Mobile business intelligence — real business value or just a snazzy demo?

I discussed some mobile BI use cases in July 2010, but I’m still not convinced the whole area is a legitimate big deal. BI has a long history of snazzy, senior-exec-pleasing demos that have little to do with substantive business value. For now, I think mobile BI is another of those; few people will gain deep analytic insights staring into their iPhones. I don’t see anything coming that’s going to change the situation soon.

BI-centric collaboration — real business value or just a snazzy demo?

I’m more optimistic about collaborative business intelligence. QlikView’s direct sharing of dashboards will, I think, be a feature competitors must and will imitate. Social media BI collaboration is still in the “mainly a demo” phase, but I think it meets a broader and deeper need than does mobile BI. Over the next few years, I expect numerous enterprises to establish strong cultures of analytic chatter (and then give frequent talks about same at industry conferences).  

Business intelligence for mid-market enterprises is problematic

Given the saturation of the large-enterprise BI market with supposed enterprise-standard BI systems, it would seem that smaller enterprises comprise a large part of the BI growth opportunity. However, the large-enterprise and mid-range BI markets are very different. For example:

• Large enterprises typically have tough challenges in data integration; smaller enterprises may truly start out with their data in only a few systems.
• There are many reasons for large enterprises not to do their BI in the cloud, such as bandwidth, internal politics, or the unsuitability of most cloud infrastructure for analytic DBMS scale-out. Smaller enterprises, however, may prefer SaaS (Software as a Service) BI.
• The BI market for smaller enterprises is heavily OEM. But unless you’re buying some kind of data/analytics bundle, the large enterprise BI market still seems overwhelmingly standalone.
• Large-enterprise BI tools incorporate much of a DBMS-like technology stack; at smaller enterprises, BI can often stick to its specialized-application-development-tool knitting. But on the other hand …
• … large enterprises almost always already have a data warehousing infrastructure. Mid-range BI buyers may not have a separate analytic DBMS. Therefore …
• … BI/DBMS bundles make more sense in the mid-market than they do at large enterprises.
• Each large enterprise has a unique infrastructure, and  commonly a unique competitive situation as well. Thus, the idea that you’ll pre-build most of an analytic application for a large enterprises — because you know what data model they need to do their BI — usually turns out to be silly. But smaller enterprises can be more homogeneous, and so for them pre-built analytic applications can actually work.

I don’t know of anybody who’s really cracked the code on mid-market BI. Crystal Reports (long owned by SAP Business Objects) has huge OEM share, but somehow hasn’t parlayed that into a comprehensive mid-market BI presence. Various SaaS or on-premise vendors have cool product ideas — e.g. Gooddata, Endeca, or my clients at PivotLink — but none seems to have set the world on fire to this point.

Departmental BI is doing better

The news is happier in a related market — business intelligence for departments of larger enterprises. However, this is a hard market to analyze, for at least two reasons. First — as is often the case — the distinction among large-enterprise-wise, smaller-enterprise-wide, and departmental BI is not a clear one.* Second, “departmental BI” has at least two major strains:

• Simple, pedestrian BI, implemented quickly.
• Investigative analytics.

*In particular, it has been the case since the 1990s that BI tools first get sold to departments, hopefully for fast implementations — think 4-6 weeks as a base case — and then spread out internally after their initial successes. I am frequently amused by vendors who claim to have pioneered that sales model sometime over the past decade, or even within the past few years.

That said, there are two main kinds of reason to do your BI departmentally, at arm’s length from central IT.

• Perhaps, for good reason or bad, IT is being insufficiently helpful at managing the data.
  • This can be a straightforward matter of politics and priorities — IT controls the data, but is slow about giving you access.
  • Also, you may want to include data that’s outside IT’s purview, be it third-party or just purely departmental.
• Further, you may want functionality that corporate-standard BI doesn’t offer. Potential examples include:
  • Cool analytic visualization.
  • “Real-time” data visualization.
  • The ability to play nicely with particular kinds of data sets.

I have a lot more to say about those points — but not in a post that’s already as long as this one.

