Business user-oriented BI solution for visual (ad-hoc) data analysis, data preparation and dashboards with access to a variety of data sources and use of your own visual query language for data.
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Founded in 2003, Tableau emerged from scientific research at Stanford University and is now among the best-known BI & analytics brands worldwide. Following its acquisition by Salesforce in 2019, the company continued to operate largely as an independent BI vendor. With the recent launch of its Tableau Next platform, Tableau has built on Salesforce’s investments in data, agentic AI and trust layers to advance its vision of empowering all users to analyze data and take informed action based on trusted insights. Tableau Next aims to deliver a new approach to business intelligence by using AI-powered autonomous agents to analyze data, proactively share insights and take action to enable humans and AI agents to work together from data to insights to action. Besides the visualization tool, Tableau Next includes Tableau Semantics, a semantic layer with AI-supported modeling features, to deliver actionable insights into users’ workflows.
Tableau remains committed to developing software that requires little training and allows business users to interpret data, mostly by means of interactive visualization and making decisions based on analytics. The intuitive user interface, built-in intelligence and option for in-memory data processing to optimize performance all contribute to the popularity of this solution for visual analysis, dashboards and data discovery. The product offers solid support for analysts through various features such as one-click analyses, data explanations and highlighting anomalies in data.
Tableau’s openness to a variety of data sources is one of its strengths, as it is not necessary to rehost data to use Tableau. The solution allows users to query data live from different data sources, to combine data from across these sources, or to move the data into its own ‘Hyper’ in-memory database for analysis. To ensure performance, Tableau optimizes live queries to generate the most efficient SQL and converts it to source-native dialect. Moreover, to reduce data movement, the product utilizes pushdown optimizations and its federated query cost-based optimizer. The vendor aims to do processing as close to the data as possible, even across different sources.
Data preparation in Tableau can be quick as many manipulations can be made directly while analyzing data, enabling a truly iterative approach to data discovery. With Tableau Prep, data preparation has been enhanced with deeper functionality and a more visual approach with recommendations for data shaping, profiling and enhanced traceability. Tableau Prep transformation flows can be created either on the desktop or via Tableau Cloud and may be scheduled and orchestrated on the server to operationalize data preparation tasks. Data science scripts such as R and Python can be dynamically integrated into Tableau’s data prep and analysis features.
With regards to AI, the Agent feature uses the Einstein Trust Layer and leverages LLMs to support customers with natural language capabilities (e.g., via creating calculations or suggesting visualizations). Tableau Pulse is a natural-language, metrics-based feature equipped with AI functionality to guide users to needed insights and assist in interpreting them. Additionally, the auto-ML capabilities of Salesforce’s Einstein Discovery integrated with Tableau provide advanced predictions and recommended next best actions.
The vendor is actively pursuing the expansion of its active community. Tableau Exchange is a platform for partners to provide a growing catalog of offerings including Viz Extensions (recently announced), connectors and accelerators (prebuilt dashboards).
BARC’s viewpoint on the product’s strengths and challenges.
The information contained in this section is based on user feedback and actual experience with Salesforce Tableau.
The information and figures are largely drawn from BARC’s The BI & Analytics Survey, The Planning Survey, The Financial Consolidation Survey and The Data Management Survey. You can find out more about these surveys by clicking on the relevant links.
All key figures for Salesforce Tableau at a glance.
Flexibilität, Schnittstellen.
Performance.
Zeit nehmen und die Daten verstehen.
Gut.
Ease of use in creating ad-hoc queries or saved routines for month-end reporting.
As we are not in the cloud, we don't always get the latest updates implemented in a timely manner.
It is a very strong contender for finance-oriented analysis. It might be a little too technical for non-finance users compared with Power BI visuals.
Favourable.
Tableau excels at turning questions into visuals quickly thanks to its drag-and-drop canvas, broad library of live data connectors, and the new AI-assisted features (Pulse and Copilot) that surface insights automatically. Strong governance tools and open APIs let large enterprises embed dashboards confidently while drawing on a huge global talent pool and community resources.
Its biggest drawbacks are the premium license costs and the complexity of deciding who needs Creator, Explorer, or Viewer seats. Multi-fact data modeling still feels clunky compared with Looker’s semantic layer, native machine learning is an add-on, quarterly server upgrades are labor-intensive, and the mobile experience demands manual layout tweaks.
