Tag Archives: search solution

Enterprise search case study: Vårdaktörsportalen makes reliable information easy to find for health professionals

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Vårdaktörsportalen (VAP) is a portal for health care providers made by Västra Götalandsregionen (VGR). This portal makes information from a number of reliable and authorised sources findable and accessible for the people who need it in their daily work. This stretches from doctors and nurses to medical secretaries primarily located in the region of Västra Götaland, Sweden. The site and most of its features and information is also accesible (in Swedish) to anyone through http://vap.vgregion.se. In November 2012 the first version of this site went live and Findwise had a big role in the creation of this search centric site. The main source of information for VAP is the regional guidelines found in a document repository within VGR but some other external sources are also included. These include trustworthy authorities like

  • Socialstyrelsen
  • Läkemedelsverket
  • SBU
  • TLV
  • 1177 – the public health care information site for citizens

This search solution is built around the open source search engine Apache Solr and our common tools for processing and indexing. For this site we have also implemented a rather unique metadata enhancement service that automatically extracts keywords from the document to index and attaches it as metadata. The keyword extraction is based on information from the medical term database SweMeSH. More information (in Swedish) can be found on Google Code. We also include synonyms to keywords to increase recall, making it easier to find documents regardless of what synonym used.

The metadata enhancement service was included because the quality of metadata on the external sites was not very good. VGR will work with the above mentioned authorities to try and make them understand that they would benefit if they improved their data. The source 1177 stands out with very good meta data and overall good quality texts.

We have conducted a user study to see how this first version is able to satisfy user demands. The result of that study shows that some local sources are missing, but a general positive feedback on the idea and the graphical design was collected.

Findwise is looking forward to continue working on VAP with VGR in the future to make it an even better tool.

Related links:

Enterprise Search and Findability discussions at World Cafe in Oslo

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Yesterday we (Kristian Hjelseth and Kristian Norling) participated in a great World Cafe event arranged by Steria in Norway. We did a Pecha Kucha inspired presentation (scroll down to the bottom of this blog post for the presentation) to introduce the subject of Enterprise Search and Findability and how to work more efficiently with the help of enterprise search. Afterwards there was a set of three round-table workshop with practitioners, where search related issues were discussed. We found the discussions very interesting, so we thought we should share some of the topics with a broader audience.

The attendees had answered a survey before coming to the World Cafe. In which 83,3% stated that finding the right information was critical for their business goals. But only 20,3% were satisfied with their current search solution, because 75% said it was hard or very hard to find the right information. More stats from a global survey on enterprise search that asked the same questions.

Unified Search

To have all the information that you would like to find in the same search was deemed very important for findability by the participants. The experience of search is that the users don’t know what to search for, but to make it even worse, they do not know where to look for the information! This is also confirmed by the Enterprise Search and Findability Survey that was done earlier this year. The report is available for download.

Trust

Google web search always comes up as an example of what “just works”. And it does work because they found a clever algorithm, PageRank, that basically measures the trustworthiness of information. Since PageRank is heavily dependent on inbound links this way of measuring trust is probably not going to work on an intranet where cross-referencing is not as common based on our experience. Most of the time it is not even possible to link stuff on the intranet, since the information is not accessible through http. Read more about it in this great in-depth article series on the difference between web search and enterprise search by Mark Bennet.

So how can we make search inside the firewall as good as web search? I think by connecting the information to the author. Trust builds between people based on their views of others. Simply put, someone has the authority over her peers either through rank (=organisation chart) or through trust. The trustworthiness can be based on the persons ability to connect to other people (we all probably know someone who knows “everyone”) or we trust someone based on the persons knowledge. More reading on the importance of trust in organisations. How to do this in practice? Some ideas in this post by BIll Ives. Also a good read: “How social is Enterprise Search?” by Jed Cawthorne. And finally another good post to read.

Metadata

By adding relevant metadata to information, we can make it more findable. There was discussions on the importance of strict and controlled metadata and how to handle user tagging. For an idea on how to think about metadata, read a blog post on how VGR used metadata by Kristian Norling.

