Data mart outsourcing

                                          Discussion of services that analyze large databases on an outsourced basis. Related subjects include:

                                          October 3, 2016

                                          Notes on the transition to the cloud

                                          1. The cloud is super-hot. Duh. And so, like any hot buzzword, “cloud” means different things to different marketers. Four of the biggest things that have been called “cloud” are:

                                          Further, there’s always the idea of hybrid cloud, in which a vendor peddles private cloud systems (usually appliances) running similar technology stacks to what they run in their proprietary public clouds. A number of vendors have backed away from such stories, but a few are still pushing it, including Oracle and Microsoft.

                                          This is a good example of Monash’s Laws of Commercial Semantics.

                                          2. Due to economies of scale, only a few companies should operate their own data centers, aka true on-prem(ises). The rest should use some combination of colo, SaaS, and public cloud.

                                          This fact now seems to be widely understood.

                                          Read more

                                          October 26, 2015

                                          Differentiation in business intelligence

                                          Parts of the business intelligence differentiation story resemble the one I just posted for data management. After all:

                                          That said, insofar as BI’s competitive issues resemble those of DBMS, they are those of DBMS-lite. For example:

                                          And full-stack analytic systems — perhaps delivered via SaaS (Software as a Service) — can moot the BI/data management distinction anyway.

                                          Of course, there are major differences between how DBMS and BI are differentiated. The biggest are in user experience. I’d say: Read more

                                          October 22, 2014

                                          Snowflake Computing

                                          I talked with the Snowflake Computing guys Friday. For starters:

                                          Much of the Snowflake story can be summarized as cloud/elastic/simple/cheap.*

                                          *Excuse me — inexpensive. Companies rarely like their products to be labeled as “cheap”.

                                          In addition to its purely relational functionality, Snowflake accepts poly-structured data. Notes on that start:

                                          I don’t know enough details to judge whether I’d call that an example of schema-on-need.

                                          A key element of Snowflake’s poly-structured data story seems to be lateral views. I’m not too clear on that concept, but I gather: Read more

                                          September 21, 2014

                                          Data as an asset

                                          We all tend to assume that data is a great and glorious asset. How solid is this assumption?

                                          *”Our assets are our people, capital and reputation. If any of these is ever diminished, the last is the most difficult to restore.” I love that motto, even if Goldman Sachs itself eventually stopped living up to it. If nothing else, my own business depends primarily on my reputation and information.

                                          This all raises the idea — if you think data is so valuable, maybe you should get more of it. Areas in which enterprises have made significant and/or successful investments in data acquisition include:? Read more

                                          November 24, 2013

                                          Thoughts on SaaS

                                          Generalizing about SaaS (Software as a Service) is hard. To prune some of the confusion, let’s start by noting:

                                          For smaller enterprises, the core outsourcing argument is compelling. How small? Well:

                                          So except for special cases, an enterprise with less than $100 million or so in revenue may have trouble affording on-site data processing, at least at a mission-critical level of robustness. It may well be better to use NetSuite or something like that, assuming needed features are available in SaaS form.*

                                          Read more

                                          December 9, 2012

                                          Amazon Redshift and its implications

                                          Merv Adrian and Doug Henschen both reported more details about Amazon Redshift than I intend to; see also the comments on Doug’s article. I did talk with Rick Glick of ParAccel a bit about the project, and he noted:

                                          “We didn’t want to do the deal on those terms” comments from other companies suggest ParAccel’s main financial take from the deal is an already-reported venture investment.

                                          The cloud-related engineering was mainly around communications, e.g. strengthening error detection/correction to make up for the lack of dedicated switches. In general, Rick seemed more positive on running in the (Amazon) cloud than analytic RDBMS vendors have been in the past.

