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.
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.
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.
You know by now that a data quality strategy is vital for your data migration project and by means of a quick recap, the reason why data migration projects overrun so often is partly due to:
I was recently asked:
“What would you expect to see included in a data quality issues log?”.
This is a great question because although some modern data quality tools will allow you to track data quality issues, a lot of data quality defects are first reported by everyday business users. We therefore need a method of tracking from both a business and technical perspective.
Following on from my recent Experian Data Quality webinar appearance, I wanted once again to respond to one of the many questions posted by attendees during the session.
This week I’m sharing some tips for one attendee who wanted to know how to improve their data collection approach across multiple departments of the organisation.
On a recent presentation I delivered as a guest of Experian Data Quality I was asked the following question:
“What is the business case for using Data Quality tools over a SQL based approach?”
This is one of those timeless questions I get asked frequently so I wanted to provide a series of steps to help you arrive at the right answer.
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