Analytic technologies

Discussion of technologies related to information query and analysis. Related subjects include:

November 15, 2014

Technical differentiation

I commonly write about real or apparent technical differentiation, in a broad variety of domains. But actually, computers only do a couple of kinds of things:

And hence almost all IT product differentiation fits into two buckets:

As examples of this reductionism, please consider:

Similar stories are true about application software, or about anything that has an API (Application Programming Interface) or SDK (Software Development Kit).

Yes, all my examples are in software. That’s what I focus on. If I wanted to be more balanced in including hardware or data centers, I might phrase the discussion a little differently — but the core points would still remain true.

What I’ve said so far should make more sense if we combine it with the observation that differentiation is usually restricted to particular domains. Read more

November 2, 2014

Notes on predictive modeling, November 2, 2014

Following up on my notes on predictive modeling post from three weeks ago, I’d like to tackle some areas of recurring confusion.

Why are we modeling?

Ultimately, there are two reasons to model some aspect of your business:

How precise do models need to be?

Use cases vary greatly with respect to the importance of modeling precision. If you’re doing an expensive mass mailing, 1% additional accuracy is a big deal. But if you’re doing root cause analysis, a 10% error may be immaterial.

Who is doing the work?

It is traditional to have a modeling department, of “data scientists” or SAS programmers as the case may be. While it seems cool to put predictive modeling straight in the hands of business users — some business users, at least — it’s rare for them to use predictive modeling tools more sophisticated than Excel. For example, KXEN never did all that well.

That said, I support the idea of putting more modeling in the hands of business users. Just be aware that doing so is still a small business at this time.

“Operationalizing” predictive models

The topic of “operationalizing” models arises often, and it turns out to be rather complex. Usually, to operationalize a model, you need: Read more

November 2, 2014

Analytics for lots and lots of business users

A common marketing theme in the 2010s decade has been to claim that you make analytics available to many business users, as opposed to your competition, who only make analytics available to (pick one):

Versions of this claim were also common in the 1970s, 1980s, 1990s and 2000s.

Some of that is real. In particular:

Even so, for most analytic tools, power users tend to be:

Asserting otherwise is rarely more than marketing hype.

Related link

October 26, 2014

Datameer at the time of Datameer 5.0

Datameer checked in, having recently announced general availability of Datameer 5.0. So far as I understood, Datameer is still clearly in the investigative analytics business, in that:

Key aspects include:

Read more

October 22, 2014

Is analytic data management finally headed for the cloud?

It seems reasonable to wonder whether analytic data management is headed for the cloud. In no particular order:

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

October 13, 2014

Context for Cloudera

Hadoop World/Strata is this week, so of course my clients at Cloudera will have a bunch of announcements. Without front-running those, I think it might be interesting to review the current state of the Cloudera product line. Details may be found on the Cloudera product comparison page. Examining those details helps, I think, with understanding where Cloudera does and doesn’t place sales and marketing focus, which given Cloudera’s Hadoop market stature is in my opinion an interesting thing to analyze.

So far as I can tell (and there may be some errors in this, as Cloudera is not always accurate in explaining the fine details):

In analyzing all this, I’m focused on two particular aspects:

Read more

October 10, 2014

Notes on predictive modeling, October 10, 2014

As planned, I’m getting more active in predictive modeling. Anyhow …

1. I still believe most of what I said in a July, 2013 predictive modeling catch-all post. However, I haven’t heard as much subsequently about Ayasdi as I had expected to.

2. The most controversial part of that post was probably the claim:

I think the predictive modeling state of the art has become:

  • Cluster in some way.
  • Model separately on each cluster.

In particular:

3. Nutonian is now a client. I just had my first meeting with them this week. To a first approximation, they’re somewhat like KXEN (sophisticated math, non-linear models, ease of modeling, quasi-automagic feature selection), but with differences that start: Read more

October 5, 2014

Spark vs. Tez, revisited

I’m on record as noting and agreeing with an industry near-consensus that Spark, rather than Tez, will be the replacement for Hadoop MapReduce. I presumed that Hortonworks, which is pushing Tez, disagreed. But Shaun Connolly of Hortonworks suggested a more nuanced view. Specifically, Shaun tweeted thoughts including:

Tez vs Spark = Apples vs Oranges.

Spark is general-purpose engine with elegant APIs for app devs creating modern data-driven apps, analytics, and ML algos.

Tez is a framework for expressing purpose-built YARN-based DAGs; its APIs are for ISVs & engine/tool builders who embed it

[For example], Hive embeds Tez to convert its SQL needs into purpose-built DAGs expressed optimally and leveraging YARN

That said, I haven’t yet had a chance to understand what advantages Tez might have over Spark in the use cases that Shaun relegates it to.

Related link

October 5, 2014

Streaming for Hadoop

The genesis of this post is that:

Of course, we should hardly assume that what the Hadoop distro vendors favor will be the be-all and end-all of streaming. But they are likely to at least be influential players in the area.

In the parts of the problem that Cloudera emphasizes, the main tasks that need to be addressed are: Read more

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