Our typical approach for establishing the capability of Data Quality management comprises the following four phases. Since building the new capability from grounds up sometimes is a radical organizational change, we frame our mission by the Kotter’s 8 Step Change Methodology.
Assessment and Profiling – any proposed change of an organization’s Data Quality system shall by linked to organization’s current circumstances, issues, and expectations. This entry phase delivers an overall view summarizing the current state in the following dimensions:
Organizational Assessment – reviewing organizational aspects of Data Quality (e.g. some data areas with unassigned responsibility or missing knowledge); see more in Data Governance.
Data Assessment – assessment to identify critical issues in selected business domains (e.g. Marketing, Sales, Accounting or Reporting), including the current strategies for correcting poor-quality data.
Compliance Assessment – evaluating how various compliance requirements (e.g. Basel), relevant for Data Governance and Data Quality Management, are considered in the current state.
Vision and Strategy – when the current situation is mapped, we deliver a vision of the future state, respecting the current circumstances, issues, and expectations, together with a transitional roadmap (since we believe in gradual rather than “big bang” changes):
Business Case – comprises an identification of the initial situation, the motivation for the change, and the proposed state, together with measurable objectives, required effort and costs, and a calculation of the project rentability.
Process System Blueprint – global models of a suitable process system for implementing Data Quality management in the organization (see Digitizing Operations/ Our Approach).
Roles and Organization Blueprint – building in the existing organizational roles or suggesting new roles to ensure data ownership, stewardship, and operational custodianship.
Applications and Tools Blueprint – designing a system of applications and tools supporting the Data Quality management processes; we cover several different technology vendors and thus can adapt a suitable solution for the organization.
Transitional Roadmap – planning a sequential transition towards the proposed blueprints/ business case, respecting organizational circumstances, priorities and related initiatives and projects.
Pilot Implementation – we believe our clients want to see material results shortly, so we dissuade them from long-term milestones and strongly suggest a gradual approach with values delivered in smaller, but persistent and sustainable steps.
Organization Implementation – picking and training appropriate people for a selected data area/ process and making their assignment operational and sustainable.
Process System Implementation – implementing just the most priority processes and in a just good-enough formal manners; usually the critical processes at the beginning is to handle existing Data Quality issues and measure the quality of data.
Applications and Tools Setup – setting up and configuring just the most basic applications and tools; e.g. a workflow system for the handling of Data Quality issues and a Data Quality profiling for the basic measurement.
Pilot Operation, Evaluation and Planning – piloting the initial system in a set of selected business domains and projects, evaluating its benefits and shortcomings, and collecting requirements for next steps.
Building on Pilot – any further evolution of the organization’s Data Quality system we build together with the client based on the experience from the Pilot Implementation and gradually increase the scope to other data areas/ business process and the maturity.