                                          So who should and will use Amazon Redshift? For starters, I’d say: Read more

                                          June 16, 2012

                                          Introduction to Metamarkets and Druid

                                          I previously dropped a few hints about my clients at Metamarkets, mentioning that they:

                                          But while they’re a joy to talk with, writing about Metamarkets has been frustrating, with many hours and pages of wasted of effort. Even so, I’m trying again, in a three-post series:

                                          Much like Workday, Inc., Metamarkets is a SaaS (Software as a Service) company, with numerous tiers of servers and an affinity for doing things in RAM. That’s where most of the similarities end, however, as? Metamarkets is a much smaller company than Workday, doing very different things.

                                          Metamarkets’ business is SaaS (Software as a Service) business intelligence, on large data sets, with low latency in both senses (fresh data can be queried on, and the queries happen at RAM speed). As you might imagine, Metamarkets is used by digital marketers and other kinds of internet companies, whose data typically wants to be in the cloud anyway. Approximate metrics for Metamarkets (and it may well have exceeded these by now) include 10 customers, 100,000 queries/day, 80 billion 100-byte events/month (before summarization), 20 employees, 1 popular CEO, and a metric ton of venture capital.

                                          To understand how Metamarkets’ technology works, it probably helps to start by realizing: Read more

                                          May 1, 2012

                                          Thinking about market segments

                                          It is a reasonable (over)simplification to say that my business boils down to:

                                          One complication that commonly creeps in is that different groups of users have different buying practices and technology needs. Usually, I nod to that point in passing, perhaps by listing different application areas for a company or product. But now let’s address it head on. Whether or not you care about the particulars, I hope the sheer length of this post reminds you that there are many different market segments out there.

                                          Last June I wrote:

                                          In almost any IT decision, there are a number of environmental constraints that need to be acknowledged. Organizations may have standard vendors, favored vendors, or simply vendors who give them particularly deep discounts. Legacy systems are in place, application and system alike, and may or may not be open to replacement. Enterprises may have on-premise or off-premise preferences; SaaS (Software as a Service) vendors probably have multitenancy concerns. Your organization can determine which aspects of your system you’d ideally like to see be tightly integrated with each other, and which you’d prefer to keep only loosely coupled. You may have biases for or against open-source software. You may be pro- or anti-appliance. Some applications have a substantial need for elastic scaling. And some kinds of issues cut across multiple areas, such as budget, timeframe, security, or trained personnel.

                                          I’d further say that it matters whether the buyer:

                                          Now let’s map those considerations (and others) to some specific market segments. Read more

                                          March 26, 2012

                                          Notes on the ClearStory Data launch, including an inaccurate quote from me

                                          ClearStory Data launched, with nice coverage in the New York Times, Computerworld, and elsewhere. But from my standpoint, there were some serious problems:

                                          I’m utterly disgusted with this whole mess, although after talking with her a lot I’m fine with CEO Sharmila Mulligan’s part in it, which is to say with ClearStory’s part in general.

                                          *I avoid the term “platform” as much as possible; indeed, I still don’t really know what the “new platforms” part was supposed to refer to. The Frankenquote wound up with some odd grammar as well.

                                          Actually, in principle I’m a pretty close adviser to ClearStory (for starters, they’re one of my stealth-mode clients). That hasn’t really ramped up yet; in particular, I haven’t had a technical deep dive. So for now I’ll just say:

                                          Read more

                                          February 11, 2012

                                          Applications of an analytic kind

                                          The most straightforward approach to the applications business is:

                                          However, this strategy is not as successful in analytics as in the transactional world, for two main reasons:

                                          I first realized all this about a decade ago, after Henry Morris coined the term analytic applications and business intelligence companies thought it was their future. In particular, when Dave Kellogg ran marketing for Business Objects, he rattled off an argument to the effect that Business Objects had generated more analytic app revenue over the lifetime of the company than Cognos had. I retorted, with only mild hyperbole, that the lifetime numbers he was citing amounted to “a bad week for SAP”. Somewhat hoist by his own petard, Dave quickly conceded that he agreed with my skepticism, and we changed the subject accordingly.

                                          Reasons that analytic applications are commonly less complete than the transactional kind include: Read more

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