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Our Approach

 

In order to realise the business benefits of best practice data management and reliable data, a structured approach is required, using not only enabling technology, but also across people and processes.

InfoBluePrint delivers success via its proven Data Management methodologies, founded within a consistent Data Management Framework, to ensure well controlled programmes and maximum benefits to your business in the shortest possible time.

Data Quality Management needs to be addressed with a combination of both preventive and corrective action. Corrective actions target the improvement of existing data whilst preventive action focuses primarily on the design and implementation of new and extended processes, procedures, systems, roles and responsibilities to ensure that future data is well managed.

InfoBluePrint has developed a generic approach to Data Quality and Information Management, which is flexible to allow for customisation where appropriate, and ensures that particular business needs can be addressed within a repeatable and predictable solution framework.

 

DQI blue arrowInfoBluePrint Data Quality Improvement (DQI)

IRM green arrowInformation Resource Management (IRM) Framework

 

 

Corrective Solutions

 

(Data Quality Improvement) solutions are applied from bottom-up to improve the quality of existing data and consist of projects to:

  • Assess the data formally and objectively
  • Plan an overall approach for the implementation of improvements
  • Determine the rules, metrics and supporting processes to be used to manage improvements
  • Correct both inaccurate as well as incomplete data - implementing cleansing and augmentation solutions respectively
  • Implement on-going maintenance and monitoring of data quality targets.

Correction projects achieve the best results when scoped to focus on specific areas of data one at a time, achieving results in a relatively short time frame and providing immediate business relief from the pains of existing data problems.

 

Data Quality Improvement infographic

 

InfoBluePrint Data Quality Improvement (DQI) Components

 

 

 

Preventive Solutions

 

(Information Resource Management) solutions address the root causes of sub-optimal data quality, are applied top-down and consist of projects to:

  • Assess the existing Information Management Processes and Systems
  • Define strategies and governance requirements for the effective management of data across the organisation
  • Define strategies for the effective management of data across the organisation
  • Design and implement the necessary Data Governance functions and Architecture controls
  • Design and implement new/improved processes to ensure the quality of data, effective management of information and eradication of new problems.
  • Implement the required roles, responsibilities, systems, technology, and supporting education to enable effective and efficient organisational management of the information asset
  • Ensure regular review of the practices to enable continuous improvement and alignment to changing business needs and priorities.

Preventive projects tend to focus at a strategic level, addressing the root cause of data quality issues, via formal Management and Governance programmes.

 

Information Resource Management infographic

 

InfoBluePrint Information Resource Management (IRM) Components

 

 

 

Applying Both Solutions

 

Applying both solutions will ensure the implementation of sustainable Data Quality and Information Management. Each organisation should determine the best combination of top-down, bottom-up initiatives and projects for their particular circumstances. In the case where the focus is on a top-down approach, the defined Management Strategy should provide the framework and guidance for determining the timing and inter-dependencies of the various data quality/information management projects to be implemented.When implementing a bottom-up approach, correction projects are usually planned based on business pressures and priorities. Once a number of improvement projects have been initiated, Governance needs will come to the fore in order to manage issues such as business rules and term definitions, across the different projects and areas of the organisation. An overall Data Management & Governance framework should then be defined, which in turn will provide future guidance and direction regarding the timing and inter-dependencies of projects.

Data Migration and Master Data Management Projects require the application of both solutions. Data Quality Improvement needs to be applied to address the data issues in the existing source systems. The Information Resource Management solution components of Strategy and Governance need to be implemented to define and facilitate agreement to the data migration approach and rules that will be applied to the data in both the source and target systems. The Data Governance function should also ensure the necessary validation is developed in the target and master data management systems to prevent the creation of repeat data quality issues in the new environments. The application of the corrective and preventative methodologies within these solutions will ensure the improved and sustained quality of the organisation’s information asset.

The InfoBluePrint Data Quality and Information Management Framework thus provides for both short and long-term benefits by improving the quality of existing data, and quality managing any new (or modified) data at or near its point of entry into a system. By following these steps, InfoBluePrint ensures that repeatable and sustainable processes are put into place, as opposed to never ending costly and inconsistent “data cleansing” exercises.

 

The InfoBluePrint approach to Data Quality and Information Management, depicted graphically above, provides for the implementation of both a “bottom-up” corrective approach as well as a “top-down” preventive one.

Putting a solution of this nature into operation is usually a complex undertaking, and so we have created an approach that breaks the problem down into meaningful phases, each of which can be managed as distinct projects from which key deliverables for the full solution are produced.