See my top 3 Data Quality lessons on Data Migration Pro
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Having just landed in the NewWorld of Data Migration Pro (DMP), I thought I'd start my blogging contributions in the data quality blog on my top three lessons/tips on data quality management.
As I suggested to DMP readers, perhaps 'My top 3' could be a topic you might have a view on?
My top 3 lessons/tips on Data Quality
Many talk about the dimensions of data quality - the things you can/should measure, monitor/manage. During my QDB Analyze and GQL days I came up with ACCTR – Accurate, Complete, Consistent, Timely and Relevant. I say had, because during my 18 months at T-Mobile as Data Quality Manager, the biggest issue we had to overcome was the business perception that data quality was poor, therefore not trustworthy. And to cut a long story short, we adopted ACT TRUE - Accurate, Complete, Timely, Trusted, Relevant, Understood and Engineered. Does anyone know the origins of ACT TRUE?
Personally the three dimensions that really 'did it' / 'do it' for me are Trusted, Understood and Engineered.
In reverse order, Engineered is about 'engineering' in data quality controls in to your end-to-end data lifecycle. It's about making data quality management an integral part of you architecture design principles. It's about culture change and can not be solved by buying a tool. As far as I am aware, no one has really made much progress on this a) vision, b) dream or c) fantasy?
Next up is Understood. From what I can tell, most, if not all organisations are very very very poor at managing their organisational memory around data/information, the meta data, the business rules, the business definitions, the business process - creation, update, delete, etc. Having accurate, complete, relevant meta data, reference data, master data - call it what you will, is one hell of an obstacle that many have thought about, and most have failed at.
Last, up is Trust - more specifically, Business Trust. Without business trust, no amount of data profile reports will 'make' the business use your data for decision making. Building business trust is a complicated one to crack and one worthy of a chapter of its own
Does the scope of your data quality strategy and plan comprehensively cover these?
