Some businesses don't realize they have a data quality issue until it's too late
Most businesses have caught on to Big Data and now recognize the inherent values this information can provide. However, this realization does not necessarily mean they know how to organize that data and verify its accuracy once it's been collected.
In fact, many enterprises approach data quality in a way that resembles the five stages of grief, writes Jim Harris in a recent OCDQ blog entry. Harris explains that most companies start out denying there is any problem with the data they have, which is followed by anger when they face significant issues that can't be blamed on any person in particular.
After the anger subsides, they become willing to invest in a quick fix, but this may only lead to subsequent problems and disappointment.
In fact, convincing companies to approach data quality as a collaborative effort between business and IT teams remains a problem, according to Hub Designs Magazine. Most firms will only be convinced when they are shown a top-down, results-driven approach to reforming their current data quality management efforts.
Only after businesses have accepted the challenges they face and committed themselves to consistent upkeep and management of their data quality will they be able to overcome these problems.
In fact, many enterprises approach data quality in a way that resembles the five stages of grief, writes Jim Harris in a recent OCDQ blog entry. Harris explains that most companies start out denying there is any problem with the data they have, which is followed by anger when they face significant issues that can't be blamed on any person in particular.
After the anger subsides, they become willing to invest in a quick fix, but this may only lead to subsequent problems and disappointment.
In fact, convincing companies to approach data quality as a collaborative effort between business and IT teams remains a problem, according to Hub Designs Magazine. Most firms will only be convinced when they are shown a top-down, results-driven approach to reforming their current data quality management efforts.
Only after businesses have accepted the challenges they face and committed themselves to consistent upkeep and management of their data quality will they be able to overcome these problems.
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