• Personally selected annual lists of 12 “Most influential” companies and 36 “Companies to watch” in analytics- and database-related sectors.
• Made it clear that these are his personal selections.
• Nonetheless has called it an Editors’ Choice list, rather than Editor’s Choice.

(Actually, he’s really called it an “award.”)

People advising Doug — who come to think of it actually are Contributing Editors to Intelligent Enterprise or something like that — included Cindi Howson, Seth Grimes, three others, and me.

And if past is prologue, I will now get a flood of PR emails calling my attention to this award that I already have both participated in and blogged about.

As usual, the sense:nonsense ratio on these lists was pleasingly high. Analytic DBMS vendors cited included IBM, Microsoft, Netezza, Oracle, Sybase, and Teradata in the “Most influential” group, with Aster, Greenplum, HP, Infobright, and Vertica among the “To watch” crowd. It’s tough to argue with those selections, whose most questionable element is probably the not-ridiculous supposition that HP could do something interesting over the coming year. Cloudera and Intersystems also made the list, deservedly.

All three of QlikTech, Tableau, and TIBCO made the list, which is appropriate given the potential for and interest in interactive data exploration technology.  The BI majors, independent or otherwise, were all on as well. In text mining, Doug included Attensity and Clarabridge, which I think is exactly right. (Plus OpenCalais.)  Upon reflection, I probably should have nominated Mark Logic, even though most of its business is non-enterprise; but hey, nobody’s perfect, and the same goes for lists. Open source was well represented, with Apache, Actuate, Jaspersoft, Eclipse, Infobright, Nuxeo and R all being cited (but not Ingres or Pentaho). Kalido made the list, with my endorsement, their silly I-CASE like marketing messaging notwithstanding.

Speaking of imperfections — there only are a few category names, and so category assignments can be pretty bizarre. (In an ideal world, middleware wouldn’t be included under “enterprise applications”.) Greenplum hasn’t really “extended” its DBMS with a “cloud” option. As much as I’d like Netezza to be more influential than SAP, that’s probably not the best way to rank them. And there are a number of “This company is on a roll!” kinds of comments that I wouldn’t necessarily endorse.

But those are all nitpicks. On the whole, it’s another nice job.

• Over a 20 week period, I had travel in 13 of them.
• 3 of those were vacation in November.
• As travel finally wound down:
  • It was time to focus a bit on my own business
  • Elder care got serious; e.g., my parents went to the hospital on consecutive days, Christmas week, the first one on their 52nd wedding anniversary
  • Linda and I both got really nasty colds
  • The holidays were happening
  • I started helping out a really cool startup company (first time I’ve taken stock in a private company in years; more on that soon)
  • There was less industry news going on anyway than in some other recent months

But of course I plan to speed up the research/analysis/writing soon. Here, FYI, are a few things I have on my plate.

For a couple of years now, the center of what I’ve written about has been high-performance analytic data processing. You can expect me to keep pursuing that in all its aspects. But there are two specific areas I’ve identified in which I want to redouble my efforts.

First, almost every BI vendor has an effort in “in-memory analytics” and/or “interactive data exploration.” I suspect there’s a lot of difference in underlying technologies, but I’m having trouble getting details. QlikTech (the worst foot-dragger of the three), Microstrategy, and Jaspersoft all owe me follow-up conversations with the people who know what’s going on well enough to explain it. Tableau keeps promising me a briefing and then not delivering. And I’m even further behind with the behemoth companies — Oracle, Microsoft, IBM/Cognos (arguably) et al.

Second, solid-state memory is coming to data warehousing. The obvious reasons are that it’s obviously close, and Moore’s Law still applies to bring it closer. More specific reasons for believing in solid-state include:

• Teradata has made large strides in making solid-state memory useful.
• The stealth start-up I mentioned above is poised to make further strides.
• (I’m not totally sure yet about this part) The in-memory analytics mentioned above might wind up working better in solid-state memory than in DRAM.

I’m spending quite a few cycles thinking about this area.

I’d also like to look further at analytic applications and advanced analytic functionality. I foreshadowed some of that in my Aster webinars. There’s some good stuff to talk about at Teradata I should try to write up soon. I need to have a follow-up conversation with fascinating anti-fraud guy I met at Netezza’s London event. But that’s all just scratching the surface.

Both the MySQL and PostgreSQL communities are in some disarray. Other non-behemoth OLTP/general-purpose DBMS seem to be, at best, thriving niche products. (I see little in the way of innovative new use for, say, Progress, Cache’, Ingres, or anything multivalue.) But it feels as if there’s more opportunity out there than is being met. And at a minimum, I’d like to learn more than the almost nothing I know about OLTP NoSQL alternatives.

