Data Quality

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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.

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