Tableau Reviews

• Visualization
• Data capacity

Where I'm working, we have many reports developed in Microsoft Excel to present results about the business. However, when we need to work a large volume of data, they are developed in Tableau because access to data from Oracle and managing large data volumes is easier than Excel. All the reports that we develop using Tableau are complimented by our directors, as the information is visualized well.

When we need to work with a large volume of data, we can create an extraction, validate data easily, and then present it to our users using Tableau Server or Tableau Reader.

I have difficulty working with many filters on the dashboards, and I'd like to see more options in the "Histories" section. QlikView makes better use of the dashboard filters.

We've used it for two years.

In some departments we chose to install Tableau Reader, but it's necessary to create a ticket to update the software for each new version. This process is not good as it disrupts our day-to-day functions.

I work with 4GB RAM and rarely I have a problem with it. My OS is 32 bits.

We've had no issues with scalability.

I always take support from Tableau's forums and communities.

We also looked at QlikView.

It's a great solution to present data, and our directors love to work with it.

It's easy to start using to deliver off-the-shelf visualizations. It makes it easy to share dashboards with a team. It's a comprehensive solution that can scale from desktop (analyst, developer) to server (large end-user audience) with small or no changes.

In my experience, its greatest achievement has been opening stakeholders' minds. It pioneered the self-serve trend (which may have social roots) where people "own" their data and IT is a service provider to ensure visualization content is based on robust data. Connecting to a massive number of sources (that can be blended in Tableau) has also contributed to this achievement.

It came at a cost. Developers end-up, many times, scratching their left ear with the right hand, in order to make visualizations more powerful, effective or appealing than off-the-shelf ones. The next challenge for Tableau will be combining ease of use for basic-to-mid level complexity design (self-serve, "power users") with flexibility and repeatable development in a corporate environment (professional designers), avoiding "hat tricks" or "cookbook" style.

Also, when building corporate dashboards, it may be challenging to gain the type of control needed without having to resort into some forum tricks or Zen-Master's advice. Workbook, connection versioning, and governance would be great for mid-to-large size organizations. Server-wise, having options to trigger alarms to an Administrator under certain conditions would also be an asset.

I've been using both Tableau Desktop and Server for four years.

No problems at all. Very robust and well documented (both Desktop and Server versions)

No problems, at least with the data sources I have used Tableau so far - MSSQL 2008 & 2012, Teradata RDBMS 14.x, and MSAS Cubes.

Tableau can scale to massive datasets without problems (I used a few, approximately one billion-row tables), provided a DBA is part of the team to fine-tune tables and views. The proprietary columnar database engine is very fast and robust.

It's great.

It's great, although forums are usually the first resource to get answers from.

I have helped customers migrate from MS Pro-Clarity and Oracle Hyperion.

Tableau is not a demanding platform in pre-set environments before rolling-out as long as tech specs are met. However, certain design decisions need to be made in advance to ensure good performance and smooth scalability on a Server deployment (e.g. permissions, standards, etc.).

I act as an implementation consultant.

Go through the exercise of planning short and mid-term to understand what the best licensing model is e.g. per Core vs. Named Seats, number of Designers, etc.

Understand your organization's profile. Do you have or want/need to develop power-users or will you use traditional IT developed dashboards in an intranet/internet? Although Tableau can fit both scenarios, the first option would be the most benefited one when compared to other solutions. Look into redefining the relationship between business stakeholders and IT, since the former will demand more consumable information for analysis instead of packaged reports.

The use is only academic, for the moment.

I've used it for six months.

No issues encountered.

No issues encountered.

No issues encountered.

I've not needed to contact them.

I've not needed to contact them.

It was easy to setup.

We did it in-house.

• BIME
• QlikView
• SiSense

Data Preparation - This is a huge plus for Tableau as so many users have to spend time preparing the data before using Tableau

• Data Interpreter
• Splits -
• Really helps the average user

Drag and drop analytics:

Tableau 9.0 has a new Analytics pane that provides quick and easy access to common analytic features in Tableau.

You can drag reference lines, forecasts, trend lines, and other objects into your view from the Analytics panel. It is now easy to edit, format, and remove the analytic items that you have added, so you can experiment with different techniques as you explore the insights your data has to offer.

Ad--‐hoc calculations:

Ad--‐hoc calculations make it easy to add and edit calculated fields for your analysis. Double--‐click an existing field on the Rows, Columns, Marks, or Measure Values shelf to begin editing, or

double-click an empty area on a shelf to create a new calculation. As you type, a list of auto--‐complete options appears in a dropdown list, making it easier to find and pick the right elements.