I’ve already said that I expect to give an industry-overview talk at MIT on January 28. I also have an overviewy press article and overviewy white paper under discussion. If those come to fruition, I’ll of course let you know.

Besides the above, I of course have a number of specific posts that I need to get around to researching and writing at some point, often on topics I’ve already written about before.  Three subjects fairly high on the priority list are scientific data management, machine-generated data, and Oracle Exadata.

And finally, I have some subjects queued up for a couple of my other blogs as well. If you don’t already take our multi-blog integrated feed, this might be a good time to switch over.

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.

]]> http://www.dbms2.com/2009/12/27/introduction-to-gooddata/feed/ 12 http://www.dbms2.com/2009/04/01/business-intelligence-notes-and-trends/ http://www.dbms2.com/2009/04/01/business-intelligence-notes-and-trends/#comments Wed, 01 Apr 2009 06:59:23 +0000 Curt Monash http://www.dbms2.com/?p=737 I keep not finding the time to write as much about business intelligence as I’d like to. So I’m going to do one omnibus post here covering a lot of companies and trends, then circle back in more detail when I can. Top-level highlights include:

• Jaspersoft has a new v3.5 product release. Highlights include multi-tenancy-for-SaaS and another in-memory OLAP option. Otherwise, things sound qualitatively much as I wrote last September.
• Inforsense has a cool composite-analytical-applications story. More precisely, they said my phrase “analytics-oriented EAI” was an “exceptionally good” way to describe their focus. Inforsense’s biggest target market seems to be health care, research and clinical alike. Financial services is next in line.
• Tableau Software “gets it” a little bit more than other BI vendors about the need to decide for yourself how to define metrics. (Of course, it’s possible that other “exploration”-oriented new-style vendors are just as clued-in, but I haven’t asked in the right way.)
• Jerome Pineau’s favorable view of Gooddata and unfavorable view of Birst are in line with other input I trust. I’ve never actually spoken with the Gooddata folks, however.
• Seth Grimes suggests the qualitative differences between open-source and closed-source BI are no longer significant. He has a point, although I’d frame it more as being about the difference between the largest (but acquisition-built) BI product portfolios and the smaller (but more home-grown) ones, counting open source in the latter group.
• I’ve discovered about five different in-memory OLAP efforts recently, and no doubt that’s just the tip of the iceberg.
• I’m hearing ever more about public-facing/extranet BI. Information Builders is a leader here, but other vendors are talking about it too.

A little more detail, especially on Jaspersoft:

• Jaspersoft is not using multi-tenancy is to offer BI SaaS itself. But SaaS vendors were demanding the feature. What’s more, a couple of household-name corporations are using Jaspersoft’s multi-tenancy to give extranet BI access to their various customers or suppliers. Lawrence Livermore Labs seems to be a Jaspersoft extranet user too.
• The way Jaspersoft’s multi-tenancy works is that the concept of “organization” is added to the privileges hierarchy. Each organization sees its own virtual server. Only administrative superusers can span organizations.
• Jaspersoft also has a new memory-centric OLAP capability — with disk-based ROLAP for overflow – unrelated to the Mondrian MDX server. That’s a pretty common story in BI these days, I think, but I’ll confess to being unclear about exactly who is offering what when in that regard.
• Jaspersoft’s memory-centric OLAP is just a query accelerator, not a near-real-time data ingester like Aleri Live Update. Jaspersoft does handle real-time telemetry from at least one space mission (to Mars) — but how great can the bandwidth on that be?
• There’s also some kind of AJAX/Web 2.0/mash-up/whatever going on in Jaspersoft v3.5.

Some more detail yet, especially on Inforsense:

• Inforsense is focused on applications that answer a few questions rather than doing high-volume analytics, and are “designed to change.” This may be needed when there’s enough of an analytic business process that conventional BI tools aren’t a good fit (perhaps unless combined with some kind of composite application development tool or methodology).
• Inforsense’s application sweet spot to date is combining and moving around various kinds of health care data. (Especially laboratory data, both research and clinical.)
• Inforsense is a bit confusing because it was founded out of an academic research effort (Imperial College, London) to do data mining parallelized onto grids. That is no longer the company’s main focus, but the confusion continues with an occasional low-revenue, supposedly-high-prestige research award.
• Inforsense is further a bit confusing because, irrespective of focus, its analytic technology can supposedly be almost all things to almost all people. (Exactly the same thing complaint could be made about almost any other BI company.)
• What remains of the academic focus is what Inforsense characterizes as a “very flexible dataflow environment.”
• Inforsense can talk to lots of data sources and so on, including web services. It can also do updating, albeit not in demanding OLTP environments.

