The Tableau Platform provides business-user-friendly analysis, data visualization and data preparation. Its core comprises various clients and a server with connectors to a wide variety of all types of data sources. Its well-structured, intuitive user interface, built-in intelligence and memory utilization to optimize performance contribute to its popularity for self-service analytics and BI, data discovery and interactive visual analysis.
Tableau was designed to provide business users with software that allowed intuitive visual analysis to get insights into data. Tableau’s product strategy of bringing analytics to the masses clearly reflects this vision. However, the strategy today encompasses more than that. Tableau continues to evolve into a modern and open platform that is not only functionally enhanced, but also incorporates data management in a modern architecture. The goal is to scale their flexible platform for the enterprise by providing the required governance features.
In June 2019, Salesforce acquired Tableau to extend its analytics offering. Salesforce currently operates a worldwide business with more than 54,000 employees. As part of Salesforce, Tableau is positioned to accelerate and extend its mission to help people view and understand data. Tableau continues to operate independently under the Tableau brand, driving forward a continued focus on its mission, customers and community. Salesforce upped the ante on analytics with transferring its Einstein Analytics to Tableau. Tableau Einstein Discovery significantly enhances the product’s value though the help it provides to find hidden trends or make elaborate predictions. It the major target of the vendor’s AI investment and its integration into Tableau is continually enhanced to increase the value for customers.
Tableau Platform is the common technology supporting four major products: Tableau Desktop, Tableau Server, Tableau Mobile, Tableau Prep Builder and Tableau Online. This review does not cover Tableau Public due to its limited relevance for enterprise use.
Tableau strives to deliver software that is as intuitive as possible and requires little training but allows business users to thoroughly analyze complicated data through visual analysis without having to rely on the assistance of BI developers. The toolset offered provides capabilities geared to analysts for in-depth analysis such as visual analysis, predefined statistical functions and data preparation capabilities.
The company previously pursued a “land and expand” strategy with a focus on business users, popular with other self-service analytics and BI vendors too. More recently, Tableau is devoting more attention to data management by introducing new features, stronger governance and enhanced support for enterprise environments.
In addition to trial versions of the product, Tableau offers ‘Tableau Public’ for free. While it is lacking capabilities to access data from data sources other than files, it comes as a comprehensive tool to analyze, visualize and finally publish data to the Tableau Public cloud. Visualizations can be embedded into websites with complete interactivity retained (e.g., for data journalism or blogging). This approach earns the vendor a lot of publicity and ensures low access barriers for people wanting to educate themselves on using the software.
Tableau’s original products were Tableau Desktop and Tableau Server. Tableau Desktop is the main authoring software, supporting the user through all steps of the analytics cycle (from data to insight). Tableau Server is the web and mobile-enabled dashboard publishing and collaboration environment. With this architecture, customers typically have a few power users who produce analytics content and a much larger share of casual users who consume reports, dashboards and analyses. Tableau has many reference customers for deployments supporting thousands of users.
Tableau Online is the multi-tenant cloud solution, which is a hosted version of Tableau Server. The product works with on-premises as well as cloud data sources. It is typically used to quickly start or scale analytics to many users without requiring huge IT involvement.
Tableau has several types of technology partners. It partners with database and application vendors such as SAP, Cloudera and others by building native connectors to these systems. Partnerships with companies like Informatica and data preparation vendors such as Alteryx support customers’ data integration needs. Tableau also partners with specialists to extend the product’s capabilities (e.g., with Datasift to provide social sentiment analysis). Finally, Tableau can connect to R, Python, Amazon SageMaker or others for advanced analytics (e.g., cluster analysis, decision trees). While the approach to have partners extend the product where it lacked features contributed to Tableau’s popularity, the vendor now concentrates on providing its own data preparation and advanced analytics capabilities.
