Achievable results
Data governance services result in the transformation of an organization's data practices resulting in:
- Increasing the availability of data for use in analytics and operational business processes, increasing trust in data.
- Increasing the speed of making changes to data structure and composition as a result of changing business requirements.
- Reducing the likelihood of losses and errors in business processes, analytics and decision support due to poor quality data.
- Improving the quality of management in the company as a result of the transition to decision-making and planning based on quality and reliable data. Development of predictive analytics.
- Opening up opportunities for digital transformation of the enterprise, digitalization of business processes, and creation of digital products.
What we do
Data governance services include:
- Survey of the IT landscape to describe the processes of receiving, updating and using data. Description of data usage modes in terms of structural units and responsible persons, business processes in which data are updated and used. Description of data flows. Inventory of existing data lakes, data showcases, analytical repositories.
- Description of data structure in sources and in business process automation systems (technological processes).
- Gathering requirements for data composition, structure, and completeness. Analyze and describe business requirements for analytics, including requirements for availability of historical data, use of machine learning tools to implement operational and predictive analytics.
- Analyzing the quality of existing data, identifying typical problems. The completeness, reliability, relevance of data, level of confidence in them, suitability of data for making management decisions are considered.
- Compilation of a reference data structure - a corporate reference model.
- Establishing correspondence (mapping) between elements of existing data structures and the structure of the reference model. Formalization of rules of data transformation from the initial (existing) structure to the reference structure.
- Formalization of data consolidation rules, rules for conflict resolution in the presence of conflicting information in different sources.
- Creation of a Modeling Agreement (Modeling Methodology) describing the rules of corporate data governance, including requirements for entity naming, application of classification and coding systems, and use of industry-specific data models. Designing organizational data governance processes.
- Creating of format-logical control rules, data integrity control rules, data enrichment rules. Representation of rules in machine-readable form suitable for direct execution in software designed for data governance.
- Creating a system of data quality control metrics.
- Generating requirements for the software through which data governance takes place.
- Implementation of master and reference data governance practices (master data). MDM system implementation (or consulting during implementation), including design of integration interactions, development of requirements for the systems to be integrated, implementation control, commissioning and pilot operation, and documentation.
- Formation of a Data Governance Development Strategy for the organization, including an employee competency development plan, a plan for transitioning to the creation and use of digital products, and to data-driven governance practices.
- Training of analysts, customer's data stewards.
- Creating a corporate glossary.
- Creating data catalogs.
- Design and implementation of data discovery and search tools, data showcases.