A few more notes, especially on Tableau Software:

• Tableau is built around a proprietary language VizQL. VizQL seems to be similar to SQL in that it focuses on filtering data. I haven’t yet read a paper Tableau sent, which should make it clearer what VizQL does that SQL doesn’t.
• Tableau is one of the new breed of “exploration” oriented BI vendors, encouraging users to just dive into data.
• I don’t know whether this is more a matter of technology or just astute marketing, but Tableau seems to be somewhat more focused than other vendors on the idea that you filter data, keep refining that filter as makes sense to you, share that filter with other people, and so on. It is hard to overstate how blind I think the BI industry is being in not aggressively developing and enhancing this kind of technology.
• That said, Tableau’s capabilities in this area still seem pretty primitive too.
• Like most software vendors, Tableau says its biggest competitor is incumbent/no decision. In Tableau’s case, the incumbent can be either BI tools or Microsoft Excel.
• Tableau says its second biggest group of competitors is other new/easy BI vendors such as QlikTech and LogiXML. Interestingly, both got mentioned with about equal emphasis.

• Much like Jaspersoft, Pentaho’s initial focus was mainly on embedded, operational BI.
• However, Pentaho now feels it has a decent end-user GUI as well, and traditional-BI is a bigger part of sales.
• Also, some sales are focused on data integration, perhaps in support of more traditional BI products. Pentaho has even had an Ab Initio replacement in data integration. (Can there be any change more extreme than going from Ab Initio to open source?)
• As an example of technical breadth, Pentaho says that its Mondrian OLAP engine is used by Jaspersoft.
• Pentaho has Excel output, but not in the form of live formulas.
• Pentaho does XQuery.
• Industries with more Pentaho adoption than average include:
  • Financial services (traditionally open-source-friendly, according to Pentaho)
  • Government (ditto)
  • Web 2.0 (obviously ditto)
  • Travel/transportation (cash-strapped)
• Frontier Airlines is a Pentaho/Greenplum customer.
• TradeDoubler is a Pentaho/InfoBright customer. (Pentaho thinks that TradeDoubler reloads its warehouse every day, which if true frankly casts some doubt on InfoBright’s architecture.)
• Data mining is something of a Pentaho sideline. There’s some university in New Zealand that built data mining capabilities in Pentaho, and some data mining research is done in that. Separately, Pentaho has been integrated with R.
• Community contributions are concentrated in the areas you’d expect — features some user or system integrator needs for a specific project, connectors, bug reports, and the like.

The briefing included one of the better slide decks I’ve seen in a while, which Pentaho gave me permission to share (in somewhat abbreviated form) here. In particular, Pentaho provided customer examples illustrating most of the use cases cited above.

Pentaho facts and figures include:

• Pentaho was founded in 2004. The first dozen or so reference customers were acquired in 2007. Before that usage of the product was mainly downloads of a free version.
• Actually, Pentaho’s free usage is more focused on embedded libraries, while paid usage is more skewed to traditional BI.
• Pentaho’s average selling price is $24-25K for first year revenue, which is extremely close to Jaspersoft’s figure.
• There are 100,000+ downloads per month, but Pentaho cautions that’s a very misleading figure. Some users download over 100 different pieces of the product, including for example all the national language support and all the different platform-specific support pieces.
• Pentaho doesn’t offer much in the way of more realistic metric of company size or success.
• Europe provides 35-40% of Pentaho revenue.
• Pentaho has at least one Asia/Pacific reference.
• 50% or so of Pentaho customers are on MySQL. Oracle and Postgres are in a rough tie for #2. That appears to be PostgreSQL rather than EnterpriseDB’s Postgres Plus.

