Analytics anyone can use.
Analytics for organizations.
Cloud analytics for organizations
For example, we’ve designed the “Working with Data” track around helping you get the most of your data sources with Tableau. Whether they are flat files, databases, data warehouses, cubes, Hadoop or wherever your data lives – the sessions in this track are going to give you the details on optimizing your data environment with Tableau.
I’m particularly excited about one of the sessions in this track which I think many of you will identify with. The session is called “How can I wrestle my @#$%ing data into Tableau?” Does this sound familiar to you? The session will be delivered by Curt Budd, one of Tableau’s Sales Consultants who spends every day working with customers helping them be successful in delivering Business Intelligence and Data Visualization across their organizations. Here are some details on the session from Curt’s perspective:
It happened to me again this week. Twice. Two different customers each had seemingly simple visualization needs. All they wanted were some simple bar charts, cross-tabs, and dashboards. But, their data was a mess. And neither customer had the time to turn this into a large-scale IT-driven data quality project. We get so excited about the highly intuitive graphical things that we can create with Tableau. But we’re sometimes defeated by seemingly vexing data problems.
I heard someone say to me the other day that data is like water. It will flow through and around anything. But, how can I dig a nice, deep channel so that the water flows neatly right into Tableau? IT-driven solutions to this problem will inevitably result in something that is robust, scalable, accurate, and auditable. But those virtues come at a cost. These types of projects take time and money. My customers barely had enough time to convey their needs to me – let alone scoping out a complete enterprise ETL project. And, frankly, neither of my customers needed the solution to last more than a week or so. They needed their visualizations to prove a point, to make a case for something. Once done, the visualizations wouldn’t be used again. And the data acquisition stuff would be abandoned for other, more pressing, needs. Such is the nature of business-driven ad hoc analysis. When you need it, you need it NOW but you don’t necessarily need it balanced out to the tenth decimal point. And, you don’t necessarily need it in perpetuity.
In short, both of my customers were seeking a quick answer to a quick question. But, they were stymied by data that wasn’t useable in its current form. In their frustration, they both came to me with essentially the same question, “How can I get that %^!! data into Tableau, and how can I do it fast?”.
This session will focus on your data. I don’t want to show you all of the newest glitzy visualizations. I want to show you some ugly raw data out in the wild. And, I want to show you how to lasso that data, wrestle it into shape, and make it available for your analysis in Tableau. We’re going to get dirty and roll up our sleeves and explore a number of common and less common data problems and how to work with them in Tableau. We’ll talk about re-shaping your data when needed, joining flat files, using custom SQL to alter the columns in flat files, aggregating data, and doing some automation of all of this via Tableau Server. This is going to be fun!
Thanks Curt, what great insight into how the conference sessions come to exist. The best part? This is just a small peek into one of the more than 130 sessions that you’ll have access to at the customer conference.