Search Analytics

Before you start to do any major work with your current enterprise search solution, look at the search log files and analyze the data. You might be surprised in what you find. Search analytics is great if you want insight into what the user expects to find when they search. Watch this video for an introduction to Search Analytics in Practice.

Other subjects

  • Access control and transparency
  • Who owns search?
  • Who owns the information?
  • Personalization of search results
All these subjects and many more were discussed at the workshops, but that will have to wait for another blog post!
As always, your thoughts and comments are most welcome!

If a Piece of Content is Never Read, Does it Really Exist?

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Since ancient times, information technology has developed from carvings in rock and wood to cell phones and Facebook. Still, the basic purpose remains the same; to facilitate communication between people separated by space and time. Therefore one can measure the successfulness of any information tool by two axes: how easy it is to create information and how easy it is to consume it. Being a Findability expert, I spend a large part of my life focusing on the latter. Therefore it troubles me that so many organizations wait so long when they are introducing new content management systems before looking at search. If I had a nickel for every time I heard “we are currently busy with building our new intranet/web page/collaboration tool and will look at search when the project is finished” I would definitely have had a few quarters by now.

I like to say that I am in the information marketing business. What I mean by that is that Findability is all about marketing information so that the consumers, your employees, can find the piece of information they need. And just as an industrialist would not construct a factory before doing a marketing plan, you should not build a new information repository without thinking about how the content created in that repository will reach its target audience. When marketing information, search is one of your most important channels.

While a enterprise search solution can definitely smooth out imperfections in information structure and quality using intelligent algorithms, spending a little time thinking about how you can make it easier for a search engine to deliver relevant results presented in a user friendly way can really make it shine. Some questions you can ask yourself are:

  • How can we make tagging so convenient that we have good metadata for presenting and filtering results using facets? Many search solutions have automated tagging functionality to take load off users.
  • How can we use search as an integration platform to pull in content from other sources instead of making costly one-time integrations?
  • How will the new information repository fit into an existing search solution, for example are we changing the metadata model and how should the documents be ranked compared to other sources?
  • Should we migrate content from an old system to the new one or just freeze information creation in the old one and have a search box that let’s the user find information from both?
  • Can we use search to avoid creating duplicate information by encouraging users to make searches before typing new content or even doing implicit searches while the user is typing?

So does a piece of content that no one ever reads exist? Well in terms of bits on a disk in a data center, yes, but in terms of business value definitely no. Designing your information repository for Findability will have great returns in improved efficiency and user satisfaction.

KMWorld 2010 Reflections: Search is a Journey Not a Destination

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Two weeks ago me, Ludvig Johansson and Christopher Wallström attended KMWorlds quadruple conference in Washington D.C. The conference consisted of four different conferences; KMWorld, Enterprise Search Summit, Taxonomy Bootcamp and SharePoint Symposium. I focused on Enterprise Search Summit and SharePoint Symposium and Christopher mainly covered Taxonomy Bootcamp as well as the Enterprise Search Summit. (Christopher will soon write a blog post about this as well.)

During the conferences there where some good quality content, however most of it was old news with speakers mainly focusing on outputs of their own products. This was disappointing since I had hoped to see the newest and coolest solutions within my area. Speakers presented systems from their corporations, where the newest and coolest functionality they described was shallow filters on a Google Search Appliance. From my perspective this is not new or cool. I would rather consider this standard functionality in today’s search solutions.

However, some sessions where really good. Daniel W. Rasmus talked about the Evolution of Search in quite a fun and thoughtful way. One thing he wanted to see in the near future was more personalization of search. Search needs to know the user and adapt to him/her and not simply use a standardized algorithm. As Rasmus sad it: “my search engine is not that in to me”. This is, as I would put it, spot on how we see it at Findwise. Today’s customer wants standard search with components that have existed for years now. It’s time for search to take the next step in the evolution and for us to start deliver Findabillity solutions adapted to your needs as an individual. In the line of this, Rasmus ended with another good quote: “Don’t let your search vendors set your exceptions to low”. I think this speaks for it self more or less. If we want contextual search then we should push the vendors out there to start deliver!