The Jaspersoft Business Intelligence Suite is BI technology designed to be integrated with operational apps. Thus, Jaspersoft says that operational BI is the core of its business. In particular:

• Jaspersoft has what it feels is a reasonably full range of query, reporting, OLAP, and dashboarding capabilities. These can be used like any other BI tools.
• Query and reporting are the main uses. OLAP has a 30% “attach rate.”
• However, the base-case Jaspersoft use is to have reports, charts, or whatever pop up at the right place in operational applications.
• I’m not sure how hard-core transactional these operational apps tend to be. The examples Nick gave me were:
  • SaaS for retailers, running next to Point-of-Sale data.
  • Adding value to industry data. (Also a kind of analytic SaaS, I think.)
  • Checking bookings for a convention center authority.
  • Foreign exchange trading (which can mean anything, but probably has transactions closely connected).
• Closed-loop operational BI, in which analytics directly drives automated behavior – and associated buzzonyms like BPM or BAM — are much more of a future for Jaspersoft than a present capability. (If that.)
• As might be expected from this focus, Jaspersoft products typically run against production databases rather than data warehouses. However, Jaspersoft has been known to partner with the occasional analytic DBMS vendor.
• And as might be expected from that focus, Jaspersoft is strongest in mid-sized enterprises and departments.
• Jaspersoft’s products are designed to be modular and embed-friendly. In a first, non-technical call, I didn’t probe for details as to what this embed-friendliness entails.
• The Jaspersoft metadata abstraction layer is based on Hibernate. It allows data from multiple sources to be joined in single queries.
• You can do business with Jaspersoft in the usual open source range of ways.
  • You can download Jaspersoft’s Community Edition, use it, and never send any money.
  • Or you can buy packaged training, per-incident support, and so on (that’s how they got up to 9000 customers)
  • You can also buy a Jaspersoft Professional Edition, on subscription. You don’t get extra code that I know of, but you get QA, certifications, warranties/indemnifications, and so on. Jaspersoft has a few hundred subscribers, averaging $25K/year.
  • You also need to pay if you’re an OEM, because Jaspersoft’s Community Edition is GPLed.
• Jaspersoft’s Professional Edition is on a real release cycle, to facilitate testing, while the Community Edition evolution is more controlled-chaos (and I’m just guessing about the “controlled” part).
• Somewhat over 50% of Jaspersoft code comes from inside the company, but that’s because they were founded by some community members and since have hired others.
• Some OEMs contribute code back to Jaspersoft. For example — and here’s a blast from the past – Cincom contributed TOTAL connectivity. (Cincom and its database management system TOTAL had their heyday about 30 years ago.)

By the way, I can’t tell from Jaspersoft’s website whether the spelling is Jaspersoft or JasperSoft. There are plenty of instances of each. However, “Jaspersoft” seemed to be the more recent trend.

]]> http://www.dbms2.com/2008/09/14/jaspersoft/feed/ 3 http://www.dbms2.com/2008/09/14/jaspersoft-numbers/ http://www.dbms2.com/2008/09/14/jaspersoft-numbers/#comments Sun, 14 Sep 2008 18:40:06 +0000 Curt Monash http://www.dbms2.com/?p=540 I chatted Friday with marketing VP Nick Halsey of Jaspersoft, which is probably the most successful open source business intelligence company. (That’s based just anecdotally, on mentions. I’d put Pentaho #2, with Talend commonly getting mentioned along with the two BI vendors for its ETL.) I’ll go straight to the numbers, per Nick, before talking in a separate post about what Jaspersoft actually sells.

Highlights include:

• Revenue run rate in the double-digit millions.
• 40% sequential growth most recent quarter. (I didn’t ask whether there was any reason to suspect seasonality.)
• 130% annual revenue growth run rate.
• “Not quite” profitable.
• Several hundred commercial subscribers, at an average of $25K annually per, including >100 in Europe.
• 9,000 paying customers of some kind.
• 100,000+ total deployments, “very conservatively,” counting OEMs as one deployment each and not double-counting for OEMs’ customers. (Nick said Business Objects quotes 45,000 deployments by the same standards.)
• 70% of revenue from the mid-market, defined as $100 million – $1 billion revenue. 30% from bigger enterprises. (Hmm. That begs a couple of questions, such as where OEM revenue comes in, and whether <$100 million enterprises were truly a negligible part of revenue.)
• Windows > Linux for downloads, Linux > Windows for production deployments (but not by much).
• MySQL #1 overall target, Oracle #1 target for “Professional” edition (i.e., those subscribers). SQL Server also strong. Small but non-zero demand for DB2.
• Company founded in 2005, based on true community/open source efforts that had been going on since at least 2001.
• Paying customers from 96 countries. Product translated into 44 languages. (5 by the company, 39 by the community.)
• Red Hat and MySQL/Sun “recently” added as “major” OEMs. I would guess, therefore, that OEM revenue is growing rapidly.

]]> http://www.dbms2.com/2008/09/14/jaspersoft-numbers/feed/ 7