BigID vs. erwin Data Modeler vs. IBM Cloud Pak for Data

Add context to data to get a bigger picture and make decisions regarding compliance, privacy, coverage, security, and governance.

Incentivized

I have had a chance to use few other data modeling tools from Quest and Oracle, but I am most comfortable using erwin Data Modeler. They understand your data modeling needs and have designed the software to give you a feeling of completeness when you are designing a data model.

Incentivized

IBM Cloud Pak for Data with Netezza is well suited for clients who require fast, economical analytics processing. It is not designed to be used as a transactional processing environment. For example, a large customer is using it during the point of sale process. That makes little sense in that business case. However, to take analysis to market faster, it excels well in that space.

Incentivized

• Coverage for all types of data
• Ease of use
• Good support

Incentivized

• Reverse Engineering: I love the way we can import an SQL file containing schema meta data and generate ER diagram out of it. This is specifically useful if you are implementing erwin Data Modeler for an existing database.
• Forward Engineering: We use this feature very frequently. Where we do database changes in our physical and logical data models and then generate deployment scripts for the changes made.
• Physical vs Logical Models: I like to have my database model split into physical and logical models and at the same time still linked to each other. Any changes you make to logical model or physical model shows up in the other.

Incentivized

• Increases our impact by combining BI skills with advanced analytics and machine learning in an easy to use visual interface.
• Visualization and reporting.
• Rapidly provides business -ready data to all users equally.
• Manage data spread across distributed stores and clouds.

Incentivized

• Mobile application isn't that user friendly
• Doesn't work with some legacy systems
• Blips and or latency in every day use.

Incentivized

• Cost is the single largest barrier to entry with this tool.
• Would like to see a greater variety of export options available for use outside of the tool, for example as a vector graphic.
• Support has been uneven at times.

Incentivized

• Cannot save changes to some secrets in the internal vault
• Sign-in issues on environments where IAM is enabled
• The Enforce quotas option is disabled

Incentivized

I had a lot of experience using erwin Data Modeler for designing data models. I think it's pretty intuitive and easy to use. It has enough features to represent your database requirements in form of a model.

Incentivized

CA customer support and our account manager have been able to support us with any issues that we have had, from managing our serial keys to issues we logged tickets to resolve. There are aspects of key management that have made it difficult over the years but support usually has worked with us.

Incentivized

Affordable, scalable, seamlessly integrates with other applications, easy to configure and use. Great customer and product support. Affordable extended product support.

Incentivized

Not listed, but I've only used alternatives built into something like the Squirrel SQL editor. That one is semi-functional but lacking many features and, in some instances, just plain wrong. The only pro there is that it's freely available and works over ODBC. I've tried some of the other free ones like Creately but didn't have much success.

Incentivized

IBM Cloud Pak for Data takes the IBM Cognos solution and provides this on an enterprise cloud platform that can be extended to support better data integration and data science capabilities.

Incentivized

• Manage Enterprise data bolume
• Manage data sources
• Low impact on our system

Incentivized

• It is difficult to assign a monetary value to it. The initial learning curve was fairly long.
• It allows us to have better support documentation.
• It allows ease of communications between functional units.

Incentivized

• We have the ability to access all our data much quicker through the unified search option.
• 30% increase in productivity through the introduction of AI.
• Improved data security and governance.

Incentivized