TDWI Austin 2016
Come find us in Austin, where we’ll be talking about data strategy and data visualization.
Sunday, December 4
Big data and data science have great potential for accelerating business, but how do you reconcile the business opportunity with the sea of possible technical solutions? Fundamentally, data should serve the strategic imperative of a business—those key strategic aspirations that define the future vision. A data strategy should guide your organization in two key areas: what actions your business should take to get started with data and where to start to realize the most value. Colette Glaeser and Edd Wilder-James explain how to create a modern data strategy to power data-driven business.
You Will Learn
- Why you need a data strategy
- How to connect data with business
- Ways to devise a data strategy
- What the data value chain is
- New technology potential
- Different project development styles
- How to organize to execute your strategy
Wednesday, December 7
From Data to Decision: Exposing Compelling Insights through Visualizations
Steve Lohr wrote in the New York Times that 80% of data science is “janitorial work”: cleaning and munging your data. But of course, the part that usually makes headlines is the other 20% after that: the insights that impact everything from business outcomes to public health. Don’t let all the hard work of scrubbing your data go to waste! Learn how to craft compelling visualizations that showcase the value of your models and make it easy for everyone to appreciate what the data is saying.
You Will Learn
- How to identify and prioritize the key relationships in your data
- Best practices for assigning those relationships to specific visual encodings like position, shape, and color
- How to pare back visual distractions and avoid the temptation to “decorate” your data
- How to leverage principles of cognitive perception for efficiency and clarity — and how to avoid triggering them in ways that might mislead or deceive your audience
- The audience should leave with the confidence and know-how to expose and explain the models they’ve created
- Data scientists, analysts, and anyone looking to share their insights