ER/Studio vs. IBM SPSS Modeler

Data Architect is well suited at organizations of all sizes. It is never too early or unnecessary to enforce proper modelling and design standards on data solutions, and this tool will help that greatly by providing an industry leading data modelling tool, ability to import ETL mappings for data lineage, enforcing and managing naming conventions through the naming convention tool, and publishing of data dictionaries through the report publisher. I was successfully able to build models, provide traceability, and document source to target with lineage throughout for both the business (by providing business definitions in the descriptions), and technical teams (by documenting ETL instructions in text fields) along with field level mapping (by creating "Attachments" representing data sources, tables, and fields) providing easy search capabilities using business friendly terms

Incentivized

Fast NLP analytics are very easy in SPSS Modeler because there is a built-in interface for classifying concepts and themes and several pre-built models to match the incoming text source. The visualizations all match and help present NLP information without substantial coding, typically required for word clouds and such. SPSS Modeler is good at attaining results faster in general, and the visual nature of the code makes a good tool to have in the data science team's repository. For younger data scientists, and those just interested, it is a good tool to allow for exploring data science techniques.

Incentivized

• ER/Studio has the ability to provide consistent field names and data types through domains, which are templates. This provides a way to have consistent naming of common fields, like CreatedBy and the data types for the fields. They also have the ability to change all the fields that use that domain to a different data type.
• ER/Studio provides the ability to create custom macros. These macros can be used to apply everything from standard fields based on domains to naming all constraints and indexes. I've also used a macro that comes with ER/Studio to spell check field and table names.
• My favorite feature is the ability to compare your data model to databases for deployments of changes, and to other data models.

Incentivized

• Combine text and data
• Provide facilities for all phases of the data mining process.
• Use a node and stream paradigm to easily and quickly create models.

Incentivized

• ER\Studio licensing can be cumbersome and upgrading from one version to another usually takes several phone calls and emails to the licensing group to get the update installed and running.
• The repository can be slow when the model count gets larger. By large I mean 20 to 30 models.
• A nice feature that I would like to see is table comments be displayed on the model along with the attributes. Currently you have to choose between the two.

Incentivized

• Has very old style graphs, with lots of limitations.
• Some advanced statistical functions cannot be done through the menu.
• The data connectivity is not that extensive.
• It's an expensive tool.

Incentivized

The ability to do predictive modeling, text analytics for both structured & unstructured data, decision management, optimization, and support for various data sources

Incentivized

I can call or email support and both get quick turn around. The only issue is they are on the west coast (US) and have a west coast work schedule and I'm on the East coast.

Incentivized

The online support board is helpful and the free add ons are incredibly appreciated.

Incentivized

ER/Studio is the most mature, reach in features and quality and therefore useful. Unfortunately, it lacks compatibility with AWS Redshift.

Incentivized

When it comes to investigation and descriptive we have found SPSS Statistics to be the tool of choice, but when it comes to projects with large and several datasets SPSS Modeler has been picked from our customers.

Incentivized

• ER/Studio has had a positive impact on my project as we can develop the data model and have a clear understanding of business needs before we continue with the development phase.

Incentivized

• Positive - Ease of decision making and reduction in product life cycle time.
• Positive - Gives entirely new perspective with the help of right team. Helps expanding the portfolio.
• Negative - Needs to have good understanding about mathematical modelling, of which talent is rare and expensive. Hence, increase the costs for R&D and manpower.

Incentivized