Tableau Software

Analysis of Tableau Software and its business intelligence products. Related subjects include:

July 20, 2014

Data integration as a business opportunity

A significant fraction of IT professional services industry revenue comes from data integration. But as a software business, data integration has been more problematic. Informatica, the largest independent data integration software vendor, does $1 billion in revenue. INFA’s enterprise value (market capitalization after adjusting for cash and debt) is $3 billion, which puts it way short of other category leaders such as VMware, and even sits behind Tableau.* When I talk with data integration startups, I ask questions such as “What fraction of Informatica’s revenue are you shooting for?” and, as a follow-up, “Why would that be grounds for excitement?”

*If you believe that Splunk is a data integration company, that changes these observations only a little.

On the other hand, several successful software categories have, at particular points in their history, been focused on data integration. One of the major benefits of 1990s business intelligence was “Combines data from multiple sources on the same screen” and, in some cases, even “Joins data from multiple sources in a single view”. The last few years before application servers were commoditized, data integration was one of their chief benefits. Data warehousing and Hadoop both of course have a “collect all your data in one place” part to their stories — which I call data mustering — and Hadoop is a data transformation tool as well.

Read more

September 29, 2013

Visualization or navigation?

I’ve suggested in the past, approximately, that the platform technology side of business intelligence is more significant than the user interface. That formulation, however, doesn’t exactly capture what I believe. To be more precise, let’s differentiate between a couple aspects of business intelligence UI.

It might seem that a lot of the action in business intelligence revolves around ever-better visualization. After all, Tableau is clearly identified as a visualization-centric technology; who’s hotter than Tableau? And numerous other vendors talk of “visualizations” too. But I don’t think that’s exactly right — rather, I see navigation as being a much bigger deal. And unlike most pure visualization, navigation usually depends strongly on underlying platform capabilities.

Examples of what I mean by innovative navigation — all of which have been developed or have gained prominence over the past decade or so — include:

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August 6, 2013

Hortonworks, Hadoop, Stinger and Hive

I chatted yesterday with the Hortonworks gang. The main subject was Hortonworks’ approach to SQL-on-Hadoop — commonly called Stinger —  but at my request we cycled through a bunch of other topics as well. Company-specific notes include:

Our deployment and use case discussions were a little confused, because a key part of Hortonworks’ strategy is to support and encourage the idea of combining use cases and workloads on a single cluster. But I did hear:

*By the way — Teradata seems serious about pushing the UDA as a core message.

Ecosystem notes, in Hortonworks’ perception, included:

I also asked specifically about OpenStack. Hortonworks is a member of the OpenStack project, contributes nontrivially to Swift and other subprojects, and sees Rackspace as an important partner. But despite all that, I think strong Hadoop/OpenStack integration is something for the indefinite future.

Hortonworks’ views about Hadoop 2.0 start from the premise that its goal is to support running a multitude of workloads on a single cluster. (See, for example, what I previously posted about Tez and YARN.) Timing notes for Hadoop 2.0 include:

Frankly, I think Cloudera’s earlier and necessarily incremental Hadoop 2 rollout was a better choice than Hortonworks’ later big bang, even though the core-mission aspect of Hadoop 2.0 is what was least ready. HDFS (Hadoop Distributed File System) performance, NameNode failover and so on were well worth having, and it’s more than a year between Cloudera starting supporting them and when Hortonworks is offering Hadoop 2.0.

Hortonworks’ approach to doing SQL-on-Hadoop can be summarized simply as “Make Hive into as good an analytic RDBMS as possible, all in open source”. Key elements include:  Read more

July 31, 2013

“Disruption” in the software industry

I lampoon the word “disruptive” for being badly overused. On the other hand, I often refer to the concept myself. Perhaps I should clarify. :)

You probably know that the modern concept of disruption comes from Clayton Christensen, specifically in The Innovator’s Dilemma and its sequel, The Innovator’s Solution. The basic ideas are:

In response (this is the Innovator’s Solution part):

But not all cleverness is “disruption”.

