Enterprise search differs from web search in some fundamental ways. But there are also similarities. Since we all know how successful web search is, let’s see if there is something to learn by examining the differences.

The most striking similarity is that both solutions involve content produced by a user.

The main differences are the search engine and who controls it, the different types of content sources, and the use of the pagerank algorithm.

Web content and web search

What makes web search so successful?  Web search was revolutionized when Google introduced their web search using the pagerank algorithm. Pagerank uses the natural structure of the world wide web, and assigns high weight to pages with many incoming links. It rests on the assumption that pages with correct and important information will be used as references on other pages. Along with pagerank, there is a large number of other factors used to drive relevancy; content quality, keywords, social media sharing etc. Most of the details are not publicly available.

Content publishers on the world wide web can use the tricks of Search Engine Optimization (SEO) to make sure their content gets optimal visibility on the web. SEO is the art of combining knowledge of two things;

- how web search engines work

- what search terms people use.

Both of these areas involve a lot of guessing. Some information can be found in guides such as the Google Webmaster Guidelines, which explains what a webmaster can do to make sure her web sites are properly indexed. Parts of this read almost like an instruction on how to create a nice school paper: “Create a useful, information-rich site, and write pages that clearly and accurately describe your content.”

By following these guidelines, you are helping web search engines understand your content.

Enterprise content and enterprise search 

Enterprise search is a different story. Content is gathered from different sources, with varying degrees of  structure, and mostly without links that could be used for pagerank.

Content publishers in an enterprise search solution are on their own, with no official guidelines describing the rules to follow to win top ranking on the intranet search. More often than not, nobody knows how the enterprise search engine really works. Compare this to the web search situation, and it should not come as a surprise if enterprise search sucks.

A solution to this dilemma requires taking a step back from the idea that enterprise search is a box that you can plug in to your intranet  and “there was search”.

Search tech guys often pride themselves in the abilities of their search engines, and will rather fix relevancy problems created by bad content by doing tricks on the technical side of things. On the other side of the story, content producers expect search to “just work”, and put all the responsibility on technology and the implementer.

Creating a great enterprise search solution requires cooperation between the makers of content and the makers of search solutions.

Content producers should know how their content will end up in search. They should know what factors affect findability. Search solutions should have documentation targeted towards the end user, which in the enterprise also might be a content producer.

ESO

We can define Enterprise Search Optimization (ESO) as the art of improving Enterprise Search. Where ESO has been applied, we should expect to find a well functioning search solution, where employees and content producers know how to create easily findable content.

Compared to SEO, Enterprise search optimization is a simple procedure, involving little  guesswork in regard to figuring out how the search engine works. It is also a difficult procedure, since ESO needs to be individually tailored and optimized for the specific informational needs for each enterprise.  To develop ESO guidelines, the search technicians need to sit down with the content producers and users to figure out the details of the information model and where the pain of missing information hurts the most.

ESO should result in a list of guidelines, or rules, similar to the lists of SEO. These rules can range from simple and obvious, making sure documents have descriptive titles, correct dates and author, to more complex involving consistent language use, metadata fields for categorization, etc. ESO rules should also explain how structure is imposed on data with less structure.

Recognizing authoritative content is solved in web search engines by using the pagerank algorithm. Enterprise search will rarely be able to use pagerank directly. Authority can often be determined by other means. This can range from simple facts like “This book is the company procedure bible” to “powerpoint is more important than word”.

Optimizing  enterprise search

Enterprise search can suck a little less by applying a customized version of SEO.

Målet vårt er ofte å lage en “intern Google” for kundene våre. Det betyr at vi må forstå hva brukerne leter etter.

Når du søker på “365″: Leter du da etter en kollega med internnummer som slutter på 365? Leter du etter personen med ansattnummer 365? Eller leter du rett og slett etter eksperten på produktet deres som heter “365″?

Jeg er en stor tilhenger av ett stort søk for bedriften din! Og jeg vet at jeg har flinke kolleger som kjenner veien frem til god relevans og en smart søkemotor.

Men: Min teori er at et godt søk blir enda bedre, jo mer vi vet om hva du egentlig leter etter.

Vi har to ganske tydelige retninger for personsøk: Enten søker du etter kontaktinfo eller så leter du etter områdeeksperten.

