Kellblog

Here’s a great presentation on this topic that I picked up courtesy of The Content Wrangler.

I had a long overdue lunch the other day with former Business Objects co-worker, and one of the smartest people I know, Alex Moissis. While most people you talk to in Silicon Valley compare software industry evolution today to evolution of the automobile industry in the early 20th century, Alex had a different view.

He thinks you should compare Oracle not to General Motors, but to ITT — i.e., to the conglomerates of the 1970s.

• Conglomerates were built through acquisition, at sometimes pricey multiples.
• They did so largely for size’s sake.
• Their leaders were incented to keep getting bigger.
• While there were arguably scale economies to be had, they were generally not realized, nor did they prove compelling compared to the disadvantages of the conglomerate model.
• In the end, they were largely broken up.

It’s an interesting viewpoint and well grounded in reality. When I talk to my friends at the new behemoths, I don’t see any signs of any real product integration and/or discontinuation coming anytime soon (e.g., next 5 years) nor do I see any obvious scale economies. In fact, when I talk to friends in two different divisions of Oracle, it’s more like talking to people at different companies than anything else.

So are we witnessing a consolidation a la the early automobile industry or the growth of conglomerates a la ITT?

My take is that while history never exactly repeats itself that I would predict that a lot of products / companies do get spun back out of the behemoths before the movie ends. And you can’t forget that the behemoths themselves are being disrupted at three-levels

• Technologically by startups. The cost of being a behemoth is that you are so buried in integration road maps that innovation gets stalled.

• Price-wise by open source. MySQL, SugarCRM, Lucene, even Ingres all seem to be chipping away and moving up against their enterprise counterparts.

• Business-model-wise by SaaS and Google. Companies don’t employ electricians today, will they employ IT staffs to do basic operational systems (e.g., HR, CRM, ERP) tomorrow? Or will they just configure multi-tenant SaaS apps and focus their technology investments in R&D — as Geoffrey Moore would say, invest IT resource in core, not context.

For more on the history of conglomerates, Alex directed me here.

Fast Search and Transfer announced their Q3 2007 results on 10/30. Here are some highlights from the announcement, some of which (the net loss, for example) aren’t actually in the company’s press release.

• Revenues of $35.6M, down 16% compared to Q3 2006 revenues of $42.5M
• Operating expenses of $121.2M, up 246% over Q3 2006 operating expenses of $49.2M
• Net loss of $100.4M, up 2200% over the net loss of $4.5M in Q3 2006.
• Cash burn of $57.9M
• They increased guidance for 4Q 07 from $43M to $47M

I’ve not had time to read everything in detail yet, but I’m sure there are lots of one-time restructuring charges in the $121M of operating expense. Fast goes to quite some length to explain why all this is good news. But to me, the numbers are numbers.

As a bit of commentary, I find it a little odd when a company’s earning press release doesn’t include the financial statements. But a lot of people do it. However, I find it quite odd when you press the link to the financial statements (which wasn’t easy to locate) and find something other than, well, the financial statements. In this case, you find a what I’d consider a veritable Q3 07 “brochure” with a few well chosen and well framed financial metrics on the first page, several pages of good news, (carefully) selected metrics and commentary, and a few high-and-to-the-right arrows, boasting 4%, 5%, and even 23% growth rates.

In fact, there’s so much pre-material in the financial statements, that you might get weary wading through it before you get to page 6 and finally find the income statement.

Hey, perhaps that’s the point.

I am a big fan of Paul Kedrosky’s Infectious Greed blog. He’s clearly a data junkie, finds some remarkable pieces of data and representations of them, and makes some great, non-intuitive connections across different data sets and applications.

While I did not attend his speech yesterday in San Francisco, I did pick up this very interesting and very topical set of slides off his blog.

If you go to Slideshare, you can see a bit of of the (very necessary) voice-over in the comments. I love the slides on venture returns, the increasingly correlation of hedge-fund strategies, the dearth of .400 hitters in baseball, and the examples of Web 2.0 collaboration and communication during the Southern California fires.

I picked up via this post on the High Scalability Blog a new paper by Michael Stonebraker, Nabil Hachem, and Pat Helland entitled The End of an Era (It’s Time for a Complete Rewrite) presented at the VLDB 2007 conference in Austria on September 23rd through 27th.

From the paper’s summary:

“In the last quarter of a century, there has been a dramatic shift in:

1. DBMS markets: from business data processing to a collection of markets, with varying requirements
2. Necessary features: new requirements include shared nothing support and high availability
3. Technology: large main memories, the possibility of hot standbys, and the web change most everything

The result is:

1. The predicted demise of “one size fits all”
2. The inappropriateness of current relational implementations for any segment of the market
3. The necessity of rethinking both data models and query languages for the specialized engines, which we expect to be dominant in the various vertical markets”

As you know, I’m a big believer in the special-purpose DBMS meme. Any database historian knows what Codd was thinking, and more importantly — what he wasn’t — when he designed the relational model. Again, from the paper:

“Ted Codd’s idea of normalizing data into flat tables has served our community well over the subsequent 30 years. However, there are now other markets, whose needs must be considered. These include data warehouses, web-oriented search, real-time analytics, and semi-structured data markets.”

The complete paper can be found here.