Here's how you can ensure data quality and integrity as an executive in Data Governance.

Quality is the core of any business and essential enough that some of the data governance initiatives I have seen were started due to poor data quality problems. Oftentimes, IT is tasked to handle data quality, but providing just a metric report to the business doesn't help build a business case; I find that business stakeholders who own the data need an active role in terms of defining data quality standards and a collaborative framework to engage in remediation processes. If not done cross-functionality, indeed, it is going to be a high toll.

We need to clearly articulate Data Strategy with challenges and Business Drivers documented along with Guiding Principles to address those. There are clearly defined Data Sources and System of Records for established Data Domains. For each data domain there should be designated Data Domain owner, who along with Data Stewards is responsible for Data Management of the data domain and it includes definition and management of Business Data Quality Rules, their Severity Levels, workflows and Thresholds. So any data consumed downstream is curated and certified. When this data is sent to Enterprise Data Layer additional validation can be put in place for cross domain validation rules. It ensures vetted data goes downstream. Data Governance is key.

Data Governance initiatives can help in emphasizing the importance of data quality, but data quality metrics only starts to gets traction with accountability and senior stakeholders buy-in. Senior leaders can make it clear that data quality is one of the pillars of data strategy, and it will be govern via data controls, KPI's will be present in data governance forum and action has to be taken to improve the scorecard. Since improving data quality requires time and effort we need to ensure that there is a budget allocation as a part of data quality project.

Ensuring data quality to start with, and "at source" is much better than fixing it afterwards. e.g a drop-down list ensures data quality compared to a text box (but I appreciate that there is a time and place for each option).

Data Quality is a practice. Executives need to govern the practice (not the tool). Governance on data quality means having control (enforcing) and being able to monitor that this practice is followed. The storytelling proposed is based (imo) on old fashion data governance. A modern approach would include automation on data assets such as data contracts or any declarative means to match expected quality (SLAs) against the returned one (SLIs). A declarative approach enables automated regulation, control, and monitoring, preventing data producers from publishing data assets without data quality. Such a methodology is not dependent from the data quality tool but it actually focuses on processes and their automation.

Data governance is one of the worst areas for buzz words and terminology that many people don't understand (but are afraid of admitting it!) or bores them silly. Use everyday language and examples. One example was when my pet dog was sent a letter inviting him to apply for a credit card! Obviously something had gone wrong with data governance and quality! This example made a point, but also made everyone smile!