Are you operating comprehensive data warehouses and require high quality and security of data? Do you need to maintain long-standing quality in metadata management and data architecture? Is it increasingly difficult for you to make changes in new projects? In that case you should consider the introduction of data governance which will ensure the management and efficient use of data. We will help you to set up this process and we also have experience with its implementation.
What a data governance programme will do for you?
- Increase confidence in data warehousing and analytical systems in general
- Save time and cost of analyses, reporting and new projects
- New possibilities of use, opening up of new business and marketing opportunities
- Better transparency, compliance with standards and regulatory requirements
- Higher customer satisfaction (fewer complaints)
What we can do for you?
We can propose a number of technical and organisational measures that will help you to:
- set up the necessary processes
- define key roles, their competencies and responsibilities
- implement specific technical processes for consolidation, unification of data, data cleansing, verification (e.g. inspection reports), and other processes
What is included in the data quality management process?
There are three key elements essential for the system operation:
- Organisational chart
- Definition of processes
In cooperation with the programme sponsor and other stakeholders the implementation consists of the following activities:
- Comprehensive analysis of the current state
- Data audit (profiling, domain analysis, etc.)
- Gathering of requirements for data quality, availability, speed of delivery, and granularity of detail
- Preparation of data governance methodology
- Identification of data owners
- Definition of roles – data stewards, custodians, auditors, quality managers, testers, their competencies and responsibilities
- Assigning roles to data sources (applications), domains (data entities, e.g. Campaign, Contract) – typically in larger organisations with matrix arrangement
- Setting, monitoring and management of SLAs (service level agreements)
- Preparation and compliance with data architecture and methodologies
- Implementation of processes
- In the field of metadata – use a tool for metadata management, metadata publication, and impact analyses
- In the field of data quality – data audit, automated testing, verification mechanisms, input and output checks, definition of business rules, rule-based corrections managed by metadata, self-generated code based on rules, consolidation, deduplication (unification) of data and use of specialised software tools
- Quality assurance (QA) processes for new projects – functional testing, unit testing, UAT, pilot operation, etc.