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DATA QUALITY FOR EVERYONE - Part 3

Christopher Wagner • January 10, 2022

Everyone Plays A Role In Data Quality

Data Quality is not something that 'just happens.' Data Quality takes a great deal of work from a wide variety of different roles. Everyone who interacts with data should play a role in defining what does data quality mean to them. 


Data Engineering - Data Engineering is the first line of defense for ensuring high-quality data. The checks Data Engineering will build will enable a high degree of foundational data quality but can be the most susceptible to being technically correct but fail business needs. Data Engineering needs to make sure the process allows for both technical and business validation checks. Rollback of data entities needs to be built and understood in the recovery process. 

 

Analytics Engineering- Analytics Engineering is where the data meets the business. Analytics Engineers need to keep an eye on expectations between data engineering and the company. 

 

Analyst - As SMEs with a deep understanding of the business. Analysts are excellent at knowing when data is not suitable for a given subject. This knowledge needs to be distilled and transformed into quality tests.


Product/ Project Manager - Like Analysts, Product/ Project Managers have a broad understanding of the business and set the baseline standards for data quality. Product Managers will ensure that each feature includes the necessary Quality Tests to ensure that the end result aligns with the expectations of people within the business. The Product/ Project Manager is a critical role that is often overlooked and can cause many data quality issues. 

 

Business Leadership - Like Analysts, Business Leadership has a broad understanding of the business. Business Leadership is an excellent source for comprehensive data quality requirements that individual SMEs may miss.



DATA QUALITY BLOG SERIES

Each day the Data Quality Blog post will be released at 8:45 AM each day.


DATA QUALITY - Part 1 January 6th

DATA QUALITY CONCEPTS - Part 2 January 7th

DATA QUALITY FOR EVERYONE - Part 3 January 10th

DATA QUALITY FRAMEWORK - Part 4 January 11th

DATA QUALITY DEVELOPMENT - Part 5 January 12th

QUALITY DATA - Part 6 January 13th



CHRIS WAGNER, MBA MVP

Analytics Architect, Mentor, Leader, and Visionary

Chris has been working in the Data and Analytics space for nearly 20 years. Chris has dedicated his professional career to making data and information accessible to the masses. A significant component in making data available is continually learning new things and teaching others from these experiences. To help people keep up with this ever-changing landscape, Chris frequently posts on LinkedIn and to this blog.

Become a Data Engineer

By Christopher Wagner • September 3, 2024
Your guide to becoming a Data Engineer.

Microsoft Fabric vs Databricks

By Christopher Wagner • August 19, 2024
Compare Microsoft Fabric and Databricks, two leading data platforms. Highlights their features, strengths, and unique offerings across various domains like data engineering, data analytics, data science, DevOps, security, integration with other tools, cost management, and governance. Microsoft Fabric is noted for its low-code/no-code solutions and seamless integration with Microsoft tools, making it accessible for users with varying technical skills. Databricks is praised for its high-performance capabilities in big data processing and collaborative analytics, offering flexibility and control for experienced data teams.

Data Wizardry and Epic Adventures: A Day in the Life of a Microsoft Fabric Engineer

By Christopher Wagner • November 15, 2023
In a dynamic data engineering scenario, Sam, a skilled professional, adeptly navigates urgent requests using Microsoft Fabric. Collaborating with Data Steward Lisa and leveraging OneLake, Sam streamlines data processes, creating a powerful collaboration between engineering and stewardship. With precision in Azure Data Factory and collaboration with a Data Scientist, Sam crafts a robust schema, leading to a visually appealing Power BI report.

Power BI usage scenarios: Lakehouse to Power BI

By Christopher Wagner • April 28, 2023
NOTE: This is the first draft of this document that was assembled yesterday as a solo effort. If you would like to contribute or have any suggestions, check out my first public GIT repository - KratosDataGod/LakehouseToPowerBI: Architectural design for incorporating a Data Lakehouse architecture with an Enterprise Power BI Deployment (github.com) This article is NOT published, reviewed, or approved by ANYONE at Microsoft. This content is my own and is what I recommend for architecture and build patterns.
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