Lek at vi skal lage et internsøk for Willy Wonka. Målet vårt er å la Oompa Loompaene bruke mest mulig tid på å lage  fantastisk godteri, og minst mulig tid på å lete etter oppskrifter, eksperter og telefonnummer.

I tillegg til å ha laget et stort “internt Google”, har vi også laget to app’er for Loompaene. En telefonbok og et ekspertsøk.

Først telefonboken:

Willy Wonka PhoneBook er akkurat det det høres ut som. En app på datamaskinen, nettbrettet eller smarttelefonen som lar Loompane søke internt etter et telefonnummer eller en Lync-kontakt. I eksempelet over husker ikke brukeren fornavnet på kollegaen, men vet at han heter Loompa til etternavn og at han jobber i sjokoladeavdelingen. (Og slapp av! Selv om du ikke ser forskjell klarer en Oompa Loompa å skille ansiktene fra hverandre.)

Dette løser et problem vi ikke bare finner på sjokoladefabrikker, men hos de fleste av våre kunder; å finne telefonnummeret. Og på denne måten har vi enda større sjanse for å gi dem rett svar på topp i resultatlisten.

Fordi brukerhistorien er så enkel som “jeg vil finne kontaktinfo til kollegaen min”, kan vi også strippe løsningen for sortering og fasetter – og ende opp med et enda enklere brukergrensesnitt.

Når en Oompa Loompa på den annen side vil finne eksperten på sorbet kan han derimot starte app’en “Willy Wonka Expert Search”:

Ekspertsøket søker gjennom alle dokumenter som ligger på filserveren til Willy Wonka, og søker gjennom alle diskusjoner, statusoppdateringer, grupper og diskusjoner på Willy Wonkas “interne Facebook”. Gjennom å finne hvem som har skrevet mest om fagområdet sorbet, kan vi mest sannsynlig også vise frem at “Roger Loompa” er eksperten på området.

Når vi i tillegg ser at Roger på Iskrem labratoriet har nevnt sorbet i sin siste statusoppdatering … kan vi vel si at vi har en vinner.

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Målet er ikke å lage en haug med app’er. Langt i fra! Målet er derimot å gi brukeren rett svar på spørsmålet sitt – hver eneste gang.

Å lage løsninger som forteller oss mer om hva brukeren leter etter, gjerne allerede før de har gjort et søk, øker sannsynligheten for å gi det rette svaret med en gang.

Our goal is often to make “an internal Google” for our customers. That indicates that we must understand what you are looking for.

When you search for “365”: Are you looking for the person with the internal phone number 365? Are you looking for a colleague with the employee number 365? Or are you actually looking for the expert on your product, named “365”?

I am all for making one big search for your company! And I know I have colleagues that are magicians enough to solve these kinds of traps.

But: My theory is that a great search gets even greater when we know more about what the users are looking for.

These  are the two directions of people search:

Let’s pretend we make a search solution for Willy Wonka. Our goal is to let the Oompa Loompas spent as much time as possible making candy, and as little time as possible searching for recipes and phone numbers.

In addition to having one great search where they find everything, we also made two apps for the Loompas. One PhoneBook and one ExpertSearch.

The PhoneBook is exactly that: An internal app on the Oompa Loompas desktop, smartboard or smartphone, where they can search for the phone number of the loompa in the chocolate department that they don’t remember the name to. (Chill! They can separate their colleagues from each other even if you think they all look the same.)

This solves a common user need  that we find not only in candy factories, but at most of our customers. And we have a lot greater chance to give them the right answer doing it like this.

Because we take away all other user stories then “I want to find my colleagues contact information”, we can also take away almost all navigation and refiners – and end up with a much cleaner interface.

When an Oompa Loompa on the other hand needs help on refining the sorbet in the ice cream room, he can start the ExpertSearch app on his desktop, smartboard og smartphone.

The ExpertSearch searches within all documents written in the Willy Wonka Factory, and goes through all the discussions, social updates and communities on their internal collaboration system. From finding who has written and talked the most about sorbet, we can most likely end up with suggestion “Roger Loompa” as the subject matter expert.

When we see that Roger in the Ice Cream Lab. in addition mentions sorbet in his latest MySite Status, I guess we have a winner.

