Datastreamer vs. Mage

Datastreamer has great competency in aggregation and classification of large amounts of unstructured, conversational/social data. We perform media monitoring on social media data which is infinitely large and changing every second. Datastreamer is able to stream that high volume of complex data reliably. There are other solutions better suited for small data movement efforts. The AI models and operations set Datastreamer apart from simple web API's that only collect data and pass it on without augmenting it's value. Very appropriate for organizations looking to use this type of information to understand and classify sentiment, identify themes/insights to assist in decision making across multi-department roles in an organization: PR, marketing, security etc.

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

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

Incentivized

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

Incentivized

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

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

Incentivized