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DATA QUALITY - Part 1

Christopher Wagner • January 6, 2022

Data Quality Will Make Or Break Your Analytics

Now more than ever, companies are striving to make data-driven decisions. With the vast amount of data at our fingertips, one can easily mistake the quantity (or speed) of the data available for the value derived from the data. 

 

'I need sales reports' quickly turned into "I have far too many sales reports' when executives realized that the 1k reports they now have access to were producing skewed actions, different results, and confusion across the organization. 

 

Just because we can turn analytics around in a day does not mean that we should. 

 

The value of Data platforms is measured not in size, speed, or the tools used to operate them but in the value generated by the actions they enable broadly across an organization. 

 

These actions range from low ROI activities (debating the quality, accuracy, and timeliness of the data) to high ROI activities (generating sales, identifying opportunities, managing risks, to data as a service). They include harmful ROI activities (data that lead to the wrong activities - think mortgage crisis). 


This blog series will guide you on a wide range of data quality topics, roles, frameworks, development, and much more.



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.
By Christopher Wagner September 3, 2024
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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.
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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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