Here are some of the examples that make me think of the whole subject. Read more

March 24, 2013

Essential features of exploration/discovery BI

If I had my way, the business intelligence part of investigative analytics — i.e. , the class of business intelligence tools exemplified by QlikView and Tableau — would continue to be called “data exploration”. Exploration what’s actually going on, and it also carries connotations of the “fun” that users report having with the products. By way of contrast, I don’t know what “data discovery” means; the problem these tools solve is that the data has been insufficiently explored, not that it hasn’t been discovered at all. Still “data discovery” seems to be the term that’s winning.

Confusingly, the Teradata Aster library of functions is now called “Discovery” as well, although thankfully without the “data” modifier. Further marketing uses of the term “discovery” will surely follow.

Enough terminology. What sets exploration/discovery business intelligence tools apart? I think these products have two essential kinds of feature:

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March 18, 2013

DBMS development and other subjects

The cardinal rules of DBMS development

Rule 1: Developing a good DBMS requires 5-7 years and tens of millions of dollars.

That’s if things go extremely well.

Rule 2: You aren’t an exception to Rule 1. 

In particular:

DBMS with Hadoop underpinnings …

… aren’t exceptions to the cardinal rules of DBMS development. That applies to Impala (Cloudera), Stinger (Hortonworks), and Hadapt, among others. Fortunately, the relevant vendors seem to be well aware of this fact. Read more

October 24, 2012

Introduction to Cirro

Stuart Frost, of DATAllegro fame, has started a small family of companies, and they’ve become my clients sort of as a group. The first one that I’m choosing to write about is Cirro, for which the basics are:

Data federation stories are often hard to understand because, until you drill down, they implausibly sound as if they do anything for everybody. That said, it’s reasonable to think of Cirro as a layer between Hadoop and your BI tool that:

In both cases, Cirro is calling on your data management software for help, RDBMS or Hadoop as the case may be.

More precisely, Cirro’s approach is: Read more

October 15, 2012

What is meant by “iterative analytics”

A number of people and companies are using the term “iterative analytics”. This is confusing, because it can mean at least three different things:

  1. You analyze something quickly, decide the result is not wholly satisfactory, and try again. Examples might include:
    • Aggressive use of drilldown, perhaps via an advanced-interface business intelligence tool such as Tableau or QlikView.
    • Any case where you run a query or a model, think about the results, and run another one after that.
  2. You develop an intermediate analytic result, and using it as input to the next round of analysis.  This is roughly equivalent to saying that iterative analytics refers to a multi-step analytic process involving a lot of derived data.
  3. #1 and #2 conflated/combined. This is roughly equivalent to saying that iterative analytics refers to all of to investigative analytics, sometimes known instead as exploratory analytics.

Based both on my personal conversations and a quick Google check, it’s reasonable to say #1 and #3 seem to be the most common usages, with #2 trailing a little bit behind.

But often it’s hard to be sure which of the various possible meanings somebody has in mind.

Related links

Monash’s First and Third Laws of Commercial Semantics state:

March 31, 2012

Our clients, and where they are located

From time to time, I disclose our vendor client lists. Another iteration is below, the first since a little over a year ago. To be clear:

For reasons explained below, I’ll group the clients geographically. Obviously, companies often have multiple locations, but this is approximately how it works from the standpoint of their interactions with me. Read more

February 21, 2012

The 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms — company-by-company comments

This is one of a series of posts on business intelligence and related analytic technology subjects, keying off the 2011/2012 version of the Gartner Magic Quadrant for Business Intelligence Platforms. The four posts in the series cover:

The heart of Gartner Group’s 2011/2012 Magic Quadrant for Business Intelligence Platforms was the company comments. I shall expound upon some, roughly in declining order of Gartner’s “Completeness of Vision” scores, dubious though those rankings may be.  Read more

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