Data has become an asset, and its quality is at the heart of business performance.
Data is tied to two closely interrelated issues:
- Operational optimization: freeing up internal stakeholders’ time and ensuring the reliability of what is exposed on digital fronts.
- Mastering information: enabling data experts to leverage data, particularly within analyses and business indicators.
1. There is a foundational set of common requirements that define the main quality criteria:
- Completeness.
- Accuracy and consistency of data.
- Adherence to values and formats.
- Uniqueness.
- And also validity over time: the data must be the most recent and always accurate.
2. Data quality is part of a broader framework: data governance.
Governance is organized around the following best practices:
- Profiling data to understand its structure and quality.
- Organizing and monitoring/reporting the data life cycle.
- Data Owners to prioritize the needs of business teams.
- Product Owners to manage application upgrades.
- A team of Data Stewards to handle day-to-day data management.
3. Data preparation is the key to information quality.
👉🏼 Gather, combine, structure, and organize data.
- Cataloguing and attention to metadata to facilitate search and retrieval.
- Transformation workflows (completeness, correction, validation).
- A storage model that speeds up data access.
- Enrichment through cross-referencing with external databases to correct or complete certain values.
- Algorithmic and/or ML processing to add indicators that qualify the information and enhance its usability.
What to remember
To combine operational efficiency with data management, one solution is becoming increasingly essential: data centralization within platforms.
- For referential data: MDM (Master Data Management).
- For all data: enterprise data platforms, increasingly adopting the ‘Lakehouse’ model.
The key to success?
👉🏼 Combining technical and business expertise by leveraging the knowledge of Data Engineers and Data Analysts.