This post is a moderately edited form of an actual interview. Two other posts cover analytic trends to watch (planned) and analytic vendor execution challenges to watch (already up).

Question: What do you think will happen next year with the Tableaus of the world?

Answer:

• I think adoption of flexible-visualization business intelligence tools will continue to be rapid.
• I think enterprise-friendly features will be increasingly important as a basis of competition.

Question: What do you mean by “enterprise-friendly”?

Answer: An example would be QlikTech no longer forcing you to use their native ETL, but rather working with Informatica and soon other third-party products. Also important can be:

• Database size.
• Concurrency.
• A full-featured development cycle for analytic applications.

Question: What does HP have to do to be relevant in analytics/data warehousing?

Answer: Avoid stupidity. HP Vertica is already relevant.

Question: OK. But what can HP do to build on Vertica?

Answer: HP — which botched Exadata 1 hardware — could do a good job with SAP HANA or other kinds of appliance products.

However:

• I don’t think trying to force Vertica beyond its natural growth — the way EMC is with Greenplum — is necessarily a good idea. Natural growth in Vertica’s case is plenty fast anyway.
• Obviously, making good Vertica hardware would be nice. But being hardware-independent is crucial to Vertica, not least because of cloud deployment, an option many buyers want to at least have in their hip pockets.

Question: You expressed some skepticism toward mobile BI/use cases. Why so?

Answer: The form factor hurts functionality a lot, so it’s only worthwhile in cases where timeliness is key.

And without more refined alert-setting functionality, it’s hard to think of that many cases.

Note: My views on mobile BI haven’t changed much since July, 2010.

Question: What about the idea of an enterprise being able to pay-per-drink to run jobs on an analytic cluster. Do you expect that concept to have any legs in 2012?

Answer: While other kinds of SaaS (Software as a Service) BI might make sense, remote computing BI that focuses on hardware cost sharing is problematic. Moving data in and out of the cluster is a big part of the overall cost, at least if you plan to process it only occasionally once it gets there. I haven’t seen a plan yet that gets around that point.

• This is a list of Monash Advantage members.
• All our vendor clients are Monash Advantage members, unless …
• … we work with them primarily in their capacity as technology users. (A large fraction of our user clients happen to be SaaS vendors.)
• We do not usually disclose our user clients.
• We do not usually disclose our venture capital clients, nor those who invest in publicly-traded securities.
• Included in the list below are two expired Monash Advantage members who haven’t said they will renew, as mentioned in my recent post on analyst bias. (You can probably imagine a couple of reasons for that obfuscation.)

With that said, our vendor client disclosures at this time are:

• Aster Data
• Cloudera
• CodeFutures/dbShards
• Couchbase
• EMC/Greenplum
• Endeca
• IBM/Netezza
• Infobright
• Intel
• MarkLogic
• ParAccel
• QlikTech
• salesforce.com/database.com
• SAND Technology
• SAP/Sybase
• Schooner Information Technology
• Skytide
• Splunk
• Teradata
• Vertica

That list includes the two I’m obfuscating, plus one more who just emailed to say a signed renewal contract is arriving this week. It does not include others who, less concretely, have said they will sign up soon.

Also, I guess there’s a bit of a gray area for Tableau. As far as I’m concerned, I’m doing an upcoming co-sponsored webinar just for Monash Advantage member Aster Data. Indeed, I declined to contract with or bill Tableau directly for its share,  because I had no good way to do that paperwork. But even so, Tableau is a cosponsor, was involved in the planning discussions and, behind the scenes, is surely footing part of the bill.

Statistics, data mining, machine learning, and/or predictive analytics. 

The more research-oriented aspects of business intelligence tools:

• Ad-hoc query.
• Drilldown.
• Most things done by BI-using “business analysts”
• Most things within BI called “data exploration.”

Analogous technologies as applied to non-tabular data types such as text or graph.

This will be be basis for my part of a webcast on March 10 at 11 am Pacific/2 pm Eastern time. The other main part of the webcast will be a demo by the webcast’s joint sponsors Aster Data and Tableau Software.

Some of Aster’s verbiage in describing and titling the webinar is so hyperbolic that I do not want to give the impression of endorsing it. But I am very hopeful that the webinar itself will be interesting and informative, and will point people at least somewhat in the direction of the benefits Aster is claiming.

