Dylan Jones is the editor and founder of Data Quality Pro and Data Migration Pro, the leading community resources for their respective professions. He has over 20 years' experience of delivering complex data quality and data-driven initiatives. Dylan is a keynote speaker, author and regular publisher of expert insights on data quality related topics.
During a recent Data Migration Roundtable, a delegate confided with me that they were feeling overwhelmed at the thought of having to coordinate and oversee a large data migration project.
Coping with all the moving parts of a complex data migration is a common challenge, so I wanted to share some practical ways that I’ve approached this in the past.
During a recent interview with Rebecca Hennessey, I also explored some of the key findings and recommendations from the research.
In this post, I want to expand on those earlier insights by introducing the ‘Data Migration Butterfly Effect’.
This phenomenon occurs when a few simple oversights, typically at the beginning of a data migration project, can cause a chain reaction leading to widespread chaos and delays as the project develops!
In this second article in the series on tips for a data quality business case, I'm going to explain the importance of creating a ‘Lean Pilot and Roadshow’ for getting senior stakeholder buy-in.
In this first of a two-part series, I share tips for increasing the success of your data quality business case.
This week I’m looking at personal buyer drivers, and how they can influence the decision to approve or reject your data quality business case.
You can finally ignore the assumptions, anecdotes and accusations about your data; data migration will expose the whole truth (and nothing but the truth!) about your data.
How good is our data? This is the question that people are trying to answer when they undertake a data quality assessment. Dylan Jones explains the best way to answer it.
From experience, I find that most companies struggle with making their data quality assessment results compelling because they take a data-centric viewpoint. They show stats and metrics that, whilst valuable to the data community, can be dull as dishwater to the business leader who needs to make tough decisions on where to focus limited staff and financial resources.
In this two-part series I want to share some practical, actionable techniques to help you create a more business focused data quality assessment.
One of the mistakes I made early in my career was to create exhaustive data quality assessments that failed to motivate and engage the business community. It was only when I tweaked my approach to tools, strategy and storytelling did the business finally sit up and in most cases, take action.
In this blog post I want to share some background to the data quality assessment process so that you can understand the fundamentals.
In this post, Dylan Jones (editor of Data Quality Pro/Data Migration Pro), shares more insights from the recent Data Migration Roundtable workshop where he was our guest speaker. You can read the previous article here: The continued need for impact assessment in data migration.
At the Experian Data Migration Roundtable, we saw two very clear themes emerge. Firstly, the need for an impact assessment before starting a data migration project and secondly, on using data migration as a design for data protection. So in Part 1 I’d like to kick off with a detailed look into our discussions around impact assessment.
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