Mage vs. Panoply

Mage is well-suited for probability score for uptake of every product is calculated for customers using ML/ Regression models, choosing customers for a product/ Top products for a customer, based on the requirement and Identifying popular product combinations using association rules from Market Basket Analysis (or affinity Analysis)\Bundle these products as combos.

Incentivized

Panoply is well suited for companies with large amounts of data that do not want all users of the company to see or view. It is appropriate for companies that have to store data for several years in case clients need the data. It is not made to constantly be downloading or looking at the documents.

Incentivized

• Ranking algorithms.
• Cloud-based tool.
• Increase user engagement.

Incentivized

• Ease of data integrations. Importing data into the data warehouse is a point-and-click effort.
• Automated data modeling. The data is modeled in a way that facilitates analysis without having to look into it myself.
• Query speed. Automated query optimization has really sped up our dashboards.

• Acquisition Contribution.
• Business Intelligence Reporting.
• Data Destinations.

Incentivized

• The user interface could use some improvement for analytical tools. Apart from that, it’s great.

It’s a good choice for building a quick, multipurpose data stack for a variety of businesses needs.

Mage was the easiest in terms of ease of implementation due to its no-code functionality. However, Mage doesn't have a whole ecosystem like AWS and slightly falls behind there.

Incentivized

Panoply has much more storage and better organization tools for searching. There are better folders and tools for keeping documents secure and stored for a long period of time. It is better to offload data in mass than singular or Zip drive documents like you would do in Google Drive.

Incentivized

• Business Understanding.
• Data Acquisition and Understanding.
• Data Modeling and Evaluation.

Incentivized

• Saved time on Redshift setup
• Saved support costs (monetary and temporal) by not having to set up on-premise
• Widens the BI tool use in organization; lowers the barrier to entry and makes organization more productive in analytical terms