Longview Analytics (arcplan) vs. Tableau Desktop

It is very suitable for complete corporate BI solutions. If you see BI as embedding information into your organization and business processes, and thus as more than just a few scattered dashboards and reports, arcplan is your platform. It's less suitable for ad-hoc reporting and data discovery. It can do it (everything is possible), but there is strong competition in this area.

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.

• Excellent dashboard.
• Well advanced BI metrics.
• Better interfacing to other DW.

Incentivized

• 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.

Incentivized

• Positioning and marketing. Most BI vendors use a non-technical sales strategy and focus on shiny, sexy dashboards to sell the story. In order to position arcplan fairly and correctly, the technical and business advantages need to be part of a sales story. So, it is not really an easy product to sell to customers, if it has to compete with now-to-wow-five-minute products.
• Learning/training for developers. It's easy to learn the basics of arcplan, because the interface is logical. But nothing prevents a beginning developer from creating a monstrous application, because there is no prescribed architecture. It really takes some experience to become a good arcplan architect. It's a disadvantage resulting from one of arcplan's biggest advantages.
• Data connectors: arcplan standard comes with a connector of your choice. Additional connectors need to be purchased separately. Many other products come with a full range of connectors.

• Formatting the data to work correctly in graphical presentations can be time consuming
• Daily data extracts can run slowly depending on how much data is required and the source of the data
• The desktop version is required for advanced functionality, editing on [the] Tableau server allows only limited features

Incentivized

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.

Incentivized

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

Incentivized

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

Incentivized

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

Incentivized

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.

Incentivized

Personally, I prefer QlikView not because it's more stable but simply just for the looks of the dashboard. One of my clients in Sydney recently moved from arcplan to QlikView. The reason was mainly looks and that QlikView has more training materials for the support users to train with. So in all, it does not really stack well against QlikView.

Incentivized

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.

Incentivized

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.

Incentivized