Most of what I get paid for is in some form or other consulting. (The same would be true for many other analysts.) And so I can be a bit stingy with my advice toward non-clients. But my non-clients are a distinguished and powerful group, including in their number Oracle, IBM, Microsoft, and most of the BI vendors. So here’s a bit of advice for them too.

Oracle. On the plus side, you guys have been making progress against your reputation for untruthfulness. Oh, I’ve dinged you for some past slip-ups, but on the whole they’ve been no worse than other vendors.’ But recently you pulled a doozy. The analyst reports section of your website fails to distinguish between unsponsored and sponsored work.* That is a horrible ethical stumble. Fix it fast. Then put processes in place to ensure nothing that dishonest happens again for a good long time.

*Merv Adrian’s “report” listed high on that page is actually a sponsored white paper. That Merv himself screwed up by not labeling it clearly as such in no way exonerates Oracle. Besides, I’m sure Merv won’t soon repeat the error — but for Oracle, this represents a whole pattern of behavior.

Oracle. And while I’m at it, outright dishonesty isn’t your only unnecessary credibility problem. You’re also playing too many games in analyst relations.

HP. Neoview will never succeed. Admit it to yourselves. Go buy something that can. 

Smaller BI vendors. Analytic DBMS evaluations commonly include BI strategy and tool selection as well. If an analytic DBMS expert tells you he needs to learn more about your product line, don’t blow him off. In fact, you should be particularly embracing anybody who’s shown a fondness for small DBMS vendors; maybe he or his clients will like small BI vendors as well. That means (among others) Jaspersoft, Endeca, and Tableau.

Information Builders. Is there anything about your BI products that is in any way technologically differentiated? If so, you might want to mention some examples to somebody some time.

Kalido. I’ve said this to you before, but it bears repeating — your positioning translates to “I-CASE for analytics,” and that’s not a good thing. If your product is not as cumbersome and entrapping as that sounds, you need to do a much better job of explaining why not.

SenSage. You are what you are. Sell out while the selling is good. You don’t have the corporate personality to make it into the analytic DBMS mainstream on your own.

Ingres. You need to be more engaged with analysts than you are. Ingres navel-gazed too much 25 years ago, and evidently you haven’t outgrown it yet.

TIBCO. You probably have a lot of cool analytic technology, but I don’t know of an influencer who has much relationship with or trust in you. Rethink how you’re approaching influencer relations top to bottom.

Tableau. You had a lot of mindshare, but it’s fading. Do something.

MarkLogic, graph DBMS vendors, etc. You’re clinging too hard to the NoSQL label. Nobody is out there deciding among Cassandra, neo4j, and MarkLogic. They might be deciding between MongoDB and MarkLogic, I guess, but if you admit to yourself that’s all it is you’ll probably change your messaging somewhat.

Objectivity. Get real about marketing. Infinite Graph is a cool opportunity. But I didn’t even ping you for a meeting when I’m in your area next week, because I wouldn’t have known who to reach out to.

Everybody (especially Objectivity). “First X deployed in the cloud” is almost surely an inaccurate claim. Don’t make it. And by the way, even if it were true, it probably wouldn’t be interesting.

• 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.

So there are a whole lot of other worthy subjects I doubt I’ll be getting to any time soon.  In some cases, of course, other people are doing great jobs of writing about same. Here are pointers to a few links that I am glad to recommend:

• I wrote recently that I’ve discovered a number of different in-memory OLAP engines. Cindi Howson far outdid that, writing at length for Intelligent Enterprise on in-memory analytics, in an article that seems to itself be a teaser for a longer, free white paper on the subject.
• CouchDB posted an eye-catching, risque slide presentation promoting CouchDB and, more generally, key-value stores, at least for internet applications.  And yes, they’ve integrated MapReduce.
• Merv Adrian posted favorably about Birst, with special reference to its OEM efforts.  As previously noted, I was highly unimpressed with Birst’s end-user BI story at the time of its September roll-out, and Jerome Pineau’s recent examination did nothing to reassure me.  But perhaps OEM is a different matter.
• Merv also offers an interesting post about data integration upstart Expressor, and a highly favorable one about “visualization” vendor Tableau.
• Ann All interviewed Nigel Pendse, who grumped that BI features are overrated, and what end users really want is great query performance. I’m not so sure about the features side of that, but I’m hugely in agreement about the performance. That’s a big part of why the analytic DBMS industry is so vibrant. It’s also why in-memory OLAP is suddenly so hot.

• 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.