With Data Streams, you can take full advantage of the Mapp Intelligence raw data with three focal points:

  • Data democracy: Data Streams allow organizations to implement real data democracy. The content of each stream can be prefiltered to include only data most relevant to the individual needs of the recipient, such as the streaming of unfiltered raw data for BI or campaign and cost data for Marketing. All decisions can be made from a single source of truth.
  • Data governance: With the help of streaming architecture Data Governance can be optimized. This means internal stakeholders can receive only the data that is relevant and needed for their work. For instance, sensitive information or data that falls under compliance regulations can easily be omitted before making the stream available. This way, sensitive financial or personal data can be protected from unauthorized access.
  • Faster insights (leading to more rapid data-driven actions)

Below you can find the most relevant use cases to be fulfilled with Data Streams:

Use CaseDescription
Fraud detection

Near real-time fraud detection models can be implemented using the Data Streams as a data source.
Example: Determining whether a transaction is fraudulent while the transaction is still in progress and not after the fact.


Scoring models (finance), recommendations (e-commerce).
Example: Instant recommendations using real-time mobile device data based on end user's location.

Enterprise reporting/ BI

Data distribution to the the parent company and/ or various organizational stakeholders internally as well as real-time reporting and dashboard implementation.

Example: Would like to enrich Mapp Intelligence Raw Data with my organizational data sources (CRM systems, etc.) and push it to my preferred BI solution.

Spam/ Bot detectionSpam/ bot detection using streaming data.
A/B TestingHigh-frequency, self-service A/B testing.
ETL Automation/ Reduction of repetitive ETL processesThe streaming raw data could be directly ingested into any internal stream, aggregated and/ or persisted to DWH, merged with other data, etc. without needing to go through batch processing, providing the customer flexibility and more options to work with.
Streamlined implementationsImplementation and development efforts with less hassle such as hot deployment of data processing bug fixes, data science models, new pipelines, etc.