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Making a lot of apps isn’t the goal in itself. Not at all! The goal is to give the users the right answer on top of the search result list, every time.

But making solutions that gives the user the opportunity to tell us more about what kind of an answer he is looking for, even before he makes the search, increases the likelihood of giving the correct answer right away.

A few weeks ago, my colleague Harald and I carried out user interviews at a customer for their “internal Google” solution. Before starting to developing concepts and interaction design, we wanted to properly figure out their users’ actual needs.

One of our findings was that the users generally know the type of content they are looking for. When caseworker Paul searched for “Vietnam Workshops” he knew that it was an application he was looking for – and not a colleague, a PowerPoint file or a news article on the intranet.

Armed with this knowledge, we got some ideas about how to improve their current search experience.

Previously, we thought that this was a good results page when searching for “Vietnam Workshops”:

But maybe this is the way to give Paul a better answer?

By asking Paul to help us decide on the “search mode” before we begin to guess his answer, we will most likely be able to give him a more accurate result.

However, when we user tested this a few weeks later we received clear feedback that the tiled design left the user feeling a little “cheated”. They had searched, but still needed to make a choice before finding the answer. This is also a well known search pattern – even explained by Monty Python.

Turns out Winnie the Pooh has the answer: “Yes please, I’ll have both” is the solution! The search results present a clear call to action to select a category, but with some results already presented on the first page:

A good argument for displaying results in the various search modes right away is to show Paul that we “know more than what he thinks.” By quickly scanning the results page, he may learn something new about his colleagues, see who’s associated with “Vietnam Workshops”, or maybe he’ll find something interesting in an intranet article; hopefully making his current task simpler and quicker to carry out.

But making it clearer that he has the opportunity to choose a search mode, can help Paul to help us give him a more accurate answer.

The facet numbers contain good info, but are not easy accessible for the user. with no alignment, and crunched together with the facet name.

Graphs on the other hand are visual representation of numbers, are easy for the eye to scan. You don’t need to read and process to understand the size relations between the columns/rows shown below.

Did you have to read the numbers to understand the size relations?

The good thing is that we can achieve the same thing for our facet numbers by using the order of magnitude for each facet. Just right align the facet numbers and you’re pretty much there. If you’re lucky, you only need to add a small amount of CSS.

Right aligning the numbers better show the numbers order of magnitude.

So, how could this be done better even better? Maybe by showing the numbers as bars with evenly spaced steps for each order of magnitude? I’ll test out the next chance I get. In the mean time, Photoshop will do:

Do we need the numbers at all? Maybe just to explain what the bars actually represents?

What do you think? All comments are welcome!

If you have read everything else of interest – and want to pump yourself up on enterprise search – look no further. We have compiled our ultimate reading list for search enthusiasts, a comprehensive collection of the most interesting books on search thinking and search doing written to date. Hopefully you’ll find something worth reading here aswell.

We would love to hear what you think of these book, and please feel free to share your own suggestions in the comments below. Happy reading!

Search Thinking

Lovely books on the softer, more fluffy side of enterprise search.

Everything Is Miscellaneous by David Weinberger – the human side of information, and why tagging is better than classification.

Ambient Findability by Peter Morville – search in a wider perspective, and possible futures.

The Search by John Battelle – the story of how Google revolutionized web search and Internet business.

Search Doing

Rock-solid and down-to-earth practical advice on search design and implementation.

Search Patterns by Peter Morville – the definitive guide to design patterns for search.

Search Users Interfaces by Marti A. Hearst – a textbook packed with useful examples of search user interface designs.

Designing Search: UX Strategies for eCommerce Success by Greg Nudelman – the how-to on eCommerce search design and implementation, with a lot of helpful guidelines.

Search Analytics by Louis Rosenfeld – a thorough and comprehensive guide to working with your search logs.

Faceted Search by Daniel Tunkelang – a crash course in one of the most enigmatic challenges of enterprise search.

Exploratory Search: Beyond the Query-Response Paradigm by Ryen W. White & Resa A. Roth – the academic perspective on information seeking behavior and Human-Computer Information Retrieval (HCIR).

Update:

Martin White has published two additional books on enterprise search that you may want to check out:

• Successful Enterprise Search Management
• Making Search Work: Implementing web intranet and enterprise search