IBM Bob is an AI-assisted development tool designed to support developers across the software development lifecycle. It helps interpret developer intent and provides assistance when working with real-world codebases by using contextual awareness of the surrounding project.
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Tableau Desktop
Score 8.5 out of 10
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Tableau Desktop is a data visualization product from Tableau. It connects to a variety of data sources for combining disparate data sources without coding. It provides tools for discovering patterns and insights, data calculations, forecasts, and statistical summaries and visual storytelling.
$1,380
per year (purchased via a Creator license)
Pricing
IBM Bob
Tableau Desktop
Editions & Modules
No answers on this topic
Tableau Creator License
$115
per month (billed annually) per user
Offerings
Pricing Offerings
IBM Bob
Tableau Desktop
Free Trial
No
No
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
Yes
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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All pricing plans are billed annually. A Creator license includes Tableau Desktop, Tableau Prep Builder, and Tableau Pulse. Discounts sometimes available for volume.
The first time using it feels like magic. Its ability to analyze an existing codebase and adapt accordingly to questions, feature requests, issues, etc. is remarkable. However, it is not limited to an existing codebase - I've had it create new applications based on styling from another application from scratch. It has quickly gone from an amazing novelty to something that is relied upon daily.
The best scenario is definitely to collect data from several sources and create dedicated dashboards for specific recipients. However, I miss the possibility of explaining these reports in more detail. Sometimes, we order a report, and after half a year, we don't remember the meaning of some data (I know it's our fault as an organization, but the tool could force better practices).
An excellent tool for data visualization, it presents information in an appealing visual format—an exceptional platform for storing and analyzing data in any size organization.
Through interactive parameters, it enables real-time interaction with the user and is easy to learn and get support from the community.
Our use of Tableau Desktop is still fairly low, and will continue over time. The only real concern is around cost of the licenses, and I have mentioned this to Tableau and fully expect the development of more sensible models for our industry. This will remove any impediment to expansion of our use.
It works well, for the most part, but asking for authorization for multiple steps or tool installation and use sometimes becomes bothersome. You can grant blanket authorizations but I do prefer to have a tradeoff there so not all is bad, but sometimes I get the impression that we get asked the same question several times during the same workflow.
Tableau Desktop has proven to be a lifesaver in many situations. Once we've completed the initial setup, it's simple to use. It has all of the features we need to quickly and efficiently synthesize our data. Tableau Desktop has advanced capabilities to improve our company's data structure and enable self-service for our employees.
When used as a stand-alone tool, Tableau Desktop has unlimited uptime, which is always nice. When used in conjunction with Tableau Server, this tool has as much uptime as your server admins are willing to give it. All in all, I've never had an issue with Tableau's availability.
Tableau Desktop's performance is solid. You can really dig into a large dataset in the form of a spreadsheet, and it exhibits similarly good performance when accessing a moderately sized Oracle database. I noticed that with Tableau Desktop 9.3, the performance using a spreadsheet started to slow around 75K rows by about 60 columns. This was easily remedied by creating an extract and pushing it to Tableau Server, where performance went to lightning fast
Tableau support has been extremely responsive and willing to help with all of our requests. They have assisted with creating advanced analysis and many different types of custom icons, data formatting, formulas, and actions embedded into graphs. Tableau offers a weekly presentation of features and assists with internal company projects.
It is admittedly hard to train a group of people with disparate levels of ability coming in, but the software is so easy to use that this is not a huge problem; anyone who can follow simple instructions can catch up pretty quickly.
I think the training was good overall, but it was maybe stating the obvious things that a tech savvy young engineer would be able to pick up themselves too. However, the example work books were good and Tableau web community has helped me with many problems
Again, training is the key and the company provides a lot of example videos that will help users discover use cases that will greatly assist their creation of original visualizations. As with any new software tool, productivity will decline for a period. In the case of Tableau, the decline period is short and the later gains are well worth it.
ChatGPT just did not have the breadth and depth of insight for my coding needs. To get an approximation of what I wanted I had to restate my prompt multiple times, and it looked almost like it did not learn from one time to the other.
I have used Power BI as well, the pricing is better, and also training costs or certifications are not that high. Since there is python integration in Power BI where I can use data cleaning and visualizing libraries and also some machine learning models. I can import my python scripts and create a visualization on processed data.
Tableau Desktop's scaleability is really limited to the scale of your back-end data systems. If you want to pull down an extract and work quickly in-memory, in my application it scaled to a few tens of millions of rows using the in-memory engine. But it's really only limited by your back-end data store if you have or are willing to invest in an optimized SQL store or purpose-built query engine like Veritca or Netezza or something similar.
We've been able to grow our own development team from within as a result of using this technology. Team members who may not have been confident enough to meaningfully contribute to the existing codebase have now become high-output developers.
We've been able to grow an automated test suite to more thoroughly test our mobile application. What once took two weeks of manual regression testing can now be done in an afternoon.
Our documentation has improved considerably, giving the product a more polished feel. Product documentation had previously consistently lagged behind new feature development.
Tableau was acquired years ago, and has provided good value with the content created.
Ongoing maintenance costs for the platform, both to maintain desktop and server licensing has made the continuing value questionable when compared to other offerings in the marketplace.
Users have largely been satisfied with the content, but not with the overall performance. This is due to a combination of factors including the performance of the Tableau engines as well as development deficiencies.