Begin with a high-value, low-complexity use case to build momentum, and invest early in data governance so users trust certified sources. Map license tiers to personas before you sign the contract, establish a small Centre of Excellence to enforce standards, automate DevOps with the Content Migration Tool, and choose Tableau Cloud unless strict data-residency rules force you on-prem.
Tableau remains the premium visual analytics workhorse: not the cheapest or easiest to master, but unmatched for exploratory visuals, governed self-service at scale, and an AI roadmap that now sits at the core of Salesforce’s platform strategy.
I like the interactivity that the software provides and the ability to author new dashboards in a very intuitive way.
Working with petabyte-scale data stretches the software's capabilities to the limit.
Find a good integrator.
Positive for the most part, though I still have some doubts about the product's direction under Salesforce ownership.
Interface intuitive, visualisations interactives et percutantes, puissance d'analyse, amélioration de la gouvernance des données.
La gestion des droits d'accès aux données est assez complexe.
Bien structurer les données sources, bien assurer la formation des utilisateurs, anticiper la gouvernance des données.
Excellente opinion.
Easy dashboard creation.
AI is only for the cloud.
Create a proof of concept to evaluate Tableau.
It is a very good BI platform.
The presentation layer when in the tool.
Lacking offline display for consistent print-like output.
Align with your architecture standards, which should include analysis of embedded analytics and intelligence tools.
Positive and they continue to evolve the tools.
Interaktivität und Dokumentation.
Konzept für Multi-Language Support.
Erste Use Cases gemeinsam mit Experten umsetzen im Fachbereich, um Fachanwender direkt am Beispiel zu schulen und eine Governance aufzubauen. Diese kann dann skaliert werden auf andere Bereiche.
Das Tool bietet was man braucht für Dashboarding, Visual Analytics und Self Service BI & Analytics. Die Hürden beim Einsatz werden schnell organisatorische und keine Tool-bezogenen.
Versatily. Easy for consumers.
Report formatting is from another age.
Strong business process of who can create / comsume / managed contents.
Great BI company, worry of the innovation pace since its aquisition.
The relatively flat learning curve, intuitive UI.
Cannot think of anything.
Take it for a trial run and ask some questions in the user community - they are active and very enthusiastic.
Great tool built with visualization and understand the data, not the tech in mind.
Tableau als Lösung wurde durch die Fachabteilungen gewählt und akzeptiert und wird eigenständig genutzt (Self-Service BI).
Tableau hat ein unzureichendes Security-Modell, um die Need-to-know-Einschränkungen in der Finanzindustrie hinreichend umsetzen zu können.
Anforderungen und Fähigkeiten sollten im Detail erhoben, analysiert und bewertet werden. Tableau ist keine Lösung für alle Probleme und löst bestimmte Aufgaben extrem gut, für andere ist es weniger geeignet. Wenn dies nicht gut analysiert wird, steht man am Ende mit mehreren Tools da, die alle unterhalten werden müssen.
Grundsätzlich ist Tableau eine moderne Lösung, die aber sehr auf dem Paradigma beruht, dass es kaum Anforderungen an Abgrenzungen der einzelnen Nutzer im Hinblick auf den jeweiligen Datenkontext gibt. In stark regulierten oder auditierten Bereichen kann dies eine komplexe Herausforderung sein.
One of the things I appreciate most about Tableau is its ease of use for generating and distributing reports. With Tableau's intuitive drag-and-drop interface, even non-technical users can quickly create sophisticated data visualizations and interactive dashboards. The ability to easily combine different views and worksheets into a cohesive story makes it simple to package insights in a way that can be readily shared and understood by others. Tableau's wide range of output options also streamlines the distribution process. Whether publishing to Tableau Server, sharing via email, or embedding visualizations in web pages or documents, distributing reports is straightforward. And with features like subscriptions and alerts, key stakeholders can be automatically notified when new reports are available or data is updated. Overall, Tableau's user-friendly design and robust sharing capabilities empower organizations to unlock insights from their data and effectively disseminate them across teams and departments. The ease of generating visually compelling reports and the flexibility in how they can be distributed is a major strength of the Tableau platform.
