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Tableau Prep Reviews and Ratings

Rating: 8 out of 10
Score
8 out of 10

Community insights

TrustRadius Insights for Tableau Prep are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Pros

Effective Data Synchronization: Several users have praised the product's data synchronization feature, which has been described as effective in keeping data up to date and accurate across different platforms or systems.

Reliable Matching Capability: Reviewers have consistently mentioned the product's reliable matching capability, allowing them to easily identify and link similar data points or records. This feature has proven to be valuable for users in their data processing tasks.

Clear Display of Raw Data: The clear and informative display of raw data coming in from the data warehouse has impressed many users. This feature enables them to gain insights into the initial state of the data, facilitating better decision-making and analysis.

Reviews

7 Reviews

Tableau Prep - A Necessary First Step

Rating: 9 out of 10

Use Cases and Deployment Scope

We have use Tableau Prep to get datasets ready for our designers to use in Tableau. Our junior designers don't necessarily have a prior skillset in data management, but a lot of features in Tableau Prep such as filtering and cleaning up data has made it a lot easier for them.

Pros

  • Importing data
  • Auto cleaning datasets
  • Troubleshooting

Cons

  • Cleaner UI
  • More tutorials within the program
  • Different ways to link data sets

Likelihood to Recommend

When there is a lot of data that is being handled that needs to be visualized, it's best to run the dataset through Tableau Prep before using Tableau. You don't need to be an expert to use it, but you do need to at the very least watch some tutorial videos. Despite this, the learning curve is very simple compared to other programs.

Vetted Review
Tableau Prep
1 year of experience

Powerful, convenient and easy!

Rating: 9 out of 10
Incentivized

Use Cases and Deployment Scope

Tableau Prep is used for re-creating an ETL workflow to connect data sources, clean data and then combining it to perform joins, and in generating a final output to use operations like pivot, join, rename, union and the control flow plane. Tableau Prep also helps with exploring the data to figure out data quality issues and in creating calculated fields for analysis.

Pros

  • Data Cleansing
  • Automation
  • Joins

Cons

  • Use of Macros within Workflow (and more types of automation)
  • Join Editor also giving a SQL Update Query
  • More types of visuals

Likelihood to Recommend

Tableau Prep is a very powerful tool when it comes to creating workflows to monitor each stage for data analysis and quality issues. Also, it is convenient to explore the data with the wealth of operators and checking work at each stage of the process. Also, some areas that would help is if there are more types of automation provided.

Vetted Review
Tableau Prep
9 years of experience

Tableau Prep is great prep for your work!

Rating: 8 out of 10
Incentivized

Use Cases and Deployment Scope

[Tableau Prep] is used by the commercial side of our organization to clean up data sets before we create dashboards or run analytics/data science projects. Some of the data sets can have missing or erroneous values and Tableau Prep helps to point out such situations so that we can address them before creating visualizations.

Pros

  • Display the raw data coming in from the data warehouse
  • Point out situations that might be erroneous
  • Show the distribution of raw data figures

Cons

  • Should allow you to connect to more databases and platforms
  • Allow clearer initial visualizations
  • Run through data quicker

Likelihood to Recommend

If your data sets are coming in without much stewardship then Tableau Prep can help to clean the data before you start trying to create visualizations for your end users. You will save a lot of time this way - rather than seeing problems once you are creating dashboards. If you don't have large data sets or your data is relatively simple, then Tableau Prep may not be needed.

Vetted Review
Tableau Prep
1 year of experience

Good basic ETL option

Rating: 9 out of 10
Incentivized

Use Cases and Deployment Scope

We use it to prepare data from multiple sources for report generation.

Pros

  • Clean data
  • Join data files
  • Create calculated values
  • Output to CSV

Cons

  • More output options
  • Allow SQL code to input data files for prep
  • Allow row level edits/deletes

Likelihood to Recommend

Great for pulling in multiple files to combine and clean for an output file for further analysis. Not good if you need to query your database for a data set to review and prep for analysis.

Vetted Review
Tableau Prep
2 years of experience

Tableau Prep your way to cleaner data!

Rating: 7 out of 10
Incentivized

Use Cases and Deployment Scope

We have been using Tableau Prep to prepare simple data sets for analysis in Tableau. We do not use it for more complex data but it definitely helps with smaller, simpler data sets. we have most of our Tableau desktop users utilizing Tableau Prep when needed, but when we do more complex data cleaning and prep we use Alteryx.

Pros

  • Easy to use
  • Integrates perfectly with Tableau

Cons

  • Can be sluggish when making a lot of changes
  • Not as robust as other software on the market

Likelihood to Recommend

Tableau Prep is a great software if all you need to do is prep some files and do some simple joins and other simple data prep steps in order to get your data in Tableau shape. However, if working with large sets of data and doing more complex calculations and other more complex data prep, there are better products on the market.

Vetted Review
Tableau Prep
2 years of experience

Great tool for data cleaning, manipulation and consolidation--and a huge time saver

Rating: 9 out of 10
Incentivized

Use Cases and Deployment Scope

We use Prep in our finance department to connect to our live data (Microsoft GP) to consolidate with our budget files to produce an output that is clean and thoroughly categorized and ready to be used consistently across Tableau workbooks, allowing us to more easily produce reports to be published on our server. We also use Prep to pull in several invoices (for example, cellular, shipping, etc) to reformat the output such that it is organized and clean for accounts payable to enter into our system.

Pros

  • Data cleaning.
  • Data exploration/validation.

Cons

  • Automated flows.
  • Bug fixes with updates.

Likelihood to Recommend

Tableau Prep is a great tool for consolidating budget with actual data. When joining sources, it gives you all the insight and transparency you need when joining data sources and any cleaning/aggregation needs to be done. If financial data comes from multiple sources (ex. a budget platform and an accounting software), Prep allows your organization to build a flow to consolidate the two sources so that everyone can open a workbook and begin from the same point. Once flows are built (especially when connected live), refreshing reports can be done with a click of a button. The capabilities Prep has to manipulate, clean and join data goes far beyond what was possible in Workbook. The extracts that are created as a single output allow working with multiple data sources in a single workbook to be a lot smoother and quicker.

Vetted Review
Tableau Prep
2 years of experience

Great product for the right task!

Rating: 5 out of 10
Incentivized

Use Cases and Deployment Scope

We did a pilot to use prep to sync data between our reservoir engineering software/database for forecasts -PHDWIN and actuals in SAP. The prep tool was used to sync the datasets and prepare them for visualization. This was used instead of using Tableau straight to sync the datasets. The outcome of the pilot was that the tool was more robust, but we chose not to do it in order to add more software/tools to the data chain.

Pros

  • Sync data
  • Match
  • Repeatable results

Cons

  • A stand alone product vs integrated
  • More training of a specialized tool

Likelihood to Recommend

I think that if the data sets are constantly changing then the value added would be more significant than in our case. If data and schema were constantly changing then the toolset would be much more suited for the case.