Enterprise Search

Do you have big data? Or just lot of it?

Of course, I’m a search nerd. I've been involved in enterprise search for over 20 years. I see search and big data as related technologies, but in most cases, I do not see them as synonymous.

And I'd also say that, while most enterprises have a lot of data, the term ‘big data’ is not applicable to most organizations.

Consider that Google (and others) define ‘big data’ as “extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions”.

Yes, the data that Amazon, Google, Facebook, and others collect qualifies as big data. These companies mine everything you do when you're using their sites. Amazon wants to be able to report “people like you bought …” to sell more product; Google wants to know what ‘people like you’ look at after a query so they can suggest it to the next person like you; Facebook.. well, they want to know what to try to sell you as you chat with and about your friends. Is search involved? Maybe; but more often some strong machine learning and internal analytics are key.

Do consulting firms like Ernst & Young or PWC have big data? Well, my bet is they have alot of information about their clients, business practices, accounting, etc.. but is it ‘big data’? Probably not.

Solr, Elastic and other search technologies can search-enable huge sets of data, so often big data is indexed to be searchable by humans. And both Solr and Elastic come with some great analytical tools.. Kibana on Elastic, and Banana, the port of Kibana for Solr based engines.

But again, is that big data Or just lots of it?

I’d vote the latter.

 

Lucidworks expands focus in new funding round

Lucidworks, the commercial organization with the largest pool of Solr committers, announced today a new funding round of $50M US from venture firms Top Tier Capital Partners and Silver Lake Waterman, as well as additional participation from existing investors Shasta Ventures, Granite Ventures, and Allegis Capital.

While a big funding round for a privately held company isn't uncommon here in 'the valley', what really caught my attention is where and how Lucidworks will use the new capital. Will Hayes, Lucidworks' CEO, intends to focus the investment on what he calls "smart data experiences" that go beyond simply artificial intelligence and machine learning. The challenge is to provide useful and relevant results by addressing what he calls "the last mile" problem in current AI:  enabling mere mortals to find useful insights in search without having to understand the black art of data science and big data analysis. The end target is to drive better customer experiences and improved employee productivity.

A number of well-known companies utilize Lucidworks Fusion already, many along with AI and ML tools and technologies. I've long thought that to take advantage of 'big data' like Google,  Amazon, and others do, you needed huge numbers of users and queries to confidently provide meaningful suggestions in search results.  While that helps, Hayes explained that smaller organizations will be able to benefit from the technology in Fusion because of both smaller and more focused data sets, even with a smaller pool of queries. With the combination of these two characteristics, Lucidworks expects to deliver many of the benefits of traditional machine learning and AI-like results to enterprise-sized content. It will be interesting to see what Lucidworks does in the next several releases of Fusion!

Your enterprise search is like your teenager

During a seminar a while back, I made this spontaneous claim. Recently, I made the comment again, and decided to back up my claim - which I’ll do here.

No, really – it’s true. Consider:

You can give your search platform detailed instructions, but it may or may not do things the way you meant:

Modern search platforms provide a console where you, as the one responsible for search, can enter all of the information needed to index content and serve up results. You tell it what repositories to index; what security applies to the various repositories; and how you want the results to look.  But did it? Does it give you a full report of what it did, what it was unable to do, and why?

You really have no idea what it’s doing – especially on weekends:

 Search platforms are notorious for the lack of operational information they provide.

Does your platform give you a useful report of what content was indexed successfully, and which were not – and why? And some platforms stop indexing files when they reach a certain size: do you know what content was not completely indexed?

When it does tell you, sometimes the information is incomplete: 

Your crawler tells you there were a bunch of ‘404’ errors because of a bad or missing URL; but will it tell you which page(s) had the bad link? Chances are it does not. 

They can be moody, and malfunction without any notice:

You schedule a full update of you index every weekend, and it has always worked flawlessly – as far as you know. Then, usually on a 3-day weekend, it fails. Why? See above.

When you talk to others who have search, theirs always sounds much better than yours:

As a conscientious search manager, you read about search, you attend webinars and conferences, and you always want to learn more. But you wonder why other search mangers seem to describe their platform in glowing terms, and never seem to have any of the behavioral issues you live with every day. It kind of makes you wonder what you’re doing wrong with yours.

