The Gartner Magic Quadrant for BI is a good place to start when looking at the rich field of players in the BI space. The usual suspects are always there – IBM, Oracle, Microsoft, Microstrategy etc. but is is always interesting to look at tools that are less well-known, or fall outside the upper-right quadrant that everyone seems to aspire to (and judge products by – as a side note I’m thinking about “The Tyranny of The Upper Right Quadrant” as a subject for a future post.)
Tableau is an interesting OLAP-type analytical product that that falls in the upper-left quadrant, qualifying it as a “challenger” in Gartner-speak. But those that like it like it a lot – Gartner goes on to describe it as “The ‘sweetheart’ of the quadrant.” Apparently customers love this product.
I took a quick look at the desktop version of the software, which is offered as a fully-functional 14 day trial.
Tableau has an interesting history. It was started by folks with a strong interest in data visualization. From the beginning Tableau was positioned as a tool that would enable fast visual representations of data (original founders included a founding member of Pixar.) Tableau advertises itself as “a stunning alternative to traditional business intelligence”, attempting to carve out a niche in an area that Cognos, for example, has traditionally not been great at (in my opinion visualization has always been clumsy in tools even as advanced as Report Studio.)
Another area Tableau claims to excel in is in raw speed – “Bring your data into Tableau’s high performance data engine and work with it at blazing speed. And do it with a click—there’s no programming required. Tableau turns millions of rows of data into answers at the speed of thought.” goes the sales pitch. No “programming” required, but definitely some thinking.
When you start doing analysis with Tableau you are offered the ability to connect to a wide, impressive range of data sources. These include Excel, the usual commercial databases etc, but also open-source favourites MySQL and PostgreSQL. As well, there is an option to connect to Cloudera HADOOP Hive. Tableau is plainly positioning itself for “Big Data”-type analysis.
When you select a relational-type data source, such as Microsoft SQL Server, you have the option to select one or more tables, and establish their joins using a series of dialog boxes. From a data-modelling perspective this interface feels a bit awkward, but it gets the job done at the desktop-level…
… and with clearly-defined keys and a simple data model this shouldn’t present data-savy users with much of a problem – more on this below.
Next we have the option of either connecting directly to a the data source, or importing the data into a native Tableau format:
This is where it gets interesting. I created a MS SQL Server database consisting of 10000 customers, 50 products, and 100 million sales rows - a very simple model, but a large overall size for my hamster-powered laptop. I then created a MS Analysis Services cube to play with. However, working from the relational model, a user can connect to the database and importing this directly Tableau’s native format – according to Gartner a column-oriented in-memory data engine. On my admittedly underpowered laptop this took a couple of hours, but performance when querying the imported data was quite impressive – it seemed at least as fast as the Analysis Services cube. This isn’t sophisticated benchmarking, but indicates that Tableau’s engine definitely has some power. Using this feature assumes that the user is comfortable arranging the hierarchies of the data themselves, instead of having a modeller do it for them in a cube.
This approach reveals something about critical about Tableau’s market – this tool is meant for people who are comfortable with the world of databases and OLAP-style structures, and for whom creating joins, hierarchies and all the rest is a natural part of the way they think about the data – but who are also the very people interested in analyzing their data. The database, the joins, the model – all of this is a means to an end, carried out, at least to some degree, by the analysts themselves. This hints at Wayne Eckerson’s observation that real analysis is often a bottom-up process, with savvy folks in the business using the powerful tools now available to them to “end run” the IT department. This tool essentially builds-in a kind of ETL between a database and a proprietary analytical structure. This isn’t mandatory, of course, and connecting to my Analysis Services cube was quite easy and natural, but this is something to think about.
As expected, visualizations are where Tableau excels. The “Show Me” tab gives the user a number of visualization options, with hints as to what is appropriate for what kind of data.
Many of the visualizations available are quite useful – for example, below I am able to visually locate a customer who is “Tier 1”, but has very low sales. Arranging this display tool seconds:
Tableau offers the user the ability to connect simultaneously to multiple data sources. Here I have 2 data sources in the “Data” tab. Contrast this with the approach Cognos takes, where multiple data sources are put together in a package that hides this from the user. Once again, the idea is that the user knows the data (and how it relates) well enough to perform these kinds of tasks – but the user can act quickly to select the data sources they want and combine them as he or she sees fit.
Digging into all of Tableau’s features is beyond the scope of this post, but this is definitely a thought-provoking product. The BI world seems to be in a never-ending struggle between quick, user-oriented tools and the more controlled, but less agile, enterprise-grade BI suites. Tableau seems to be positioning itself as a product for the highly competent analyst in a relatively small organization – or a small part of a large organization. Gartner provides some insight here: “Tableau’s products often fill an unmet need in organizations that already have a BI standard, and are frequently deployed as a complementary capability to an existing BI platform. Tableau is still less likely to be considered an enterprise BI standard than the products of most other vendors.” Tableau is not a general-purpose reporting tool – it is an analysis tool, for analysts.
#1 by Harsh Vardhan on November 2, 2012 - 4:19 pm
Well said! Who needs an enterprise software? The only 2 things that people care right now is – Simplicity and cost effectiveness. No one uses every functionality of a tool. If anything is lacking, there is always a workaround. Business Analytics is not a buzz word any more and you can’t compete using a price differentiation strategy. Cheaper, Faster and Better – 3 Mantras in order…If your’s is cheaper and faster, not many would care if you are not that better…
#2 by Anand on March 29, 2013 - 6:10 am
I found this article when i would like to see how the BI World is looking cognos Vs Tableau. In my experience i pre-dominently worked in cognos and just started to work in the Tableau. I see the tableau as a self serviced data visualization and representation tool rather than standard reporting tool. In cognos also we have self serviced tool like query studio and analysi studio. But compared to tableau, it has most powerful and ‘beautiful’ features.Here analysis reports and dashbords can be done in minutes but the same thing takes days in cognos. By tableau data extract we can work disconnected from the server also. Active reports in cognos gives that fuinctionality but features this gives not at the level of tableau. but in tableau users should have the knowledge about the underlying database tables or whatever they are using as source. If the users world start analyzing their data on the go in tableau kind of tool , i am afraid what other enterprise standard report tools would do and it impacts technical report developers also .