That’s why FSFP specializes in helping organizations craft and implement strategies to ensure data remains trustworthy and actionable across the enterprise. Our robust data quality approach is a multi-disciplinary exercise that includes aspects of data governance, master data management and metadata management capabilities. To truly translate data value into business value, we also embed progress and impact measurement to align your data quality efforts to key business objectives.
No matter how well-designed or well-implemented your enterprise information management strategy, poor data quality can lead to poor overall results. Unreliable data erodes trust at all levels, creating situations where even the most data-driven stakeholders revert to making critical decisions based on gut instinct — in absence of trusted, high-quality data.
- Develop a pragmatic data quality strategy
- Create a business case and conduct a cost-benefit analysis of improving data quality
- Align data quality with the data governance organization
- Define roles and responsibilities for managing data quality
- Establish progress and impact metrics to understand what is meaningful to the business and measure data quality momentum in a meaningful way across people, process, technology and data
- Define and implement data quality policies, processes and standards across the data lifecycle
- Select a technology vendor