Tableau Alternative: Addressing Challenges and Solutions for Modern Analytics

In today’s dynamic Business Intelligence (BI) landscape, selecting the right analytics platform is critical. Ongoing trends in BI tools shape evaluation criteria, influencing companies' choices and decision-making processes.

What are the must-haves of a modern analytics solution?

• Suitable for all user personas (consumers, business analysts, developers).
• User-friendly analytics UI that is intuitive and efficient.
• Capable of managing large data volumes and adapting to increased workloads.
• Able to be scaled for multiple users, both inside and outside the organization.
• Easy facilitation of administrative tasks.
• Flexible to customize and seamless to embed.
• Equipped with developer tooling and integration features.
• Accelerated with AI-fueled features.

The above are just some of the analytics requirements currently in demand. To ensure your solution aligns with your specific goals, check out How to Evaluate BI Tools to Choose the Best One. And for a no-nonsense guide to today’s analytics tools, you might like to read: Comparing the Best BI Tools: Select the Right Solution for Your Business.

What is GoodData?

GoodData is a leading provider of analytics solutions. Its scalable and high-performance platform caters to businesses of all sizes across various industries, from startups to enterprises.

GoodData is an API-first, AI-powered analytics platform that empowers engineers with APIs for data definition, optimization, and visualization. The platform is user-friendly, open, and scalable, making it ideal for embedding analytics into SaaS products, internal BI, or large-scale data products. Generative AI accelerates data-driven insights, and AI-fueled features are a key element in GoodData's future vision.

What is Tableau?

Tableau is an alternative to GoodData and is considered a competitor. It was acquired by Salesforce in 2019 and is well-known in the analytics world. Its standout product, Tableau Desktop, has earned popularity for being a top visualization tool that enables users to customize every detail of a chart and explore flexible layouts with layering options. Since 2023, Tableau has also been incorporating AI-fueled features.

Tableau excels in rich visualizations, but in today's BI and analytics landscape, is this enough? As businesses expand, there are additional needs like consistent data governance and ease of making changes, and unpredictable future user bases can lead to higher costs. With this in mind, alternatives to Tableau may provide a more optimal solution.

Tableau dashboard (resource: Dashboard Examples)

Key differences between the two competitors

The table below summarizes how Tableau and GoodData measure up in relation to key differentiators.

What is Tableau best known for?

Tableau is considered a viable alternative to GoodData for a reason: it remains one of the most popular visualization tools and is well-known for several features that users like:

Data visualization capabilities

Users appreciate how Tableau Desktop swiftly transforms large datasets into visually appealing graphs and charts, providing valuable insights at high speed.

Intuitive drag-and-drop

Reviewers consistently praise Tableau Desktop's intuitive drag-and-drop interface, enabling users to quickly create dashboards and enhance productivity. The tool's user-friendly design is appreciated for easy data navigation.

Wide range of data sources

Tableau provides extensive data connectivity, offering different connectors for almost all data warehouses and a wide range of sources. Users can easily establish relationships between multiple datasets (using Tableau Prep as well), utilize CSV files for visualizations, and connect private network or on-premises data through Tableau Bridge.

Tableau as a skill

Tableau's expert developer community strongly influences analytics and BI purchases, driving platform adoption, fueling market growth, and increasing demand for Tableau skills in the job market.

What challenges do Tableau users face?

Below are some of the main issues users encounter with the on-premise solution, Tableau Desktop.

• Tableau faces data governance challenges with its locally installed Desktop tool on each user's device. This can cause inconsistencies and tracking issues when sharing personalized dashboards due to varying data models and setups.
• Tableau has issues with scaling and sharing as its Server demands infrastructure management. The cost increases when handling larger datasets and other Tableau products when they need to be shared inside or outside the company.
• Tableau’s pricing models are unpredictable, charging per user for internal use cases and per capacity for external use cases. Companies may find it challenging to predict actual usage, especially over an extended period, which leads to high costs.
• Since its acquisition by Salesforce, Tableau appears to be less focused on innovation, resulting in delayed responses and a perceived lack of support.

Another issue is that Tableau Desktop is just a visualization tool; for a more comprehensive solution, users require other Tableau products, like Tableau Server for internal sharing, Tableau Cloud for external sharing, and Tableau Bridge for local database access. Tableau Desktop also lacks new features, including AI, which is available only in Tableau Server and Tableau Cloud, making it appear outdated. Integrating other Tableau products is essential to address sharing, collaboration, and advanced analytics tailored to your business.

How does GoodData address the challenges Tableau users face?

As one of Tableau’s main competitors, let's look at how GoodData addresses the issues their users come up against.

