Tableau Software
Analysis of Tableau Software and its business intelligence products. Related subjects include:
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:
- Query modification.
- Query result revisualization.*
| Categories: Business intelligence, Endeca, Memory-centric data management, QlikTech and QlikView, Tableau Software | 8 Comments |
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:
- Concurrent workloads benchmarked in the lab are poor predictors of concurrent performance in real life.
- Mixed workload management is harder than you’re assuming it is.
- Those minor edge cases in which your Version 1 product works poorly aren’t minor after all.
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
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:
- Cirro does data federation for analytics.
- Cirro has 10 full-time people plus 4 part-timers.
- Cirro launched its product in June.
- Cirro doesn’t have customers yet, but hopes to fix that soon.
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:
- Helps with data transformations.
- Helps join Hadoop data to relational tables, even if the joins are large ones.
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
| Categories: Business intelligence, Cirro, Data integration and middleware, Hadoop, MapReduce, Tableau Software | 5 Comments |
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:
- 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.
- 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.
- #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:
| Categories: Analytic technologies, Business intelligence, QlikTech and QlikView, Tableau Software | 3 Comments |
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:
- This is a list of Monash Advantage members.
- All our vendor clients are Monash Advantage members, unless …
- … we work with them primarily in their capacity as technology users. (A large fraction of our user clients happen to be SaaS vendors.)
- We do not usually disclose our user clients.
- We do not usually disclose our venture capital clients, nor those who invest in publicly-traded securities.
- Excluded from this round of disclosure is one vendor I have never written about.
- Included in this round of disclosure is one client paying for services partly in stock. All our other clients are cash-only.
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
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:
- Overview comments about the 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms, as well as a link to the actual document.
- Business intelligence industry trends — some of Gartner’s thoughts but mainly my own.
- (This post) Company-by-company comments based on the 2011/2012 Gartner Magic Quadrant for Business Intelligence Platforms.
- Third-party analytics, pulling together and expanding on some points I made in the first three posts.
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
Analytic trends in 2012: Q&A
As a new year approaches, it’s the season for lists, forecasts and general look-ahead. Press interviews of that nature have already begun. And so I’m working on a trilogy of related posts, all based on an inquiry about hot analytic trends for 2012.
This post is a moderately edited form of an actual interview. Two other posts cover analytic trends to watch (planned) and analytic vendor execution challenges to watch (already up).
Updating our vendor client disclosures
Edit: This disclosure has been superseded by a March, 2012 version.
From time to time, I disclose our vendor client lists. Another iteration is below. To be clear:
- This is a list of Monash Advantage members.
- All our vendor clients are Monash Advantage members, unless …
- … we work with them primarily in their capacity as technology users. (A large fraction of our user clients happen to be SaaS vendors.)
- We do not usually disclose our user clients.
- We do not usually disclose our venture capital clients, nor those who invest in publicly-traded securities.
- Included in the list below are two expired Monash Advantage members who haven’t said they will renew, as mentioned in my recent post on analyst bias. (You can probably imagine a couple of reasons for that obfuscation.)
With that said, our vendor client disclosures at this time are:
- Aster Data
- Cloudera
- CodeFutures/dbShards
- Couchbase
- EMC/Greenplum
- Endeca
- IBM/Netezza
- Infobright
- Intel
- MarkLogic
- ParAccel
- QlikTech
- salesforce.com/database.com
- SAND Technology
- SAP/Sybase
- Schooner Information Technology
- Skytide
- Splunk
- Teradata
- Vertica
Upcoming webinar on investigative analytics
I recently coined the phrase investigative analytics to conflate
- Statistics, data mining, machine learning, and/or predictive analytics.
- The more research-oriented aspects of business intelligence tools:
- Ad-hoc query.
- Drilldown.
- Most things done by BI-using “business analysts”
- Most things within BI called “data exploration.”
- Analogous technologies as applied to non-tabular data types such as text or graph.
This will be be basis for my part of a webcast on March 10 at 11 am Pacific/2 pm Eastern time. The other main part of the webcast will be a demo by the webcast’s joint sponsors Aster Data and Tableau Software.
Some of Aster’s verbiage in describing and titling the webinar is so hyperbolic that I do not want to give the impression of endorsing it. But I am very hopeful that the webinar itself will be interesting and informative, and will point people at least somewhat in the direction of the benefits Aster is claiming.
| Categories: Analytic technologies, Aster Data, Business intelligence, Data warehousing, Presentations, Tableau Software | 3 Comments |
Advice for some non-clients
Edit: Any further anonymous comments to this post will be deleted. Signed comments are permitted as always.
Most of what I get paid for is in some form or other consulting. (The same would be true for many other analysts.) And so I can be a bit stingy with my advice toward non-clients. But my non-clients are a distinguished and powerful group, including in their number Oracle, IBM, Microsoft, and most of the BI vendors. So here’s a bit of advice for them too.
Oracle. On the plus side, you guys have been making progress against your reputation for untruthfulness. Oh, I’ve dinged you for some past slip-ups, but on the whole they’ve been no worse than other vendors.’ But recently you pulled a doozy. The analyst reports section of your website fails to distinguish between unsponsored and sponsored work.* That is a horrible ethical stumble. Fix it fast. Then put processes in place to ensure nothing that dishonest happens again for a good long time.
*Merv Adrian’s “report” listed high on that page is actually a sponsored white paper. That Merv himself screwed up by not labeling it clearly as such in no way exonerates Oracle. Besides, I’m sure Merv won’t soon repeat the error — but for Oracle, this represents a whole pattern of behavior.
Oracle. And while I’m at it, outright dishonesty isn’t your only unnecessary credibility problem. You’re also playing too many games in analyst relations.
HP. Neoview will never succeed. Admit it to yourselves. Go buy something that can. Read more