6 Keys to an Agile Tableau Implementation

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As organizations become more data driven and realize the benefits from using visual analytical tools such as Tableau, the volume and velocity of analytic based questions grow. As these new questions are posed, organizations become aware of the challenges they face in preparing the underlying data including: How do you repeatedly cleanse and shape your data so your visualizations are always up-to-date? How can you scale to ensure you are including all of your relevant data? How can you modify your existing and proven data models to provide ad hoc and what if analysis? Join our webinar and see how you can maximize your analytics in Tableau with the 6 keys to an agile Tableau implementation.

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Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization. This webinar illustrates data modeling as a key activity upon which so much technology and business investment depends. Specific learning objectives include: - Understanding what types of challenges require data modeling to be part of the solution - How automation requires standardization on derivable via data modeling techniques - Why only a working partnership between data and the business can produce useful outcomes

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