It costs more to maintain than you thought and it always needs updates:

When you first got the platform you knew there we ongoing expenses you’d have to budget – support, training, updates, consulting. But just like your kid who needs books, a computer, soccer coaching, and tuition, it’s always more than you budgeted. Sometimes way more!

You can buy insurance, but it never seems to cover what you really need:

Bear with me here: you get insurance for your kids in case they get sick or cause an accident, and you buy support and maintenance for your search platform.  But in the same way that you end up surprised that orthodontics are not fully covered, you may find out that help tuning the search platform, or making it work better, isn’t covered by the plan you purchased – in fact, it wasn’t even offered. QED.

It speaks a different vocabulary:

You want to talk with your kid and understand what’s going on; you certainly don’t want to look uncool. But like your kid, your search platform has a vocabulary that only barely makes sense to you. You know rows and columns, and thought you understood ‘fields’; but the search platform uses words you know but that don’t seem to be the same definition you’ve known from databases or CMS systems.

It's hard for one person to manage, especially when it's new:

Many surveys show that most companies have one (or less) full-time staff responsible for running the search engine – while the same companies claim search is ‘critical’ to their mission.  Search is hard to run, especially in the first few years when everything needs attention. You can always get outside help – not unlike day care and babysitters – but it just seems so much better if you could have a team to help manage and maintain search to make it behave better.

How it behaves reflects on you:

You’re the search manager and you’ve got the job to make search work “just like Google”.  You spent more than $250K to get this search engine, and the fact that it just doesn’t work well reflects badly on you and your career. You may be worried about a divorce.

It doesn’t behave like the last one:

People tend to be nostalgic, as are many search managers I know. They learned how to take care of the previous one, but this new one – well, it’s NOTHING like the earlier one. You need to learn its habits and behaviors, and often adjust your behavior to insure peace at work.

You know if it messes up badly late at night, even on a weekend or a holiday, you’ll hear about it:

If customers or employees around the world use your search platform, there is no ‘down time’: when it’s having an issue, you’ll hear about it, and will be expected to solve the issue – NOW. You may even have IT staff monitoring the platform; but when it breaks in some odd and unanticipated way, you get the call. (And when does search ever fail in an expected way?)

 You may be legally responsible if it messes up:

Depending on what your search application is used for, you may find yourself legally responsible for a problem. Fortunately, the chances of you personally being at fault are slim, but if your company takes a hit for a problem that you hadn’t anticipated, you may have some ‘career risk’ of your own. Was secure content about the upcoming merger accidentally made public? Was content to be served only to your Swiss employees when they search from Switzerland exposed outside of the country? And you can’t even buy liability insurance for that kind of error.

When it’s good, you rarely hear about it; when it's bad, you’ll hear about it:

Seriously, how many of you have gotten a call from your CIO to tell you what a great experience he or she had on the new search platform? Do people want to take you to lunch because search works so well? If you answered ‘yes’ to either of these, I’d like to hear from you!

In my experience, people only go out of their way to give feedback on search when it’s not working well. It’s not “like Google”. Even though Google has hundreds or people and ‘bots’ examining every search query to try to make the result better, and you have only yourself and an IT guy.

You’ll hear. 

The work of managing it is never done:

The wonderful southern writer Ferrol Sams wrote :

“He's a good boy… I just can't think of enough things to tell him not to do.” Sound like your search platform? It will misbehave (or fail outright) in ways you never considered, and your search vendor will tell you “We’ve never seen a problem like that before”. Who has to get it fixed? You have to ask?

Once it moves away, you sometimes feel nostalgic:

Either you toss it out, or a major upgrade from your vendor comes alone and the old search platform gets replaced. Soon, you’re wishing for the “Good old days” when you knew how cute and quirky the old one was, and you find yourself feeling nostalgic for it and wishing that it didn’t have to move out.

Do you agree with my premise? What  have I missed?

Sometimes you're just wrong! (Maybe).

OK, this one falls into the 'eat your own words' category, so I have to come clean. Well, partly clean. Let me explain.

