Organizations are looking for technologies that help more people do sophisticated analysis. At Tableau, we’re investing in analytical capabilities that help anyone—from data scientists to business users—get to answers faster and uncover unanticipated insights. Through machine learning, statistics, natural language, and smart data prep, we are making analytics more approachable to users of all skill levels.
In this report from technology research and advisory firm Constellation Research, Vice President and Principal Analyst Doug Henschen looks at Tableau’s intelligent data prep, automated data discovery, recommendations, and natural language investments.
View our webinar with Doug Henschen of Constellation Research and members of our natural language, data prep, and artificial intelligence engineering teams. Learn more about emerging smart technologies in analytics and how Tableau is helping to drive these features forward.
Doug Henschen (Constellation Research) and Francois Ajenstat (Tableau) discuss smart analytics trends in this interview. Building upon the analytical richness of the Tableau platform, our built-in smart capabilities are designed to both lower the barrier to entry to analytical processes and help advanced users ask more sophisticated questions.
Watch nowInteracting with data using natural language lowers the barrier of entry to insights, helping people answer their questions more quickly without deep, technical data expertise.
Tableau introduced Ask Data for Tableau Server and Online in Tableau's 2019.1 release. With Ask Data, you can use natural language to ask questions and instantly get a response right in Tableau. Answers come in the form of rich data visualizations with no need to understand the structure of your data, so you can get to your answers faster. Fully integrated in the Tableau platform, Ask Data works with existing data sources with no additional set up needed. Check out an interactive Ask Data demo.
Bring natural language generation capabilities to your dashboards through our NLG partners including Narrative Science and Automated Insights. Leveraging Tableau's APIs, these technologies automatically generate text-based descriptions of visualizations to help customers extract more meaning from their data.
With powerful machine learning, we’ve made self-service data prep more automatic in Tableau. By reducing the amount of manual work needed to combine, shape, and clean data, everyone can get the most from their data faster.
Data Interpreter helps everyone get to analysis faster by automatically detecting sub-tables, removing unique formatting, and allowing users to quickly and easily pivot and split data from spreadsheets, PDFs, and more.
Fuzzy matching indexes and groups related words by pronunciation or common characters in one click. Powerful algorithms work behind the scenes to help users prep data faster, minimizing manual cleanup.
Start your analysis faster with the right data. Tableau uses machine learning to recommend database tables and joins based on your organization's data source usage metrics.
We’re packaging up powerful algorithms and statistical models in an approachable way, giving our customers new superpowers, and reducing the burden when analyzing complex datasets.
Automatic clustering is a drag-and-drop function in Tableau, allowing anyone to use a built-in k-means clustering model to find statistically significant groupings in data that might not have otherwise been seen.
By packaging powerful predictive models into an easy-to-use experience, we’re bringing predictive capabilities to more people, helping them anticipate changes in their business.
Predict where your business is going. Simply drag and drop to generate a forecast. Tableau automatically selects the forecasting model based on the data, and accounts for seasonality with exponential smoothing.
Tableau not only connects to file outputs from MATLAB, R, SAS, and SPSS as data sources, but supports direct integration with R and Python. Run code directly inside Tableau, as well as visualize and manipulate model results from predictive services such as Rserve and TabPy. With statistical model integration, it’s easy for anyone to leverage models built by your data science team.
