Tableau Strives to Empower a Broader Audience with Business Science


Summary Bullets:

R. Bhattacharyya

• Tableau has introduced the concept of Business Science, which brings advanced data analytics tools to users that aren’t data scientists.

• During its mid-November user conference, Tableau debuted two new tools that support Business Science: Model Builder and Scenario Planning.

Tableau’s mission is to help all people better visualize and understand data. The company wants to make analytics, and the insights it can drive, available to line of business professionals throughout an organization. To support this vision, it has introduced the concept of Business Science. Business Science brings advanced data analytics tools to users that aren’t data scientists, yet feel comfortable working with data and have a strong understanding of the operational and overarching business issues facing their organization. The tools are powerful enough to offer capabilities such as customized views, drill downs, and predictions, but don’t require the expertise of a data scientist to manipulate them. Essentially, they empower the data-savvy, line of business professional to combine domain expertise with advanced analytics to make better decisions more quickly.

During its mid-November user conference, Tableau debuted multiple new features and tools, but two stand out because of their direct support of Business Science: Model Builder and Scenario Planning. Model Builder uses the AI capabilities of Einstein (from Salesforce) and embeds them in Tableau. It enables users to create predictive models within Tableau using Einstein Discovery, without the need for access to Salesforce. It also has features that allow for collaboration and review among business teams. Scenario Planning provides a visual comparison of scenarios to help evaluate next best actions.

Tableau’s latest announcements address enterprise pain points. Across all industries, organizations are looking to make better use of the data they are collecting. However, initiatives to scale analytics and AI projects are often challenged by a lack of resources and a lack of skills. Data scientists and AI specialists are expensive and can be hard to find, and end users don’t have the expertise to identify and manipulate data, let alone generate AI-driven insights. Adding to the challenge is that line of business professionals, quite understandably, often question the results of analytics solutions they don’t understand. Solution providers are stepping in to fill these gaps by offering tools that come with built-in AI capabilities, as well as tools that explain findings and allow for more voices in the creation process. However, off the shelf solutions may not meet the unique requirements of all organizations; therefore, tools that enable some manipulation by data savvy professionals (such as those recently announced by Tableau, Google, Domo, and others) address an unresolved need. Looking ahead, empowering a broader audience is essential to scaling advanced analytics.

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