90% of the World’s data was created in the last two years. Currently doubling in size every couple of years, it is likely to reach 44 zettabytes by 2020. That is 44 trillion gigabytes.  Helping manage this kind of exponential growth in data, Tableau was an idea that originated out of a Department of Defense project in conjunction with Stanford university to help organize and visualize data from relational databases.  Founded by an ensemble cast of an academy award winning university professor, a computer scientist and a savvy business leader, it has grown from an obscure startup to a $6 billion dollar company in little over a decade. We’re going to explore the reasons for this runaway success story in the below report.
Tactical to Practical : Mission to simplify data
While it might have started off as a defense project, Tableau’s founders were quick to identify its potential and expand it to commercial applications. As the number of smart devices touches 2 billion, the number of people uploading photos, videos, text and other content is rapidly increasing. With a mission to make breakthrough products that help “people see and understand data”, Tableau wants to give users the power of data at their fingertips. And this is Tableau’s niche: Simplifying the opportunity for individuals and enterprises to use data to learn about customers habits, demand patterns and supply side constraints.
A Solution For Everyone
To achieve their strategic goals, Tableau needed to find widespread adoption across sectors and industries. They did this and created value for their customers by allowing their customers to connect large databases and parse through the data quickly and easily. This also includes interpreting data realtime (when connected to a live database), enabling dynamic filtering and converting SQL entries to immediately usable visual reports. Simultaneously, the products needed to be priced in a manner to allow for both large corporations as well as small industries to be able to use their service. Most importantly, people without significant experience in SQL should be able to easily and intuitively learn adopt it. Finally, it needed to be scaleable such that once a consumer is on Tableau, they should be able to enhance features and services when new terminals were added without having to lose data or configuration in transition.
“The fundamental innovation is a patented query language that translates your actions into a database query and then expresses the response graphically. The breakthrough was the ability to do ad-hoc analysis of millions of rows of data in seconds with Tableau’s Data Engine.” 
Keeping in mind their industry application but wanting to reach out to large as well as smaller customer accounts, Tableau primarily offers three products to consumers 
- Fast Analytics for Everyone: Desktop version
- Analytics in the Cloud: Tableau Online
- Business Intelligence: Tableau Server
With a VizSQL powered front end and analytics using a high performance data engine, they managed to create such an offering. Due to its ease of use and reasonable pricing, it is already being used in a wide range of model building and analysis. Included below are few samples pulled out from ‘Viz of the day’.  From Aerospace to Retail and Banking to Entertainment and all the industries in the middle, they already count amongst their customer base over 35,000 companies and customer accounts.
Transition and Evolution
In addition to offering a differentiated product suite, another ingenious move by the company is to keep all the products on an annual licensing fee. Not only does it give the consumers the option to try out a new product at a reasonable price, it allows Tableau to retain control over future pricing of their product. And while the initial euphoria over the IPO has died down , Tableau continues to perform extremely well vis-à-vis their competitors. As the number of devices and content generated grow, the significance of analytics and data security will only increase from here on. And while staying competitive and continuing to innovate, Tableau needs to look at including predictive behavior into their analytical tools as they evolve to compete in an increasingly competitive Artificial Intelligence landscape.