interpret the query and deliver relevant results. What is needed, according to Dick Stenmark, is a way to bridge the gap between technology (the search engine) and people (the users of the search engine).

As I see it there are two ways you can bridge this gap:

  1. Help users become better searchers
  2. Customize search solutions to fit the needs of different user groups

Helping users become better searchers

I have mentioned this topic in one of my earlier posts. Users are not good at describing which information they are seeking, so it is important that we make sure the search solutions help them do so. Already existing functionalities, such as query completion and related searches, can help users create and use better queries.

Query completion often includes common search terms, but what if we did combine them with the search terms we would have wanted them to search for? This requires that you learn something about your users and their information needs. If you do take the time to learn about this it is possible to create suggestions that will help the user not only spell correctly, but also to create a more specific query. Some search solutions (such as homedepot.com) also uses a sort of query disambiguation, where the user’s search returns not only results, but a list of matching categories (where the user is asked to choose which category of products her search term belongs). This helps the search engine return not only the correct set of results, but also display the most relevant set of facets for that product category. Likewise, Google displays a list of related searches at the bottom of the search results list.

These are some examples of functionalities that can help users become better searchers. If you want to learn some more have a look at Dan Russells presentation linked from my previous post.

Customize search solutions to fit the needs of different user groups

One of the things Dick Stenmark talked about in his presentation for us at Findwise was how different users’ behavior is when it comes to searching for information. Users both have different information needs and also different ways of searching for information. However, when it comes to designing the experience of finding information most companies still try to achieve a one size fits all solution. A public website can maybe get by supporting 90% of its visitors but an intranet that only supports part of the employees is a failure. Still very few companies work with personalizing the search applications for their different user groups. (Some don’t even seem to care that they have different user groups and therefore treat all their users as one and the same.) The search engine needs to know and care more about its’ users in order to deliver better results and a better search experience as a whole. For search to be really useful personalization in some form is a must, and I think and hope we will see more of this in the future.

Relevance is Important – and Relevant

A couple of weeks ago I read an interesting blog post about comparing the relevance of three different search engines. This made me start thinking of relevance and how it’s sometimes overlooked when choosing or implementing a search engine in a findability solution. Sometimes a big misconception is that if we just install a search engine we will get splendid search results out of the box. While it’s true that the results will be better than an existing database based search solution, the amount of configuration needed to get splendid results is based on how good relevance you get from the start. And as seen in the blog post, it can be quite a bit of different between search engines and relevance is important.

So what is relevance and why does it differ between search engines? Computing relevance is the core of a search engine. Essentially the target is to deliver the most relevant set of results with regards to your search query. When you submit your query, the search engine is using a number of algorithms to find, within all indexed content, the documents or pages that best corresponds to the query. Each search engine uses it’s own set of algorithms and that is why we get different results.

Since the relevance is based on the content it will also differ from company to company. That’s why we can’t say that one search engine has better relevance than the other. We can just say that it differs. To know who performs the best, you have to try it out on your own content. The best way to choose a search engine for your findability solution would thus be to compare a couple and see which yields the best results. After comparing the results, the next step would then be to look at how easy it is to tune the relevance algorithms, to what extent it is possible and how much you need to tune. Based on how good relevance you get from the start you might not need to do much relevance tuning, thus you don’t need the “advanced relevance tuning functionality” that might cost extra money.

In the end, the best search engine is not the one with most functionality. The best one is the one that gives you the most relevant results, and by choosing a search engine with good relevance for your content some initial requirements might be obsolete which will save you time and money.