Instant analytics:

Instant analytics provides an interactive experience for comparing summary information about a subset of marks to all the marks in your view. For example, you can compare the average for a few marks to the average for all the marks. After you’ve added trend lines, reference lines, reference bands, or distribution bands to your view, select one or more marks to see the new analytical indicators appear for the selection in addition to the analytical indicators for the whole view.

Level of Detail (LOD) Expressions:

New expression syntax in the Tableau calculation language lets you quickly create calculated fields that compute at the specified levels of detail. LOD calculations help you compute at multiple levels of aggregation and make it easier to create fields for analytical comparison (such as cohort analysis and totals or Average across segments), simplifying calculations that previously took several steps.

It's both intuitive and complex/deep. For our organization, it's given us opportunities to provide both on-sight and on-line training. We now have an online training product that more than offsets the expense associated with days of training. With Tableau, our customers more analytical.

It even has a connection to R and SAS for advanced users.

Also, it has given us the ability to collaborate with the Tableau servers, which is robust and can handle tens of thousands of users.

Tableau Server can now scale for unlimited users Enterprise scale brings with it the need for Governance. Data sources and workbooks must be vetted before they are "out there" to see except for the intended content consumers

The basic design of Tableau has some features that could be modified.

* There is no need to have five (5) ways to add a new worksheet. Brevity is rewarded - new users have so much to learn that 2 ways would do the job

* How to render time - When performing analysis, no single issue is more important than time series data. Tableau uses visual clues in many places - When one uses the pull down option for time that could be visualized better as a continuum. those selections should be presented in green to be visually consistent. Those above in blue.

If you're making a structured, strategic purchase, make sure that you have a plan for professional training.

• Go slow and methodically
• You must consider size of the company and types of users
• Desktop Plus server users - understand the constraints on the backend, and make sure you have enough CPU power
• There's lots of free stuff on their website that's great for the average user.

• Ease of data visualization
• Ease of dashboard creation
• Ease of dashboard sharing

The data was made clearer for us through visualization. In addition, it allowed us to automate result monitoring.

The integration and usage of multiple data sources is sometimes not that straightforward.

I've used it for six months.

All connections were to a SQL Database, and there were no issues with deployment.

No issues with stability.

No issues with scalability.

Did not need it.

Did not need it.

We already had OLAP Cubes in place, but we haven't switched and are currently using both.

It was straightforward.

It has given us faster reporting, resulting in better management of the information, and better usage of information in decision making.

Definitely understand your data structure before using Tableau. Tableau allows you to manipulate and depict data in various ways, as long as you have proper, well structured input.

It has ease-of-use features that make it a good data discovery tool for use with our clients. Additionally, the ability to rapidly mine data and visualize data issues for clients is valuable.

We can use this tool with our internal data as well as on client sites to rapidly evaluate data sets and gain understanding of patterns rapidly.

The integration of multiple data sources has sometimes been difficult and the size limitations are sometimes an issue.

We have been evaluating this product for approximately six months now.

No issues encountered.

No issues encountered.

We are very early in our project but do not see any major limits yet.

Customer service has been excellent.

Technical support is very good.

No previous solution was in place.

The connections to the SQL data and flat files were straightforward.

Implementation was all done in-house.

Unknown at this time.

• Tableau
• PowerPivot/PowerBI
• Birst
• Sisense

1. Understand your business hierarchies intimately.

2. Create flattened data files for roll-ups.

3. Be prepared to clean and process data prior to use.

• It is very powerful
• Flexibility
  

Minor issues only. My biggest one is control over the order in which filters and sorts are applied. There is some control of this but not enough.

I've used it for seven months.

No issues encountered.

No issues encountered.

No issues encountered.

It's very good.

Our parent company has its own internal Tableau support team so I have had no contact with Tableau’s own technical support

No previous solution was used in this company, but I do have experience of Domo.

It was complex. We have a complex situation with extensive data security set up at three and a half levels across over 100 active users with most of the users not only controlled by level but also having access to very specific sets of data only.

Inevitably the setup for this in Tableau was complex. This is not a criticism of Tableau however – just the opposite, the impressive thing is that it had the flexibility to achieve it at all

I implemented it myself.

Tableau is a very powerful and flexible product, it also demands some level of skill on the part of the user. When compared with Domo for example, the end result in its standard form is more flexible, is able to put more information in a single view, and offers more user interactivity. However the other side of this coin is that in its standard form, it is much less accessible to less skilled users. We have managed to develop an interface that is as accessible as Domo, but this has been quite a complex and time-consuming task.

All the features are valuable.

We plan to empower users with Enterprise BI and Self Service BI by Tableau.

It is still in our evaluation to replace our old BI tools.

We used Infobeacon and now we are using Cognos and Microsoft BI. We are currently looking for a better product.

Quality of data, flexibility and the performance of the tool(s) are the critical aspects of BI. Test the critical aspects by doing a proof of concept using a huge amount of data.