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Piotr Czarnas

Founder @ DQOps open-source Data Quality platform | Detect any data quality issue and watch for new issues with Data Observability

Piotr Czarnas is an expert in the field of data quality and data science, known for his work in developing DQOps, an open-source data observability software aimed at measuring data quality through key performance indicators.

With a Master's degree in Computer Science - Software Engineering from the University of Wroclaw, Piotr has a strong educational background in the field.

His professional journey includes roles such as Founder at DQOps, Data Quality Solution Architect at Procter & Gamble, Board Advisor at Lyftrondata, CEO / Founder at Lyftrondata, IT Expert, Technical Team Leader, Unit Leader .NET, and Application Architect at Roche Polska.

Piotr Czarnas's experience spans across various domains, showcasing his expertise in data quality management and technical leadership.

Through DQOps, Piotr enables users to conduct data quality checks, analyze large-scale partitioned tables, and monitor data quality dashboards, emphasizing accurate and reliable data for effective decision-making in data science projects.

Highlights

Aug 4 · linkedin

Business and technical data steward roles should be separated.

I am receiving many comments on my posts that mention the responsibilities of data stewards. These comments suggest different responsibilities for data stewards.

When I look at who is commenting, I can quickly notice that the vision of a data steward role strictly depends on the person's position.

One group expects that a data steward is a business function that is not directly responsible for getting the hands dirty with data quality issues. Another group will see the role of data stewards as data quality engineers who are directly defining and managing data quality checks.

Let's separate it. There are two types of data stewards:

👉 Business Data Stewards prioritize the business impact and usability of data.

👉 Technical Data Stewards focus on the technical implementation and maintenance of data quality.

I collected the typical activities of these two roles for your review.

If you want to learn more about data quality, please visit my website and download my free eBook "A step-by-step guide to improve data quality".

#dataquality #dataengineering #datagovernance

Aug 3 · linkedin

Waterfall data quality projects will fail.

Data quality projects are not meant to be run as waterfall projects. Data platforms are too dependent on the data quality of upstream data sources. The data in the upstream data sources depends on the business processes of applications that collect the data.

No matter how smart the data analysts or the data platform owner are, nobody in a bigger organization understands all business processes and how they are mapped to data models.

Data quality projects are meant to be run in an agile way. Always prioritize data assets and pick the tables of the highest importance for the next sprint.

If you want to learn more about running iterative data quality projects, please visit my website and download my free eBook "A step-by-step guide to improve data quality".

#dataquality #dataengineering #datagovernance

Piotr Czarnas
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Location

Warsaw, Mazowieckie, Poland