It's good to be reminded every so often that bad data is really, really costly to businesses. A recent Tech Republic post says: “It is estimated that poor data quality costs US companies $600 billion per year. It isn't just the potential for serious mistakes that poor data engenders, but also the painstaking amount of time and human effort that it takes to fix this data.”
Mary Shacklett's article mentions how many organizations have “clean data champions” whose work is often unappreciated and unrewarded. One of her tips for insuring better data quality addresses this directly: “Move greater responsibility for clean data to the end business: IT is the custodian of most corporate data but not its creator, nor its primary user. If the quality of transactional and big data are going to improve, it will be managers within the business who finally get fed up with the daily mistakes and loss of time that are consumed with trying to rectify situations created by the data being wrong the first time.”
So let's think for a moment: Why aren't managers already fed up with the effects of dirty data? Maybe they are not directly measuring the impact of poor quality data or ascribing the need for rework to the wrong causes. Maybe they are uncertain about how to influence the upstream activities that are creating and conveying the dirty data.
My guess is most managers have a shrewd idea of how much dirty data is hurting them, even if they don't always want to put a hard figure on it. But I think many managers have yet to realize they can question and critique the data streams they work with – let alone that they must do so.
It's all about seeing data in terms of value. Data is both raw material and end product of today's businesses. Data is involved in every business process – it's being consumed, transformed, and shared. Once you see data this way, shoddiness becomes unacceptable.
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