App Builder

Unified Data Management

Introduction

End-to-end data management is an essential requirement for effective automation, traceability of operations, risk management, decision support …

To do so, 2OS offers very advanced and fully integrated functionalities allowing the total control of data from their integration, quality control to their versioning and their governance.

Features

  • Connectors : One of the advantages of an ETL solution is the possibility to interact with files, databases and different types of applications (ERP, CRM, …) via specific dedicated components. As more and more companies adopt a cloud strategy, it is important that an ETL is hybrid, allowing an interface of files or databases in the company (on premise), as well as cloud application.
  • Transformations : An ETL must provide a powerful engine to manage complex transformations. These transformations, like connections, are parameterised via dedicated components. In general, it is possible to easily perform any kind of transformation via dedicated components for each use. It is also possible to have several inputs and outputs in the same data flow.
  • Reject Management : The components generally have error outputs that can be used to send the rejects to the desired target for evaluation (e.g. database, flat file).
    It is possible to schedule notifications to businesses by email via a dedicated component. Our new self-service platforms combine ETL operations and analysis screens, bringing increasingly more functionalities within the same platform.
  • Data profiling : It is becoming increasingly important to analyse data quickly and therefore to have the necessary tools available.
  • Data lineage : Data Lineage traces the origin and all transformations of a target data item. This functionality is increasing in demand as it is required in some areas in order to comply with legislation. This feature eliminates the need to manually retrace steps to understand final processing results.

Benefits

  • Integrated metadata management
  • Data centralization and multi-source management
  • Substantial cost reduction
  • Ensure optimal data quality
  • Assist in decision making
  • Ensure optimal data governance
  • Ensure optimal analytical processes
  • Improve process efficiency

Key points

  • Connectors
  • Transformations
  • Reject management
  • Data profiling