Agility has become the competitive edge and analytics platform provider Tableau is looking to lower the barrier to using data science techniques with a new class of AI-powered analytics designed to enable business users to make better decisions faster.
Tableau will deliver Einstein Discovery in its 2021.1 release later this month to enable business science, which the company said empowers more people with data, simplified model creation, predictions, what-if scenarios, forecasting and other analytical methods—using clicks rather than code.
The goal is to help people go beyond understanding what happened and why it happened, to explore likely business outcomes and inform proactive action, the company said. Other enterprise capabilities coming in 2021.1 aim to simplify analytics at scale and expand the Tableau ecosystem to help anyone use data to understand their environment, make decisions and rapidly iterate to find solutions, Tableau said.
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Data science meets business science
Data science is business-critical, highly precise and driven by deep experts, said Francois Ajenstat, chief product officer of Tableau. “It’s the hottest thing now with AI and it’s used for very important decisions. But it’s used by specialists for complicated projects; it’s very expensive and you need precision on them. Most, unfortunately, fail.”
To build truly data-driven organizations means unlocking the power of data for as many people as possible, he said. “Democratizing data science will help more people make smarter decisions faster.”
Business science is a new category of AI-powered analytics that will enable business people with domain expertise and the right business context to do custom predictive models, simulations and drive scenarios really easily, he said.
“Both are extremely important,” Ajenstat added, but the idea is to give predictive power to a larger population to be able to do things like fraud detection.
Traditional offerings in the business intelligence space have been designed for specialists and they are “complicated and hard to use,” Ajenstat said. “With Einstein’s … new business discovery capabilities we think we’ll make use of data science in a way that is more applicable to business people and it can be used for faster, more relevant business insights.”
For example, Tableau customers can use business science to improve supply chain efficiency, predicting the likelihood of a purchase or maximizing delivery of goods or services. On-time shipping is a big concern, Ajenstat said, and a user can see different categories of products and have transparency into their shipping status.
Einstein will make on-time predictions based on the data for all transactions, he said. “It’s interactive … and can calculate dynamically,” Ajenstat said.
Data science can also help with vaccine research and development, while business science could help with distribution and getting shots in people’s arms, he said. “Business science will [determine] how do we optimize our supply chains to get as many vaccines to people as possible and lower the amount of distribution challenges we have.”
Additional product features include:
New Microsoft Azure Connectivity Improvements designed to allow people to connect to their data in Azure SQL Database (with Azure Active Directory) and Azure Data Lake Gen 2. Tableau also now supports Azure Active Directory in two existing connectors, Azure Synapse and Azure Databricks.
A new Extension Gallery designed to help people easily search for and discover connectors and dashboard extensions within Tableau. From the gallery, customers can find and install connectors and dashboard extensions that add functionality to their dashboards and connectors. This will enable Tableau to access additional databases and applications within their Tableau workflow.
A redesigned notification experience displays a Tableau user’s shares, comments, extracts and prep flows together in one dedicated space. This communicates all important changes across the organization. People can choose where to receive these notifications—directly in Tableau, email or both—ensuring they’re aware of important alerts or updates.
Quicker, easier level of detail expressions let people use context menus or drag-and-drop a measure onto a dimension to automatically create a level of detail expression with the default aggregation.
The goal is to help business people be more agile and responsive to changes, Ajenstat said. “That’s at the heart of business science—democratizing the data science capabilities” so organizations can become data-driven with data and analytics as core pillars of their success, he said.