1. Challenges in data governance

Tableau Desktop is installed on each user's device. Users can connect databases and create personalized dashboards, but sharing may result in inconsistent metrics and tracking challenges due to different data models and setups.

On the other hand, GoodData provides a consistent, simple one-product solution to manage and maintain data, data models, and dashboards across multiple teams, departments, customers, and vendors.

2. Difficult to scale

For Tableau scalability, the assistance of professional teams is required to manage infrastructure and performance As dashboards grow, Tableau users face increasing difficulties. Scaling hardware for large datasets can be problematic. Transitioning to Tableau Server or Tableau Online for user and data growth is an option, but this demands professional expertise and ongoing maintenance.

On the other hand, GoodData has a distributed architecture for processing extensive data from diverse sources. Its platform hosts multiple tenants or customers in unified workspaces, eliminating the need for hardware maintenance.

3. Tableau is too expensive

Some users are considering alternatives to Tableau because it is expensive due to old deals from when software pricing was non-flexible and non-transparent. The commonly used Tableau Desktop is part of this costly model, which hasn't changed much in the past decade.

Meanwhile, GoodData offers various pricing options to accommodate a company's budget and align with preferences and requirements. The pricing models are transparent and highly predictable, catering to both internal and external use cases.

4. Lack of support and improvement

Since Salesforce acquired Tableau, some believe there’s been a lack of focus on innovation. Responses to issues or questions are noticeably delayed, and Tableau seems to prioritize planned schedules or industry trends, like integrating AI features, over customer feedback.

GoodData demonstrates a strong commitment to continuous improvement, frequently delivering features. Prioritizing customer feedback, GoodData's teams provide top-level support through dedicated account and customer success managers.

AI in analytics: Tableau vs GoodData

The two competitors share the same goal of integrating AI into their analytics solution.

In 2023, Tableau introduced AI features that resulted in Tableau GPT and Tableau Pulse. Empowering analysts and users, Tableau GPT automates data analysis and generates insights based on Einstein GPT, a Salesforce collaboration with OpenAI. Tableau Pulse provides metrics from recent data trends identified by Tableau GPT.

NOTE: AI features (Tableau GPT and Tableau Pulse) are part of Tableau Server and Tableau Cloud. In Tableau Desktop, sharing dashboards via Tableau Cloud or Server is required to access these AI features.

Like many of its competitors, GoodData continues to compete with Tableau by integrating AI into its platform. The introduction of GoodData Labs provides an opportunity for GoodData prospects and customers to explore new AI features that are still in development, including the creation of Large Language Models to assist analytics consumers, analytics engineers, and developers.

What sets GoodData and Tableau apart when it comes to AI?

Tableau recently introduced a metrics layer, thanks to which it is possible to use AI within their platform. It offers customization and automatic updates, and integrates seamlessly with existing Tableau Pulse dashboards, making AI utilization possible across three layers:

• Metrics layer, also known as headless BI, contains all the metrics and KPIs used organization-wide (as the core of Tableau Pulse).
• Insight platform, built on the metrics layer to automatically generate and rank insights in natural language using generative AI.
• Next-gen experience, directly presents user-friendly metrics to users in their workflow. This promotes quick scalability as users discover, follow, and share relevant metrics across platforms like Slack, email, and the Tableau web app.

As a forward-looking alternative analytics platform, GoodData, aligns with both IT and analytics/BI trends. Even before the AI trend it had integrated a metrics store into its platform — a semantic layer component alongside the logical data model (LDM). This store fosters a shared understanding of entities and relationships, enabling analysts' creations to be reused. The metrics store ensures consistent retrieval and versatile analysis by organizing metrics built upon the LDM, with definitions and calculations. These metrics are usable in diverse contexts, sliced, diced, and filtered by different dimensions. Thanks to API-first and Headless BI, the backend and semantic layer are decoupled, allowing metric definitions to be consumed by any data tools (other BI tools, custom apps, or AI/ML apps or models).

To ensure future-proofing, GoodData also leverages an "Analytics as Code" approach, which enables users to create and manage analytics using programming code.Thanks to these capabilities, GoodData utilizes Large Language Models (LLMs) to accelerate the analytics-building process through the automatic generation of analytics objects (such as metrics and visualizations). While LLMs handle code-based structures (as-code approach), GoodData's semantic layer translates technical expressions and metadata into user-friendly terms, ensuring accessibility for non-technical users and enabling the development of diverse AI features.

AI-enhanced features in Tableau and GoodData

The table below offers a side-by-side comparison of the AI features the two competitors offer: