Data Credibility: A Key Data Quality Dimension in the Big Data Era
Recently, there’s been a great deal of discussion within the information management industry about data quality. This discussion has gone beyond simply talking about what data quality means, to cover the potential consequences of poor data quality in operational and analytical environments.
There are many dimensions to data quality. One of the most frequently discussed dimensions is data credibility. First of all, what exactly is data credibility?
As Malcolm Chisholm puts it in an Information Management article – “Data credibility is the extent to which the good faith of a provider of data or source of data can be relied upon to ensure that the data really represents is what the data is supposed to represent, and that there is no intent to misrepresent what the data is supposed to represent.”
Given that we are seeing awe-inspiring advances in technology and an abundance of information available at our fingertips, (big) data credibility has become more important than ever.
For example, social media today is a major catalyst for big data projects. Unfortunately, it is also the most infected area when it comes to data quality. It might be easy to dismiss the concern of data credibility at first, until we consider the impact it has on businesses. Data generated from social media feeds provide organizations a powerful way to connect with customers and prospects to create interest in their products. Interest generates clicks – clicks generate revenue – and revenue dictates the success or failure of a business. But, this can only happen if the data we are getting from these social streams is credible, which often is not the case.
A big data project with unreliable data can have a catastrophic impact on businesses that rely on the outcome. With more and more organizations utilizing data mining and data analytics to improve their business, these companies are taking huge risks dealing with ‘dirty data’.
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Having credible data can make or break your analytics project. With more and more companies realizing the importance of social media and racing to harvest that information and gain insights, credibility is going to place pivotal role in realizing true value of this data.
It is becoming increasingly clear that there is a strong need for higher standards of data credibility. Unreliable data can lead to squandered opportunities and financial challenges for businesses. The ramifications of data credibility have and will continue to have an impact for businesses across the board, in every industry, and in every part of the world.
Originally published on MidSize Insider June 2014
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Credibility requirements haven’t stopped the media world from shifting towards less credibility. For them, being first with something appears to be a bigger motivator than credibility. History will tell if this will create a brand debacle for them or not.
It is undeniable that credibility checks will increase cost and reduce agility. The ‘wisdom’ of crowds should offer a strong central tendency, so in big data, analyses need to shift towards probability assessments, and by design these statistical techniques reduce the impact of low quality/low credibility data.
An other way to say this is that the market will bifurcate in low and high credible data, and that the subsequent analysis techniques need to take this into account.
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