Forrester Research: Build Trusted Data with Data Quality
Key takeaways of the report include: How managing data quality brings IT and the business closer together, Different data quality definitions, and advantages of transparency in data quality.
"Build Trusted Data with Data Quality," written by analyst Michele Goetz compliments of Lavastorm Analytics. See how organizations are keeping tabs on data conditions to build confidence and trust in the data.
Key takeaways of the report:
- Managing Data Quality Brings IT and the Business Closer Together - Business intelligence and business process projects expose data quality and master data challenges. They often provide the venue for the lines of business and IT to put data in context of business objectives and outcomes. These conversations direct resources to address data quality issues and provide a business case for data technology budgets.
- Data Quality is Defined Differently by the Business and IT - Business stakeholders define data quality by access, relevancy, and timeliness. IT defines data quality by the physical nature of data to pass or fail data processing rules. Each definition is correct, but to ensure data is performing to business expectations, you need a tangible link when measuring and reporting on data quality conditions
- Transparency in Data Quality expands Data Use and Changes Behavior - Lack of trust in data affects an organization's adoption of a data-driven culture. Consistency is often the catalyst for trust in data sources and intelligence. To increase data use and adoption of validated sources, collaborate to establish common definitions and transformations, syndicate definitions and policies, then measure data to thresholds
Download this Forrester Research paper today compliments of Lavastorm Analytics.
For more information, please visit: www.lavastorm.com