I was out of town last week, but just before I left I wrote an article asserting that Elasticsearch really isn't 'enterprise' search. The article drew alot of attention and comments from both sides of the argument. I have to say I still think that's the case, but an announcement by Microsoft seems to differ, and end up a net positive for Elasticsearch. Microsoft tells us that Elasticsearch is the platform under the covers of Microsoft's Azure search offering. It looks like you have a couple of options - as long as you're on Azure:

a) You can download and use the open source Elasticsearch platform available on GitHub; or

b) Use Microsoft's managed service 'Facetflow Elasticsearch' which incorporates (some of) the open source code in various places.

Microsoft calls this "a fully-managed real-time search and analytics service" while, according to ZDNet, it is for 'web and mobile application developers looking to incorporate full-text search into their applications'. 

Either way, it's certainly yet another step forward for Elasticsearch, and is a big step forward in visibility for the company. It's not clear what kind of revenue they will receive from the deal - Microsoft being relatively famous for being quite frugal. And after all, smart search folks like Kevin Green of Spantree Technology Group talk about its strengths and liabilities, saying it *is* fast ('wicked fast'); fault-tolerant; distributed and more. But it is not a crawler; a machine learner; a user-facing front end, and it is not secure. 

So I'll agree a partial 'mea culpa' is in order; adding capabilities to an open source project can make it more enterprise ready. But I think the jury may still be out on the rest of my piece. Stay tuned!

Big data: Salvation for enterprise search?

Or just another data source?

With all the acquisitions we've seen in enterprise search in the last several years, it's no wonder that the field looks boring to the casual observer. Most companies have gone through two or more complex, costly search implementations to a new search platform, users still complain, and in some quarters, there seems to be 'quality of search fatigue'. I acknowledge I'm biased, but I think enterprise search implemented and managed properly provides incredible value to corporations, their employees, and their customers/consumers. That said, a lot of companies seem to treat search as 'fire and forget', and after it's installed, it never gets the resources to get off the ground in a quality way.

It's no surprise then that the recent hype bubble in 'Big Data' has the attention of enterprise search companies as they see a way to convince an entirely new group of technologists that search is the way. 

It's certainly true that Hadoop's beginning was related to search - as a repository for web crawler Nutch in preparation for highly scalable indexing in Lucene/Solr no doubt. Hadoop and its zoo* of related tools certainly are designed for nerds. At best, it's a framework that sits on top of your physical disks; worst case it's a file system that does support authentication but not really security (in the LDAP/AFD sense). And it's a great tool to write 'jobs' to manipulate content in interesting ways to a data scientist. How is your Java? Python? Clojure? Better brush up.

The enterprise search vendors of the world certainly see the tremendous interest in Hadoop and 'big data' as a great opportunity to grow their business. And for the right use cases, most enterprise search platforms can address the problem. But remember that, to enterprise search, the content you store in Hadoop is simply content in a different repository: a new data source on the menu.

But remember, big data apps come with all the same challenges enterprise search has faced for years plus a few more. Users - even if data scientists and researchers - think web-based Google is search; and even though - as a group - this demographic may be more intelligent than your average search users, they still expect your search to 'just know". If you think babysitting your existing enterprise search solution is touch, wait until you see what billions of documents does for you.

And speaking of billions of records - how long does your current search take to index your current content? How long does it take to do a full index of your current content? Now extrapolate: how long will it take to index a few billion records? (Note: some vendors can provide a much faster 'pipe' for indexing massive content from Hadoop. Lucidworks and Cloudera are two of the companies I am familiar with; there may be others)

A failure in search? Well, it depends what you want. If you are going to treat Hadoop as a 'Ha-Dump' with all of your log files, all of your customer transactional data, hundreds of Twitter feeds for ever and ever, and add your existing enterprise data, you're going to have some time on your hands while the data gets indexed.

On the other hand, if you're smart about where your data goes, break it into 'data lakes' of related content, and use the right tool for each type of data, you won't be using your enterprise search platform for use cases better served with analytics tools that are part of the Apache Zoo; and you’ll still be doing pretty well. And in that universe, Hadoop is just another data source for search - and not the slow pipe through which all of your data has to flow.

Do you agree?

 

*If you get the joke, chances are you know a bit about the Apache project and open source software. If not, you may want to hold off and research before you download your first Hadoop VM.