Tableau Software

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

June 14, 2017

The data security mess

A large fraction of my briefings this year have included a focus on data security. This is the first year in the past 35 that that’s been true.* I believe that reasons for this trend include:

*Not really an exception: I did once make it a project to learn about classic network security, including firewall appliances and so on.

Certain security requirements, desires or features keep coming up. These include (and as in many of my lists, these overlap):

More specific or extreme requirements include:  Read more

April 13, 2017

Analyzing the right data

0. A huge fraction of what’s important in analytics amounts to making sure that you are analyzing the right data. To a large extent, “the right data” means “the right subset of your data”.

1. In line with that theme:

2. Business intelligence interfaces today don’t look that different from what we had in the 1980s or 1990s. The biggest visible* changes, in my opinion, have been in the realm of better drilldown, ala QlikView and then Tableau. Drilldown, of course, is the main UI for business analysts and end users to subset data themselves.

*I used the word “visible” on purpose. The advances at the back end have been enormous, and much of that redounds to the benefit of BI.

3. I wrote 2 1/2 years ago that sophisticated predictive modeling commonly fit the template:

That continues to be tough work. Attempts to productize shortcuts have not caught fire.

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

July 7, 2015

Zoomdata and the Vs

Let’s start with some terminology biases:

So when my clients at Zoomdata told me that they’re in the business of providing “the fastest visual analytics for big data”, I understood their choice, but rolled my eyes anyway. And then I immediately started to check how their strategy actually plays against the “big data” Vs.

It turns out that:

*The HDFS/S3 aspect seems to be a major part of Zoomdata’s current story.

Core aspects of Zoomdata’s technical strategy include:  Read more

May 13, 2015

Notes on analytic technology, May 13, 2015

1. There are multiple ways in which analytics is inherently modular. For example:

Also, analytics is inherently iterative.

If I’m right that analytics is or at least should be modular and iterative, it’s easy to see why people hate multi-year data warehouse creation projects. Perhaps it’s also easy to see why I like the idea of schema-on-need.

2. In 2011, I wrote, in the context of agile predictive analytics, that

… the “business analyst” role should be expanded beyond BI and planning to include lightweight predictive analytics as well.

I gather that a similar point is at the heart of Gartner’s new term citizen data scientist. I am told that the term resonates with at least some enterprises.  Read more

April 9, 2015

Which analytic technology problems are important to solve for whom?

I hear much discussion of shortfalls in analytic technology, especially from companies that want to fill in the gaps. But how much do these gaps actually matter? In many cases, that depends on what the analytic technology is being used for. So let’s think about some different kinds of analytic task, and where they each might most stress today’s available technology.

In separating out the task areas, I’ll focus first on the spectrum “To what extent is this supposed to produce novel insights?” and second on the dimension “To what extent is this supposed to be integrated into a production/operational system?” Issues of latency, algorithmic novelty, etc. can follow after those. In particular, let’s consider the tasks: Read more

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:

Read more

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

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