Perfect is the Enemy of Good when Building Big Data Confidence

Social-Puzzle1-300x219Confidence in big data is highly variable.  Some data sources have inherent uncertainty.  So why shouldn’t you spend as much time as needed to make big data perfect?  Time.  You simply don’t have enough time to sort out every data irregularity, every ambiguity, every incomplete attribute.  And for many big data use cases, you don’t need to.  That’s why perfect is the enemy of good.  In the era of big data, governance has evolved to first diagnose the usage, then prescribe the appropriate amount of governance.  So the objective is not to make it perfect for every possible usage up front, it’s to make it good enough for the use case at hand.

Tony Baer of Ovum explains this in more detail in his blog post here –

For more information on building big data confidence check out IBM Big Data Hub


About David Corrigan

I’ve spent my entire career helping clients utilize emerging technology to solve their customer data problems. I've always enjoyed solving abstract problems. I've worked with hundreds of companies to utilize new technology, plan and drive to a roadmap, and evangelize and drive momentum for their information projects. During the day, I work on product strategy and marketing for @InfoTrellis, and I'm busy trying to disrupt the customer data and analytics market so that organizations can finally understand every single one of their customers. After hours, I like to take photographs, read, write, practice yoga, or watch soccer - Manchester United and Toronto FC are my teams of choice. Follow me on Twitter @DCorrigan or on LinkedIn at

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