Financial Services

Learn how financial services organizations leverage the hybrid cloud ready, Pentaho platform for effortless data on-boarding and off-loading, on demand data prep, data engineering, and powerful, insightful analytics.

Read more about the Financial Services industry successes in data automation using Pentaho.

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Effortless Data Onboarding and Offloading

  • Automated  Onboarding - Automate data ingestion and onboarding from many sources using profiled data sources to automatically create data onboarding transformations. Users can cleanse and blend diverse data sources to create analytics-ready data models. Automated onboarding speeds up development, testing, and deployment time by 10x.
  • Access Structured  and Unstructured Data - Easily bring together data from various sources and blend relational data from transactional and analytic databases with unstructured data from Hadoop and NoSQL databases for powerful analytics.
  • Data Warehouse Optimization - Reduce strain on your data warehouse and existing infrastructure and improve the performance of information delivery by off-loading less frequently used data and transformation workloads to Hadoop.
  • Mainframe Offload - BNY Mellon uses Pentaho to operationalize their mainframe data as part of their Hadoop and cloud initiatives.

On Demand Data Preparation and Data Engineering

  • Validate and Cleanse - Standardize and validate data to ensure it complies with business rules and standards. De-duplicate, cleanse and correct inconsistent or redundant data and preview data during the data prep process, without switching applications.
  • Process, Blend, and Enrich - Blend and enrich all data, orchestrate the data pipeline using governance standards, and deliver analytics-ready data sets to end users in the correct format.
  • Lineage, Security, and Metadata – APIs for lineage information, coupled with metadata bridges, provide visibility into data flows and transformations in graphical viewers, or Enterprise Metadata Management tools with centralized and secure control.
  • Machine Learning - Operationalize machine learning by working seamlessly with current languages such as R, Python, Scala and libraries such as Spark MLlib and Weka.

Powerful Analytics

  • Report Automation - Report and send alerts based on a variety of prepared, blended, sliced and diced data. Visualize more than just structured data.
  • Business Logic - Visually design your business logic, data engineering, data orchestration, and visualizations without writing any code or having to consider the underlying storage and computational systems.
  • Data Virtualization – Expedite integration of data not sourced to the hub, by exposing big data in small views only when an application, report or user asks for it.

Hybrid Cloud Ready

A business analytics solution needs to remain open and agnostic to different sources of data to quickly adapt to change. As more data and processing capabilities move to the cloud, companies should have the flexibility to choose from diverse vendors or merge data integration with business intelligence service for accurate processing capacity planning.

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