In-Database Technologies

Business Services

In-Database Technologies

Manage data where it lives

  • Automatic publishing client.

    • Automatically translates and publishes models as programs inside the database.

  • In-database execution.

    • Enables SES model-scoring computations to execute fully inside the database.

  • SES Enterprise Miner model support.

    • Supports a robust class of SES Enterprise Miner predictive and descriptive models, including the preliminary transformation layer (e.g., data imputations).


    In-Memory Statistics for Hadoop

    In-Memory Statistics for Hadoop is an Interactive, in-memory programming Performs all mathematical calculations in memory.

    Insurance Analytics Architecture

    Ensure consistency, reduce costs and data preparation time, and make fact-based decisions.Insurance data model

    Integration Technologies

    Expanding your choices for integrating SES intelligence.Integration of synchronous and asynchronous business processes

    Intelligence and Investigation Management

    Enhance law enforcement and public safety with a structured environment for collecting, managing

    Inventory Optimization Management

    Manage inventory to meet customer requirements while reducing costs. Data management Data from disparate sources of product distribution

    IT Resource Management

    Optimize IT resource capacity and performance across the enterprise.Interactive data integration environment

    In-Memory Statistics for Hadoop

  • Interactive, in-memory programming
  • Performs all mathematical calculations in memory.
  • Uses a dynamic group-by processing operation to compute and process results for each group, partition or segment without having to sort or
    index data each time.
  • Provides a new web-based interface.
  • Interactive programming language supports submitting, retrieving results and then submitting more statements on the fly.
  • Chains together analytical tasks as a single in-memory job without having to reload the data or write out intermediate results to disks.
  • Lets you update source tables with new column transformations and filter rows, and perform group-by processing.

    • Insurance Analytics Architecture

    Ensure consistency, reduce costs and data preparation time, and make fact-based decisions.

    Insurance data model

  • Serves as a single version of the truth, covering all key insurance subject areas.
  • Stores historical information at a granular level.
  • Provides a comprehensive dictionary that describes insurance data elements.
  • Maps all physical data structures to business terms.
  • Includes both logical and physical data models – e.g., ERwin data models and SAS metadata.
  • Can be deployed in multiple databases, including SAS, Oracle, Teradata and DB2.
  • Provides business data definitions that are consistent with global insurance data standards, such as ACORD.
  • Supports a variety of business issues, including Solvency II.
  • Supports P&C (both personal lines and commercial lines) and life products, and has the flexibility to extend to new lines of business.

    • Data management

  • Accesses data from virtually any system in any form.
  • Embeds data quality into batch, near-real-time and real-time processes.
  • Cleanses data in native languages, with specific language awareness and localizations for more than 20 worldwide regions.
  • Contains customized and reusable data quality business rules that can be accessed directly within process job flows.
  • Handles data migration and synchronization federation projects.
  • Includes wizards for accessing source systems, creating target structures, importing and exporting metadata, and building and executing data extraction, transformation and loading (ETL) process flows.
  • Provides a dedicated GUI for profiling data and identifying and repairing source system issues, while retaining the business rules for later use in the ETL processes.
  • Has enterprise connectivity to data sources – AS400, ODBC, IBM DB2/UDB, Informix, Microsoft Access, Excel, SQL Server, Netezza, Oracle, Sybase, SAS, Teradata and more.
  • Supports both unstructured and semi-structured data.
  • Uses out-of-the-box standardization rules to conform data to corporate standards, and lets you build customized rules for special situations.
  • Migrates or synchronizes data between database structures, enterprise applications, mainframe legacy files, text, XML, message queues and a host of other sources.
  • Joins data across sources for real-time access and analysis.

    • Reporting and business intelligence

  • Provides a Web-based, interactive reporting interface for business users.
  • Includes query capabilities for all levels of users across multiple BI interfaces.
  • Slices and dices multidimensional data using a special slicer dimension and by applying filters on any level of a hierarchy.
  • Displays performance results via critical first-alert, call-to-action dashboards.
  • Provides dynamic business visualization tools for interactive data exploration, visual queries and more.

    • Integration Technologies

    Expanding your choices for integrating SES intelligence.

    Integration of synchronous and asynchronous business processes


    Interoperability with enterprise directory servers


    Intelligence and Investigation Management

    Enhance law enforcement and public safety with a structured environment for collecting, managing and analyzing intelligence data.
    Data management

  • Provides an insurance-specific fraud data model.
  • Consolidates historical data from internal and external sources – claims systems, watch lists, third parties, unstructured text, etc.
  • Eliminates or reduces redundant or inconsistent data with the solution’s built-in data quality tools. Seamlessly integrates with existing third-party systems.

    • Advanced analytics with embedded AI and machine learning

  • Provides a broad set of modern statistical, machine learning, deep learning and text analytics algorithms from within a single environment
  • Enables you to improve fraud models by testing different approaches in a single run, and comparing results of multiple supervised learning algorithms with standardized tests.
  • Provides an array of analytical capabilities, including clustering, different types of regression, random forests, gradient boosting models, support vector machines, natural language processing, topic detection and more.
  • Continuously updates and improves models based on prior output results.

    • Intelligence and Investigation Management

  • Enhance law enforcement and public safety with a structured environment for collecting, managing and analyzing intelligence data.

    • Data management

  • Provides an insurance-specific fraud data model.
  • Consolidates historical data from internal and external sources – claims systems, watch lists, third parties, unstructured text, etc.
  • Eliminates or reduces redundant or inconsistent data with the solution’s built-in data quality tools. Seamlessly integrates with existing third-party systems.

    • Advanced analytics with embedded AI and machine learning

  • Provides a broad set of modern statistical, machine learning, deep learning and text analytics algorithms from within a single environment
  • Enables you to improve fraud models by testing different approaches in a single run, and comparing results of multiple supervised learning algorithms with standardized tests.
  • Provides an array of analytical capabilities, including clustering, different types of regression, random forests, gradient boosting models, support vector machines, natural language processing, topic detection and more.
  • Continuously updates and improves models based on prior output results.

    • Inventory Optimization Management

  • Manage inventory to meet customer requirements while reducing costs.
    • Data management
  • Data from disparate sources of product distribution can be aggregated while eliminating inconsistencies and redundancy.
  • All factors that affect demand can be considered, which creates more accurate forecasts.
  • Data from can be pulled from ERP and legacy systems.
  • Transform, standardize and cleanse your data.
    • Inventory optimization
  • A buyers’ workspace with a suggested plan of orders for a given facility in a single, dual or multiechelon network.
  • Replenishment plans for low-stock items.
  • Cost by source for each order.
  • Cost summaries for each optimized plan.
  • Demand-driven planning and optimization suite integration

    • IT Resource Management

  • Optimize IT resource capacity and performance across the enterprise.
  • Interactive data integration environment
  • A visual design tool provides an easy-to-use GUI for enhanced data integration capabilities and administrative tasks prior to creating reports.
  • Wizards are specifically designed for building IT data marts, adapters, aggregations, staged tables and columns, and ranking IT data.
  • Enterprise access and interpretation of IT performance data
  • Adapters deliver a complete data model for each IT resource data source and convert raw IT resource data to information for analysis and reporting.
  • Device-independent measurement units and standardized variable-naming conventions for IT resource measurements enable complex IT metrics to be communicated and consumed by both IT and business users.
  • SES IT Resource Management adapters conform to standards for aggregations (simple and summarized), ranks and filters for IT measurements in the context of aggregations and report groups.

    • Request for Services

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