Tableau Desktop builds on a proven client-server architecture and makes extensive use of its integrated in-memory columnar data storage ‘Hyper’. Customers can use the in-memory engine to easily combine data from many sources, speed up analysis or take load off operational systems. The core authoring component is Tableau Desktop, but most authoring features even data preparation are available on the web too. Advanced and better guided data preparation are done in Tableau Prep Builder, which is being continuously enhanced (e.g., to write data to external databases). Tableau Prep Builder can be extended with Tableau Prep Conductor, a server component that is used to schedule and automate data flows.
Tableau offers connectivity to a substantial number of data sources of all types. In addition to relational and multidimensional databases, on-premises and cloud business applications can be accessed too. Tableau is also capable of integrating local files with capabilities to parse and import data from tables in PDF. There are functional differences between relational (SQL) and multidimensional (MDX) data sources. Some features are not available when connecting to OLAP data sources but can be used when connecting to relational data sources and vice versa.
Tableau supports two paths to connect to data sources. The most common option is to use the integrated data storage ‘Hyper’ to create optimized extracts and to load data into memory. Using extracts is beneficial when the underlying database is slow, the load on the underlying system should be lessened or when data from multiple system should be combined. Replicating data may require replicating security settings too and can be a challenge for proper data governance. Relationships were introduced to increase flexibility during analysis by joining data during analysis instead of in the data source. Queries retrieve less data, which speeds up analyses and data duplication is avoided through ‘understanding’ the granularity of data. To increase flexibility for Tableau and Hyper users, the vendor is opening its platform to third-party providers by offering access to data stored in Hyper. ‘Live connect’ leaves data in the underlying data store and queries the data at runtime. In this case, Tableau acts as a pure front-end analysis tool and does not store the data.
Most Tableau content producers and consumers are business users rather than data scientists or BI developers. Data access and preparation are performed in Tableau Desktop, or increasingly Tableau Prep Builder, using a graphical interface with built-in intelligence to assist users.
Tableau’s primary focus is interactive and intuitive visual analysis to efficiently dissect data and find patterns. Measures and dimensions are selected via drag-and-drop for analysis and visualized instantly in interactive visualizations. Users are guided by the software with functions such as ‘Show me’, which proposes appropriate visualizations for any combination of data fields selected. Tableau offers grouping, hierarchizing and calculation options supported with context-sensitive wizards. Its calculation library’s analytical feature set ranges from simple trend to smoothed forecasts, table calculations and other analytical and statistical functions such as cluster analysis that are easy to apply without requiring coding.
‘Explain Data’ provides users with automatically created calculations, visualizations and natural language explanations (NLG) for requested KPIs. It reveals patterns, clusters and outliers based on ML-driven algorithms to help users to explore data they cannot sift through manually. Through a recently optimized UI, viewer can create statistical models with a single click. ‘Ask Data’ is Tableau’s offer for customers looking for natural language query support (NLQ). Basic analyses can be created by simply asking Tableau about the data is serves to users. The dashboard integration as well as the search experience were improved drastically. This offers the opportunity to reach users not well-versed in analytics or grant access to analytics everywhere which is core to Tableau’s vision of analytics success.
Visualizations reside in individual sheets in Tableau, much like the sheets in an Excel workbook. To create a dashboard, the users drag worksheets from a shelf onto the dashboard canvas and rearrange visualizations. Users can extend the information on a dashboard by linking to further information. Tableau also possesses a parameter and global filter concept, which allows dashboard consumers to intuitively filter the data displayed to a specific range. In addition, Tableau provides features for data-driven alerts and custom content subscription.
User & Use Cases
Tableau is popular in medium to large enterprises with above-average sized deployments in terms of median and mean users. Tableau deployments are maturing and serve a growing share of information consumers as well as an increasing number of users with interactive dashboards built on top of visual analyses. Viewing standardized content such as dashboards and formatted reports are equally important for Tableau’s customers despite the tool’s proficiency to serve individual information needs through ad hoc queries and analysis well.
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