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.
$70
per month
Workday Prism Analytics
Score 8.1 out of 10
N/A
Workday Prism Analytics extends the core capabilities of Workday. The product promises to allow users to generate finance and HR insights from any data source, all within Workday, and securely distribute it to the entire organization. Workday Prism Analytics includes data preparation, data management, and data discovery tools.
Tableau Desktop is one the finest tool available in the market with such a wide range of capabilities in its suite that makes it easy to generate insights. Further, if optimally designed, then its reports are fairly simple to understand, yet capable enough to make changes at the required levels. One can create a variety of visualizations as required by the business or the clients. The data pipelines in the backend are very robust. The tableau desktop also provides options to develop the reports in developer mode, which is one of the finest features to embed and execute even the most complex possible logic. It's easier to operate, simple to navigate, and fluent to understand by the users.
In my organization, we mainly use Workday Prism Analytics in HR and Finance departments. It not only enables us to make data-centric decisions but also helps reduce the need for data experts since we are able to visualize data on our own through self-service analytics.
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.
It's web based. No need to install any desktop clients on your machine to use platfora.
It's best suited for a big data Hadoop environment. I can rate it as the #1 BI tool for a big data hadoop environment.
Platfora follows kind of the same architecture as Hadoop architecture like Master and Slave architecture. It scales with the data volumes.
Querying data is very good and very fast. (Platfora Lens)
Client presentation wise it's good. You can get different kinds of graphs.
Platfora almost supports everything on Big Data technologies including file formats, compression etc.
Security is not compromised and it can deal in parallel with any Hadoop distributor security implementations. Just take an example of Knox on Hortonworks, so it will deal with that and cloudera , MapR
Its very easily understandable and for the new people who wants to try platfora, learning curve is low
You can create your own datasets in platfora. You can store your results as a dataset in platfora and can share across
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.
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
I have never really used support much, to be honest. I think the support is not as user-friendly to search and use it. I did have an encounter with them once and it required a bit of going back and forth for licensing before reaching a resolution. They did solve my issue though
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.
The training for new users are quite good because it covers topic wise training and the best part was that it also had video tutorials which are very helpful
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.
If we do not have legacy tools which have already been set up, I would switch the visualization method to open source software via PyCharm, Atom, and Visual Studio IDE. These IDEs cannot directly help you to visualize the data but you can use many python packages to do so through these IDEs.
Both are great products. The advantage of SAP Workforce Analytics is that it's widely interoperable between different APIs and databases. Having said that, Workday Prism Analytics scores much better in user-friendliness and the learning curve for the teams to start using it is very low. If Workday enhances its APIs functionality, it can compete easily with SAP Workforce Analytics.
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.
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.
