Data Governance

Data Governance

Rediscover 'information' as an important corporate resource

In order to guarantee and secure the data quality of company data, the introduction of a company-wide data governance is necessary. Comprehensive and tailored data governance helps companies to keep their organizational processes under control.

Definition: Data Governance

The goal of data governance is to manage data throughout its entire lifecycle and to guarantee high data quality. Data Governance uses guidelines to determine which standards are applied in the organization and which areas of responsibility should handle the tasks involved in achieving high data quality.

The right product for your data governance processes:

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Ensure the highest data quality in your systems in the long term thanks to data governance. In order to use your company data efficiently to achieve your strategic goals, it is necessary to create responsibilities, processes, standards and KPIs. In particular, the compliant implementation of processes requires technical support provided by a software solution.

 

Dimensions of Data Governance

 

 

Data governance contains three basic design elements:

  1. The designation of necessary tasks within the data quality management (DQM)
  2. The identification of roles and the definition of the responsibilities of each role
  3. The company-wide implementation of processes for the fulfilment of data quality management tasks

The designation of necessary tasks within the data quality management

For the success of a data quality management it is necessary to create clear tasks as well as to define goals to measure the success. First, it must be clearly described which data, systems, applications or business processes are to be included. Prioritization on the most important and business-relevant data is necessary here. Tasks to be defined in data quality management can be, for example, the development of a data quality strategy or the definition of data maintenance processes.

Agreeing on objectives is necessary to measure the success of cleansing and monitoring and to demonstrate the direct benefits for the company.

Identifying roles and defining the responsibilities of the individual roles

A data governance strategy is characterized by different roles. These are positions held by employees to perform specifically defined tasks. The definition of roles and responsibilities is your guarantee for establishing helpful processes and anchoring data governance in active day-to-day business.

Typical roles in data governance processes:

  • Data Stakeholder – responsible for problem solving
  • Data Governance Officers (DGO) – specify data quality standards
  • Data Stewards – supervise and implement data quality standards

 

What is data governance?

 

To ensure the quality of your data throughout its entire life cycle, the interaction of strategic and operational implementation in all areas of the business as well as data governance and data stewardship is necessary. Data governance as a framework for your data quality management must be anchored in the strategic corporate objectives. Data stewards ensure the operational implementation.

The company-wide implementation of processes for the fulfilment of data quality management tasks 

Company-wide data governance processes define the responsibilities for the identified data quality management tasks. Clearly defined responsibilities allow the data quality management to be successfully advanced.

DataRocket as data management software becomes the central data quality tool for all existing data. You benefit from using the company-wide resource ‘information’ in a targeted manner in your value-added processes.

Act instead of react

DataRocket is your software for proactive data governance

High-quality data requires a perfect organization, which can be represented by a holistic and proactive data governance. DataRocket is the appropriate software for achieving this service. Ensure sustainable high data quality directly when you create data and cleanse your master data assets in a structured manner using workflows.DataRocket Logo Weiß

Ihre Vorteile bei Data Governance mit DataRocket: Kundendefiniert

CUSTOMER-DEFINED

DataRocket offers a customized configuration according to your data governance strategy. Quality checks and authorizations are created individually according to your business rules and processes.

Ihre Vorteile bei Data Governance mit DataRocket: Real-time

REAL-TIME

Data Governance Processes as a micro-service enables you to continuously check your data quality in real time. With DataRocket, created and validated data is also transferred efficiently and in real-time to the target systems.

Ihre Vorteile bei Data Governance mit DataRocket: Universal

UNIVERSAL

DataRocket provides a central hub for implementing your data governance processes. Highest interface compatibility is guaranteed by the read and write connection of SAP and non-SAP systems.

 

How DataRocket supports your data governance processes

Data Governance Processes

DataRocket offers four workflows for implementing proactive data governance.

When creating data as well as for all changes in the data set, an automated check is performed based on predefined data quality rules regarding correctness and completeness. This effectively prevents data errors from the outset. DataRocket offers an additional data quality function for data consolidation. Data records that relate to the same business object but contain different information are merged into one comprehensive, meaningful data record – the golden record. The workflows for data quality management are supplemented by a clear user role concept that supports the responsible and sustainable organization, control and optimization of data and information in the organization.

Creation of master data in DataRocket
 
- Proactive approach: Avoidance of data errors during master data creation
- Quality control of data over its entire life cycle
- Automated verification of data based on pre-defined data quality rules

Creation of master data in DataRocket
 
- Proactive approach: Avoidance of data errors during master data creation
- Quality control of data over its entire life cycle
- Automated verification of data based on pre-defined data quality rules

Change of master data in DataRocket
 

- Quality control of data over its entire life cycle
- Automated verification of data based on pre-defined data quality rules
- Changes of data in batch (correction of several data records in one workflow)
- To-Do list for correction in the clearly arranged process cockpit for each user

Change of master data in DataRocket
 

- Quality control of data over its entire life cycle
- Automated verification of data based on pre-defined data quality rules
- Changes of data in batch (correction of several data records in one workflow)
- To-Do list for correction in the clearly arranged process cockpit for each user

Cleansing of master data in DataRocket
 

- Use of quality rules for checking corrected data
- Display of errors in a traffic light system
- Tool tips for the correction of data records for the data stewards
- Freely definable workflows (e.g. the principle of dual control)

Cleansing of master data in DataRocket
 

- Use of quality rules for checking corrected data
- Display of errors in a traffic light system
- Tool tips for the correction of data records for the data stewards
- Freely definable workflows (e.g. the principle of dual control)
- Assignment of rights at attribute level

Consolidation of master data in DataRocket
 

- Creation of golden records (harmonization of data sets)
- Duplicate detection with blur logic
- Score-based automated consolidation of data records
- Manual consolidation of data records in a web-based workflow

Consolidation of master data in DataRocket
 

- Creation of golden records (harmonization of data sets)
- Duplicate detection with blur logic
- Score-based automated consolidation of data records
- Manual consolidation of data records in a web-based workflow
- Definition of consolidation rules
- Transfer of Golden Records to SAP and any non-SAP systems

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