One area where Tableau could be improved is in the intuitive handling and representation of targets, especially for sales and marketing use cases where tracking progress against monthly or weekly goals is fundamental. Currently, adding target lines or values to visualizations in Tableau requires a fair bit of manual effort and data manipulation. There is no streamlined or user-friendly way for managers to simply input their target metrics and have them automatically visualized alongside the actual data. The process typically involves creating separate calculated fields for the targets, potentially joining or blending data sources, and then layering the target values as reference lines or shapes. This can be cumbersome, especially for less technical users just wanting a quick way to overlay targets on top of visualizations. It would be great if Tableau could provide a more seamless, out-of-the-box method for defining and displaying targets across different visualizations. Perhaps a dedicated Target shelf or parameter that allows inputting singular values or ranges that could then be dragged onto views instantly. Enhancing this capability would allow sales and marketing managers to more easily analyze performance against goals without wrestling with data manipulation. The ability to flexibly update and manipulate target representations directly within visualizations would be invaluable.
Plan your processes and KPIs in advance: Before rolling out Tableau, take the time to clearly define your key performance indicators (KPIs) and the reporting processes you want to enable. Having a solid understanding of the metrics that matter most to your business will allow you to structure your data sources and Tableau workbooks purposefully from the start. Determine which KPIs need to be tracked at different levels of the organization to avoid rework down the line. Establish a governance method: Implement a governance plan outlining rules and best practices around how Tableau content will be generated, validated, and distributed throughout the company. Define guidelines for areas like data source management, naming conventions, user permissions, standardized calculations, and branding. Having a coherent governance approach will ensure reporting remains consistent, secure, and easy to navigate as usage scales. Build for different user skill levels: When architecting your Tableau environment, account for the varying analytical skills across your user base. Provide some pre-built dashboards and reports for more casual users, while enabling deeper ad-hoc analysis for power users. Leverage appropriate data source design and Tableau features like set analysis and parameters to construct content suited for different audiences. Define a centralized report delivery system: Establish a centralized platform or server environment where finished reports and dashboards can be securely published and accessed. Utilize Tableau's distribution capabilities like subscriptions, alerts, and email reporting to automate scheduled report delivery. Set up folders, projects and permissions to manage access appropriately across teams and level of the organization. Provide training and enable a data-driven culture: Beyond technical implementation, prioritize change management. Invest in training and enablement so employees understand how to leverage Tableau to find and share insights. Nurture a data-driven culture where making decisions based on accurate reporting and visualized data becomes the norm. By proactively planning your reporting processes, governance, user roles, delivery approach and cultural embrace of analytics upfront, you can set your organization up for a smooth and sustainable Tableau deployment that becomes deeply ingrained across teams.
My general opinion is that Tableau is an excellent data visualization and business intelligence tool, but I agree with the assessment that it has a relatively small learning curve for basic report delivery, while having a longer learning curve to truly master report generation and leverage Tableau for robust BI capabilities. On the positive side, Tableau's core strength lies in its incredibly intuitive user interface that allows even non-technical users to connect to data sources and quickly start building visualizations with simple drag-and-drop actions. The ability to easily distribute reports and dashboards via Tableau Server or Online makes it great for organizations wanting to make data and insights more accessible. However, as one moves beyond just consuming pre-built content, the learning curve does get steeper to become proficient in advanced report and dashboard authoring. Skills like data preparation, creating proper data models, utilizing calculations, building intricate visualizations, and setting up interactive actions/dashboards require more dedicated training and experience. To truly unlock Tableau's potential for enterprise-grade BI, there are many nuances to understand around data architecture, visual best practices, performance optimization, governance, security, and integrating Tableau into a larger data strategy. Crafting a seamless, robust self-service analytics experience requires well-designed data sources, careful management of content, and adhering to standards. So, in summary, I agree that while Tableau has a very approachable front-end for basic reporting, there is a longer ramp to develop more sophisticated BI skills and harness the full capabilities of the platform across data modeling, analytics, dashboarding, and content management. Having a mix of Tableau power users and consumers is often the key to a successful rollout.
Visualization capabilities and tie in to data cleaning software. Flexibility in presenting and analyzing data. Good for use with business users and content creators across the board.
Certain functions are most difficult to implement as a business user with limited coding experience. Certain in depth analyses are difficult.
Complete all the training they offer.
Great, probably overpriced software.
Tableau is easy to use and very versatile.
It’s AI integration with the Tableau Cloud product should be more apparent.
Try it out. Find you most capable employees to test the product.
The gold-standard of data visualization.
Intuitive, not costly, efficient & effective.
Scalability and lacks functionality.
Be very clear in your objectives. Have realistic expctations.
It's a good tool, in general.
Clean interface and visualization style.
Data modeling in the cloud and ability to update data for users. Also, the pace of product innovation.
Assess the company's roadmap because it is possible that Power BI may innovate ahead of Tableau.
Neutral.
