Tableau is great for data visualization. But as your organization’s analytical needs evolve, pretty charts are no longer enough and you’ll likely run into these Tableau limitations:
- Analytics Bottleneck at Data Team: Tableau requires dedicated analysts to create reports and maintain the tool. As more reports are needed, non-technical users have to rely on data teams for ad-hoc data questions. Analysts tun into “English to SQL” translators.
- Limited Self-service Capacity: Tableau does not allow business users to customize their own setup of dimensions and metrics in the reports they want, causing frustration for users who don’t know SQL but still need to explore data beyond pre-defined views in Tableau.
- Disparate Metric Definition: With Tableau, it’s easy to end up recreating the same metrics with different calculations in different places. As the number of reports grows, metric definitions might become more disparate and inconsistent, making it difficult for the data team to maintain accuracy across multiple reports.
- No Code Version Control: Tableau uses its own proprietary file formats - so there’s no code version control/change process management. One person editing locally and pushing up can overwrite everyone else, making it difficult for a team to manage.
If you face the problems above, Holistics might be a suitable alternative for your organization.
Designed For Self-service
Everyone explores data in a curated environment without writing SQL. No more “request queue frustration” for both business and data teams.
Reusability Of Logic
Analysts manage business logic centrally and define reusable data models in Holistics' semantic modeling layer.
Version Control with Git
Holistics allows data models to be defined as code, enabling tracking of changes between releases and analytics logic to be checked into Git Version control.
How does Holistics work?
With Holistics, data teams manage a central definition of business metrics and data logic in a code-based modeling layer.
Business users build their own reports and get accurate analytics in a curated environment, without having to learn SQL. Dashboards and data logic can also be serialized into code and checked into Git version control repository.
Holistics vs Tableau: Feature-by-Feature Comparison
|Pricing and Budget|
|Payment Terms||Require annual plan lock-ins as only yearly plans are available with hefty upfront payments.||Both monthly and yearly plans are available. Yearly plan at 20% discount off monthly plans.|
|Reporting and Visualization|
|Analytics Approach||Data Visualization focused.||Code-based semantic layer with self-service data exploration.|
|Report Creation done via||Cloud and desktop. Tableau Cloud offers many of the same features as Tableau Server, save for more advanced features.||Cloud; 100% browser-based, works perfectly on all OS (Mac, Linux, Windows)|
|Variety of visualizations||Both Tableau Cloud and Tableau Server offer rich features for creating interactive visualizations. Tableau Cloud, while having a more user-friendly interface, lacks more advanced functionalities to handle larger data sets and complex reports. For example, analysts need to build custom data range calculations and parameters to create Period-over-period comparisons.||25 essential chart types with basic customization options. Custom charts via VegaLite are available for more complex visualizations.|
|Works well with data sources that are||Desktop Spreadsheets, SQL, and a large number of application connectors.||Cloud SQL databases(s).|
|Data Analysts’ Experience|
|Approach Towards Modeling||Tableau’s data modeling approach is not SQL-friendly where SQL is discouraged from using in the workflow. Lacking the flexibility that SQL offers - analysts might find it difficult to work with data that requires more advanced transformations in Tableau.||Data models are designed a first-class SQL experience approach. Easy access to the underlying SQL of all data models provides visibility on how they are created.|
|Semantic Modeling Layer||Called the “Logical Layer” in Tableau - which allows analysts to define relationships between physical tables and group related logical layers into explorable datasets. However, these datasets require root models which limit model reusability.||Analysts manage business logic centrally and define reusable data models then expose them as explorable datasets. Holistics datasets have dynamic root models which allow analysts to handle more complicated analytics use cases.|
|Data Preparation||Data Preparation is prepared through configuring data workflows via a GUI (Tableau data preparation, purchased separately).||Data preparation is done via the automatic execution of SQL transform models (including materialized views).|
|Data Join||Published Tableau data sources cannot be used in joins - so users have to reuse the same code over and over in different join scenarios.||Users can define a virtual join using relationships or create a Transform model with a SQL statement that joins 2 tables - which allows for better reusability of logic and data models can be used in multiple places - helping analysts avoid repeating the same SQL query/logic.|
|Skillsets needed for Data Modeling||Needs Tableau developer skill sets to use it well (which is not transferrable). Typically requires technical DBA to set up and prepare database tables for querying.||Anyone can use Holistics as long as they know how to write SQL queries, without extension engineering knowledge.|
|Git Version Control||No. Without Git version control, it’s difficult to track changes made to Tableau files and users may run the risk of accidentally overwriting or losing important work. Sharing Tableau files without version control can also result in a slower and more error-prone collaboration process.||Yes. Analysts can commit analytics code to Git and track every change, perform branching, code reviews to ensure accurate and rigorous analytics workflow.|
|External Data Delivery|
|Shareable links, without any login (Restrict views to specific email addresses), with password protection and permission control||To view dashboards online, viewers need to log in with their own accounts.||Shareable links can be created and shared easily with external stakeholders without a Holistics user account.|
|Automatic email delivery||Email schedules are only available to Tableau users on a self-service basis, and not available to external stakeholders.||Email schedules can be created to send to any number of recipients, including non-Holistics user|
|Google sheet export (Automatic)||Need 3rd party integration||Available natively within Holistics|
|Automated Slack schedules||Need 3rd party integration||Available natively within Holistics|
|SFTP export schedule||Need 3rd party integration||Available natively within Holistics|
|Embedded Dashboard pricing||Embedded analytics is priced by user count. 20 dashboard viewers would cost $3,600/yr||Unlimited dashboard viewers, available to all plans except Entry.|
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Frequently Asked Questions
How do I know if you guys won't shut down in the future?
How do I extract my report definitions data out of Holistics in the future?
Is Holistics EU-compliant? Do you guys have GDPR?
We also have a standard Data Privacy Agreement provided upon request, please email [email protected] for more information.
Is Holistics SOC2 compliant?
How easy is it to learn Holistics? Does it require advanced training or can users pick it up quickly within few hours?
For Data Consumers: zero learning curve. They can create their own charts and dashboards without writing SQL, using a drag-and-drop interface.
For Data/technical teams: just SQL knowledge is needed in terms of skills.
What SQL data sources do you support?
I'm based in the US/Europe. Will customer support be a problem?