Another good session was delivered by Ellen Feaheny on how to utilize both old and new systems smarter. It was from this session the title of this post origins, “It’s a journey not a destination”. I thought this sums up what we feel everyday in our projects. It’s common that customers want to see projects to have a clear start and end. However with search and Findability we see it as a journey. I can even go as far to say it’s a journey without an end. We have customers coming and complaining about their search; saying “It doesn’t work anymore” or “The content is old”, to give two examples. The problem is that search is not a one time problem that you solve and then never have to think about again. If you don’t work with your search solution and treat search as a journey, continually improve relevance, content and invest time in search analytics your solution will soon get dusty and not deliver what your employees or customers wants.

Search is a journey not a destination.

OmniFind Enterprise Edition 9.1 – New Capabilities Discussed Over Breakfast

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During the last year a number of interesting things has happened to IBM’s search platform and the new version, OmniFind 9.1, was released this summer. Apart from a large number of improvements in the interface, the change to basing the new solution on open source (Lucene) has proven to be a genius by-pass of some of OmniFinds previous shortcomings.

The licensing model is still quite complicated, something Stephen E Arnold highlighted earlier this year. Since a number of our customers have chosen to take a closer look at OmniFind as a search solution we decided to host a breakfast seminar together with IBM last Thursday, in order to discuss the new features and show how some of our customer are working with it.

Without a doubt, the most interesting part is always to discuss how the solution can be utilized for intranets, extranets, external sites and e-business purposes.

Apart from this, we also took a look at some of the new features:
Type ahead (query suggestion), based on either search statistics or indexed content

Type ahead

Faceted search i.e. the ability to filter on dates, locations, format etc as well as numeric and date range. The later is of course widely used within e-business.

Facets for e-business

Thumbnail views of documents (yes, exactly what it sounds like: a thumbnail view for first page of documents in results page)

Thumbnail of a document

Search analytics in OmniFind 9.1 holds a number of interesting statistic capabilities. Some things worth mentioning is number of queries, query popularity, number of users, average response time (ms) and worst response time (ms).

Save searches (to be able to go back and see if new information has been included), search within result sets (to further narrow your result set within a given result set) and did-you-mean functionality (spell checking) are also included.

..and improvements on the administrator side, just to mention a few:

  • Ability to change the relevancy i.e. to adjust and give certain types of information higher ranking
  • Support for incremental indexing i.e. to only re-index the information that is new or changed since the last time you made it searchable

To conclude: IBM is making a whole lot of improvements in the new version, which are worth taking a closer look at. During the spring we are running upgrading projects for some of our customers, and we will keep you up-to-date with the different application areas OmniFind Enterprise Edition 9.1 is being used for. Please let us know if you have any particular questions or have areas that you are interested in.

Enterprise Search and Business Intelligence?

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Business Intelligence (BI) and Enterprise Search is a never ending story

A number of years ago Gartner coined “Biggle” – which was an expression for BI meeting Google. Back then a number of BI vendors, among them Cognos and SAS, claimed that they were working with enterprise search strategically (e.g. became Google One-box partners). Search vendors, like FAST, Autonomy and IBM also started to cooperate with companies such as Cognos. “The Adaptive Warehouse” and “BI for the masses” soon became buzzwords that spread in the industry.

The skeptics claimed that enterprise search never would be good at numbers and that BI would never be good with text.

Since then a lot a lot has happened and today the major vendors within Enterprise Search all claim to have BI solutions that can be fully integrated (and the other way around – BI solutions that can integrate with enterprise search).

The aim is the same now as back then:  to provide unified access to both structured (database) and unstructured (content) corporate information. As FAST wrote in a number of ‘Special Focus’:

“Users should have access to a wide variety of data from just one, simple search interface, covering reports, analysis, scorecards, dashboards and other information from the BI side, along with documents, e-mail and other forms of unstructured information.”

And of course, this seems appealing to customers. But does access to all information really make us more likely to take the right decisions in terms of Business Intelligence. Gartner is in doubt.

Nigel Rayner, research vice president at Gartner Inc, says that:

”The problem isn’t that they (users) don’t have access to information or tools; they already have too much information, and that’s just in the structured BI world. Now you want to couple it with unstructured data? That’s a whole load of garbage coming from the outside world”.

But he also states that search can be used as one part of BI:

“Part of the problem with traditional BI is that it’s very focused on structured information. Search can help with getting access to the vast amount of structured information you have”

Looking at the discussions going on in forums, in blogs and in the research domain most people seem to agree with Gartner’s view: enterprise search and business intelligence makes a powerful combination, but the integrations needs to be made with a number of things in mind:

Data quality

As mentioned before, if one wants to make unstructured and structured information available as a complement to BI it needs to be of a good quality. Knowing that the information found is the latest copy and written by someone with knowledge of the area is essential. Bad information quality is a threat to an Enterprise Search solution, to a combined BI- and search solution it can be devastating. Having Content Lifecycles in place (reviewing, deleting, archiving etc) is a fundamental prerequisite.

Data analysis

Business Intelligence in traditionally built on pre-thought ideas of what data the users need, whereas search gives access to all information in an ad-hoc manner. To combine these two requires a structured way of analyzing the data. If the unstructured information is taken out of its context there is a risk that decisions are built on assumptions and not fact.

BI for the masses?

The old buzzwords are still alive, but the question mark remains. If one wants to give everyone access to BI-data it has to be clear what the purpose is. Giving people a context, for example combining the latest sales statistics with searches for information about the ongoing marketing activities serves a purpose and improves findability. Just making numbers available does not.

enterprise search and business intelligence dashboard

Business intelligence and enterprise search in a combined dashboard – vision or reality within a near future?

So, to conclude: Gartner’s vision of “Biggle” is not yet fulfilled. There are a number of interesting opportunities for the business to create findability solutions that combines business intelligence and enterprise search, but the strategies for adopting it needs to be developed in order to create the really interesting cases.

Have you come across any successful enterprise search and business intelligence integrations? What is your vision? Do you think the integration between the two is a likely scenario?

Please let us know by posting your comments.

It’s soon time for us to go on summer vacation.

If you are Swedish, Nicklas Lundblad from Google had an interesting program about search (Sommar i P1) the other day, which is available as a podcast.

Have a nice summer all of you!

Relevance is Important – and Relevant

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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.

Search and Accessibility

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Västra Götalands regionen has introduced a new search solution that Findwise created together with Netrelations. Where both search and accessibility is important. We have also blogged about it earlier (see How to create better search – VGR leads the way). One important part of the creation of this solution was to create an interface that is accessible to everyone.

Today the web offers access to information and interaction for people around the world. But many sites today have barriers that make it difficult, and sometimes even impossible for people with different disabilities to navigate and interact with the site. It is important to design for accessibility  – so that no one is excluded because of their disabilities.

Web accessibility means that people with disabilities can perceive, understand, navigate, interact and contribute to the Web. But web accessibility is not only for people that use screen readers, as is often portrayed. It is also for people with just poor eyesight who need to increase the text size or for people with cognitive disabilities (or sometimes even for those without disabilities). Web accessibility can benefit people without disabilities, such as when using a slow Internet connection, using a mobile phone to access the web or when someone is having a broken arm. Even such a thing as using a web browser without javascript because of company policy can be a disability on the web and should be considered when designing websites.

So how do you build accessible websites?

One of the easiest things is to make sure that the xhtml validates. This means that the code is correct, adheres to the latest standard from W3C (World Wide Web Consortium) and that the code is semantically correct i.e. that the different parts of the website use the correct html ”tags” and in the correct context. For example that the most important heading of a page is marked up with ”h1” and that the second most important is ”h2” (among other things important when making websites accessible for people using screen readers).

It is also important that a site can easily be navigated only by keyboard, so that people who cannot use a mouse still can access the site. Here it is important to test in which order the different elements of the web page is selected when using the keyboard to navigate through the page. One thing that is often overlooked is that a site often is inaccessible for people with cognitive disabilities because the site contains content that uses complex words, sentences or structure. By making content less complex and more structured it  will be readable for everyone.

Examples from VGR

In the search application at VGR elements in the interface that use javascript will only be shown if the user has a browser with java script enabled. This will remove any situations where elements do not do anything because java script is turned off. The interface will still be usable, but you will not get all functionality. The VGR search solution also works well with only the keyboard, and there is a handy link that takes the user directly to the results. This way the user can skip unwanted information and navigation.

How is accessibility related to findability?

http://www.flickr.com/photos/morville/4274260576/in/set-72157623208480316/

Search and Accessibility

Accessibility is important for findability because it is about making search solutions accessible and usable for everyone. The need to find information is not less important if you are blind,  if you have a broken arm or if you have dyslexia. If you cannot use a search interface you cannot find the information you need.

“what you find changes who you become” -Peter Morville

In his book Search Patterns Peter Morville visualizes this in the ”user experience honeycomb”. As can been seen in the picture accessibility is as much a part of the user experience as usability or findability is and a search solution will be less usable without any of them.

How to Create Better Search – VGR leads the way

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I realise we are a bit late. Fredrik Wackå, a senior IT-strategist, has already written an excellent article on his blog (in Swedish). He has, among other things, been interviewing Kristian Norling (at Twitter), who has been working with portal strategies and better search for many years at Region Västra Götaland. Although, for all our non-Swedish speaking guests here is a short summary:

Findwise has during the last few months been working on a new search solution for Region Västra Götaland. The two main goals have been to deliver a search experience that seems both fast and accurate. The result? Today making a search at VGR takes about 0,1-0,2 seconds, faster than a Google search on the web.

Furthermore, there was a need for context. Large amount of information requires ways to filter and sort – otherwise the users will drown in the result list. By giving the end-users the ability to sort the search result the users can look for general information within an area as well as quickly narrow down to a specific piece (for example by two clicks be able to see only the PDF-files created in 2009). The filters (and thereby metadata standard) includes:

  • Information type
  • Where the document resides
  • Where it belongs in the organization
  • What source it has
  • When it was last changed
  • Who has written it
  • What format it resides in
  • Keywords that has been created

The search solution also includes a metadata service. As so many others VGR has been struggling with getting the metadata in place.

Apart from the metadata supported by the system (where Dublin Core is being used) the metadata service is doing two things:

  • Analyses the content in the text, compares it to taxonomy and gives the writer suggestions of keywords that he/she can use
  • Gives the writer the ability to add additional keywords

Apart from this the end-users will be able to add etiquettes (tags). These will be compared with two lists. If the tags appears in the “white list” it will be published right away, if they are in the “blacklist” they will be deleted. Anything inbetween are controlled before they are published.

To conclude: a lot of effort has been put into creating a good search experience and VGR continues to deliver functionality and solutions that are light-years ahead of many others. The combination of supporting systems and using the “collected intelligence” of the writers and end-users will make it even better over time. Search is about both supporting systems, content and people.

Read more in Fredrik Wackås blog

Google Search Appliance Learns What You Want to Find

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Analyzing user behaviour is a key ingredient to make a search solutions successful. By using Search Analytics, you gain knowledge of how your users use the search solution and what they expect to find. With this knowledge, simple adjustments such as Key Matches, Synonyms and Query Suggestion can enhance the findability of your search solution.

In addition to this, you can also tune the relevancy by knowing what your users are after. An exciting field in this area is to automate this task, i.e by analyzing what users click on in the search result, the relevancy of the documents it automatically adjusted. Findwise has been looking into this area lately, but there hasn’t been any out-of-the-box functionality for this from any vendor.

Until now.

Two weeks ago Google announced the second major upgrade this year for the Google Search Appliance. Labeled as version 6.2, it brings a lot of new features. The most interesting and innovative one is the Self-Learning Scorer. The self learning scorer analyzes user’s click and behaviour in the search result and use it as input to adjust the relevancy. This means that if a lot of people clicks on the third result, the GSA will boost this document to appear higher up in the result set. So, without you having to do anything, the relevance will increase over time making your Search Solution perform better the more it is used. It’s easy to imagine this will create an upward spiral.

The 6.2 release also delivers improvements regarding security, connectivity, indexing overview and more. To read more about the release, head over to the Google Enterprise Blog.