Roman’s latest venture is business intelligence firm Gooddata. Gooddata was founded in 2007 and has been soliciting and getting attention for a while, so I was surprised to learn that Gooddata officially launched just a few weeks ago. Anyhow, some less technical highlights of the Gooddata story include:

• Gooddata believes it makes BI easy to adopt, unlike every other BI vendor on the planet — not excluding the many other BI vendors who say the same thing about themselves.
• Gooddata is entirely cloud-based, specifically in the Amazon cloud. I.e., Gooddata is selling SaaS-based BI.
• Gooddata wants to sell to enterprises that are large enough to have more than a couple of BI users, and small enough not to be well served by the BI market leaders.
  • In revenue terms, this is the ever-popular $100 million – $1 billion market.
  • Specifically, Gooddata believes that those enterprises may have decent “back office” BI, but don’t have much in the front office. Gooddata wants to provide them with front office BI, which seems to basically mean CRM analytics. Gooddata sees this as a market in which QlikTech is the major player. Generally, Gooddata wants to emulate and go after QlikTech.
  • Even more specifically, Gooddata wants to sell to Salesforce.com customers, who it believes are not well-served by what passes for built-in analytics at Salesforce. Partnering with NetSuite didn’t work as well, since NetSuite’s customers turn out to be smaller firms than are in Gooddata’s target market.
• Something I heard from both Jaspersoft and Gooddata is that there’s a hot market in providing cloud-based BI to online gaming companies. I gather these are mainly games running on mass communication platforms such as Facebook or the iPhone. Surely not coincidentally, it seems likely that:
  • These are small companies whose success – and hence data intake – can suddenly explode.
  • The data originates in cyberspace, with no particular need ever to come to the game companies’ own premises.
• Gooddata has 50 production customers.
• Gooddata had 2500 “projects” at the end of beta in June, and is adding 100 more per month. (Those numbers look weird together.) A “project” is a lot like a database, with associated reports, security privileges, etc.
• Gooddata has close to 40 people, mainly in development.
• I didn’t detect much of a sales strategy, nor much of a marketing strategy beyond the impressive early buzz generation. Perhaps that’s a partial explanation as to why the rate of Gooddata adoption fell even before the company officially launched.
• I forgot to ask what those 50 customers were actually paying, but considering Gooddata’s price list, it appears a typical price range for Gooddata’s stuff would be $500-$2,000/month.

Gooddata technical highlights include:

• Gooddata is building an entire BI stack – reporting, dashboards, ETL, in-memory database management, everything. I doubt Gooddata would claim that the pieces are best-of-breed in many ways other than BI ease of adoption and use.
• So far I’ve seen three Gooddata ease-of-use features or feature groups that strike me as differentiated – reusability (of metrics and/or reports), collaboration, and tag clouds. More on those below. Gooddata is also building toward an agility pitch, but those features aren’t all baked yet.
• Gooddata is MySQL-based today, but plans to move to a memory-centric compressed column store in 2010. Roman doesn’t reject analogies to SAP’s BI/BW/whatever Accelerator. Yes, folks – Gooddata is yet another BI vendor doing some form of memory-centric OLAP. That’s a big trend.
• I’m guessing that a big reason Gooddata is reinventing so many technical wheels is to ensure that the Gooddata stack is seamlessly multi-tenant from top to bottom. (Hasso Plattner of SAP’s comments on a similar idea suggest a similar emphasis.)
• Gooddata has its own multidimensional query language called MAQL (the A doesn’t seem to stand for anything). Today MAQL generates SQL for MySQL. The future columnar memory-centric data store will — I think – understand MAQL natively.

Now we get to the good stuff. When I wrote about reinventing business intelligence back in May, I focused on some interesting developments I see as actually underway — at least on an experimental basis and/or from small vendors – namely:

• Text-search interfaces. Well, while I didn’t see true text search in the Gooddata demo, I did see tag clouds, which have some of the same benefits.
• Collaboration tools. Well, Gooddata has a nice-looking approach to BI collaboration, heavily reflected in its UI metaphors. (That said, I haven’t really compared Gooddata to Microsoft SharePoint or SAP’s Portal/Rooms/whatever.)
• Memory-centric analytics (for speed of exploration). As noted above, Gooddata has that coming soon.
• Data exploration that tries to ignore fixed relational schemas, ala Attivio or Splunk. Roman says Gooddata is interested in or working on that, but offers no timetable.

Meanwhile, something I’ve been seeking for years, but haven’t seen much progress on since enhancement stopped on Cognos Metrics Manager, is more user-friendly metrics management. Well, it doesn’t have a lot of bells and whistles, but at least Gooddata has the basics – a list of already-defined metrics, and a reasonable way of compounding them into other metrics. I think that kind of thing will be a major BI feature going forward, to the point that a few years from now we’ll be worrying about how to port them from one BI vendor’s tool from another.

Bottom line: If you’re interested in BI, you should look at a Gooddata demo.

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