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The market leader in self-service data discovery and visualisation, Tableau 9.2 continues to improve and delight with plenty of enhancements to help keep the competition at bay.
Support for a wide range of data sources
Intuitive graphical interface
Enhanced data filtering and preparation tools
Comprehensive training and support ecosystem
Open-source R integration
Newcomers face an appreciable learning curve
Limited predictive analytics
From $999 per user for the Personal Edition or $1,999 for the Professional Edition
The market for Business Intelligence (BI) and data analytics has evolved rapidly in the last few years, moving away from a top-down, IT-led approach in favour of user-driven data discovery and visualisation. Tableau has been a prime mover behind this trend, with a clear focus on easy-to-use data visualisation that has pushed it to the top of the self-service analytics tree — a position it's looking to consolidate as a raft of new players seek a share of the burgeoning data analytics market.
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Tableau is now in its ninth incarnation: we tested version 9.2, released in January, using the main Tableau Desktop application, which is available for both Windows and Mac OS. Personal and Professional editions are available here, the main difference being the number and type of data sources supported, with the cheaper personal edition limited to just six, including simple CSV files, Excel spreadsheets and Access databases. This rises to over 40 with the Professional Edition, adding connectors for all the leading SQL databases plus data held by popular cloud-based services such as Microsoft Azure, SAP and Salesforce.com.
Preparing the data
Multiple data sources can be connected to Tableau worksheets and joins created between tables. Moreover, the software can automatically cope with a host of exceptions both in how data sources are structured and the content itself. The rows and columns in a spreadsheet, for example, may need to be swapped, or there may be graphics and other information ahead of column headings which, rather than have to go back to the source and fix them there, can be filtered out by Tableau using its own built-in Data Interpreter.
Tableau will also allow you to split date and address fields, create new calculated fields, work out latitude and longitude from addresses for use with geographical charts, and pivot specific rows and columns at any time during an analysis. Added to all this, you can perform ad-hoc calculations on data using simple expressions much like formulae in a spreadsheet. Note, however, that the automatic data interpretation tools are aimed mostly at spreadsheets and may struggle with larger data sets.
Drag and go
Once connected to your data sources you can really get to work, a process that begins by dragging and dropping the connected columns and rows onto a worksheet, moving them about, drilling down and applying a variety of tools to better understand the data through visualisation.
To begin with, this can be a very haphazard process. It can also be confusing and we were often left puzzled by the results we got. However, changes can be quickly undone and reapplied and expert help is also on hand in the form of a 'Show Me' tool — simply select the fields to be analysed and this will automatically list the visualizations that best match the data, as in the example below:
Choose one of these suggestions and the required fields are automatically added in the right places on the worksheet and the visualisation created for you. This is shown in the next screenshot, where a geographic symbol map has been selected to show sales in countries across the EU:
It's very much a matter of experimenting to get the required results:there are lots of options to manipulate, order and create charts from your data. The latest release also includes analysis tools to, for example, identify trends and generate forecasts that are, again, very easy to apply — although you do need a reasonable grasp of statistics to get the best out of them and they may be beyond the scope of a lot of users. That said, Tableau isn't just for end users. It can also be used by specialist data analysts to create applications for less-technical staff, with options to create not just worksheets, but also interactive dashboards comprising multiple worksheets, text, images and web pages, plus narrated 'stories' to walk users through presentations of one or more worksheets or dashboards.
Visualisations (or Vizzies in Tableau jargon) can also be embedded in web pages; another advanced option is the ability to integrate analyses built using the open-source R language, which is popular with professional data scientists. These can be called directly and Tableau tools used to visualise the results.
Beyond the desktop
When it comes to distributing and sharing visualisations, Tableau Desktop can create packages for use with the free Tableau Reader, with the inevitable mobile apps another option for Apple and Android device users — although an app for Android smartphones has yet to be released.
Beyond that, users of the Professional edition can publish and share their workbooks, dashboards and analyses via Tableau Server. Designed for in-house deployment, this includes tools to directly create and edit visualisations, although these are limited in functionality and do not replace the need for Desktop licenses.
A web-hosted version of the server product is also available (Tableau Online) accessible via a browser, with a more comprehensive SaaS implementation planned for the future to, ultimately, enable Tableau customers to build and run analyses without the need for the local Desktop application. This is much like the existing free web-based Tableau Public service that allows anyone to create visualisations for sharing online, but with a lot more features and the ability to create and manage local workbooks.
The Tableau learning curve
Overall we were impressed with what Tableau 9.2 has to offer and it's easy to see why it has become the market leader. However, despite a reputation for accessibility, there's a lot for newcomers to learn and a good tip here is to start analysing your own data as soon as possible. Not least because learning how to create a visualisation is a lot easier when you have an insight into the data concerned, plus some idea of what you're expecting to get out of it.
Experienced data professionals should have little trouble, but non-specialists will need plenty of time to become proficient. However, a key strength of Tableau compared to some alternatives is the huge support ecosystem that comes with it, including a comprehensive library of video tutorials, step-by-step guides, webinars and white papers plus a very active and knowledgeable user community, all aimed at get you up and visualising with Tableau as quickly as possible.