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Issues vs. solution

According to surveys more and more organizations of all sizes addresses

the following issues:

RDS product responds to these issues and

its deployment brings:

Inconsistency of data across systems
Centralized storage of static and reference data with process governance over these data and with definition of responsibilities and accesses
Poor quality or delayed reports (including reports for the parent organization or regulator)
Rapid identification and propagation of changes, including an automated data distribution to surrounding systems
Errors in transaction processing caused by invalid or inconsistent data
Assurance that all systems are using the same static and reference data, and that interconnected systems understood each other
Difficult tracing the causes of conflicts and inconsistencies in the data. The high cost of data synchronization between systems
Assurance that the systems do not get data that do not meet defined validations and that could later lead to conflicts
The high cost of maintenance of reference data and specifically codebooks
Flexible and powerful tool ready for further expansion in MDM area

Features in detail

Architecture - a single source of truth

Data are at one place in one format. The repository is the source of so-called "One truth" for all other systems.

Flexible data model

Structure of managed data is described using metadata, which allows the creation of any model in an entity.

The model allows the definition of hierarchies, relations of various kinds and complex structures.

Data and model changes over time, therefore RDS allows the model to change without programmer intervention.

Data check - validation

RDS monitors the overall quality of managed data including defined relationships and hierarchies.

RDS only provides valid data. All data have data owners who are responsible for them.

Everything is audited and can be traced.

Business rules enable advanced validation - syntactic checks are often not enough and it is necessary to evaluate complex rules and conditions.

Complete history of data and model

RDS works with the data and the model in the same way.

Each record has its validity.

RDS can reconstruct any moment in history.

Any changes in the model and rules can be tracked.

RDS validates the continuity of history.

Security and access control at all levels

Configurable user roles and groups.

Access rights are with granularity down to the level of individual records.

Access rights for user actions.

Detailed audit of all changes and actions.

End-to-end data lifecycle management

Integrated workflow framework provides an option to define the workflow for creating tasks and their delegation.

Workflow provides various possibilities for the approval and several automated tasks such as "publication", "validation" and others.

Events generated in RDS can be easily consumed.

For easy management of running tasks there is worklist available.

Easy to use

RDS has a user-friendly web interface for working with data with metadata.

Out-of-the-box integration

RDS generates events and sends them to defined recipients.

RDS implements SOA principles and is able to integrate online using Web services and messaging.

It is also possible integration using batch files.

Imports and exports

RDS supports import and export to and from various data formats (ie XLS, XML, CSV).

One type of export is Delta Export, which allows you to export just data changes since the last export.


How RDS works

Platform RDS


RDS can also be used to

Master Data Management

Get control of your Master Data like Customer or Product

Metadata Management

Use RDS for Business Vocabulary, Report Catalogue or Organizational structure

Data Quality

Increase data quality with RDS.


Selected references

RDS Product Deployment & Services

Project goal

To deliver centralization of the responsibility for codebook loading, versioning and providing.

Key Requirements

Codebook data loaded from Excel sheets (more than one excel sheet)

Passive data distribution – codebooks on demand – files (batch) and webservice interfaces

Business Goals and Impacts


Centralize the responsibility for distribution of codebooks to consuming systems.


All systems work with the same version of codebooks valid and relevant for given time.

RDS Product Deployment & Services

Project goal

Policy and Payment backoffice management for distribution channels.

Key Requirements

Metamodel of contract, form workflow and validation of contract data.

Pair payments with policies.

Centralized store for reference data.

Business Goals and Impacts


Provide centralized and easy way how to pair payments and policies and provide the data to BE systems.


Great impact on contracts data quality. Automation of several processes.

RDS Product Deployment & Services

Project Goal

Complete Master Data Management for codebooks.

Key Requirements

Unique codebook structure – fix part and variable part.

Strong integration demands – used for codebook value translation on the fly.

SPart of the DWH infrastructure.

Business Goals and Impacts


Decrease costs and manual work for synchronization of static data between systems.


No need for distributed codebook synchronization. Responsibility moved to business users (data owners).

RDS Product Deployment & Services

Project Goal

To deliver centralization of the responsibility for static data quality and distribution.

Key Requirements

Systems provide their static data in one moment – time trigger.

RDS loads the data and makes the validation.

RDS compares the data with previous version and creates the difference (delta).

RDS sends the difference through the Delta Publisher component to the integration platform.

Business Goals and Impacts


Join and validate data from external systems and distribute the clean and valid data to internal systems.


Only valid data is present in the internal systems, invalid data is reported back for repair.


Project Goal

Deliver of analysis and design of Master Data.

Key Requirements

Strong analytical and business-oriented team that will be able to analyze objects, to evaluate their inclusion in the Master Data Management, and propose a comprehensive description of the data entity.

Business Goals and Impacts


Mapping of company's Master Data.


Data unification and centralization, enabling next step in the Data Governance.