The impact of data quality reach

One of the common traps I see data quality analysts falling into is measuring data quality in a uniform way across the entire data landscape. For example, you may have a transactional dataset that has hundreds of records with missing values or badly entered formats. In contrast, you may have […]

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Showing the ugly face of bad data: Part 1

Financial institutions are mired with large pools of historic data across multiple line of businesses and systems. However, much of the recent data is being produced externally and is isolated from the decision making and operational banking processes. The limitations of existing banking systems combined with inward-looking and confined data practices […]

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What skills will be required to make sense of big data?

Small data is akin to algebra; big data is like calculus.

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Big wishes for data management

In the movie Big, a 12-year-old boy, after being embarrassed in front of an older girl he was trying to impress by being told he was too short for a carnival ride, puts a coin into an antique arcade fortune teller machine called Zoltar Speaks, makes a wish to be big, […]

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The three myths of ongoing data quality: Financial gains based on data quality

If you are looking for a way to fund your data quality objectives, consider looking in the closets of the organization.  For example, look for issues that cost the company money that could have been avoided by better availability of data, better quality of the data or reliability of the […]

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Crowdsourcing data improvement: Part 3

In this blog series, I am exploring if it’s wise to crowdsource data improvement, and if the power of the crowd can enable organizations to incorporate better enterprise data quality practices. In Part 1, I provided a high-level definition of crowdsourcing and explained that while it can be applied to a wide range of projects […]

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Social media numbers: The data quality challenge

.@philsimon on the reliability of social numbers.

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The three myths of data quality: Put it in production and leave it alone

Once in a while, people run into an issue with the data that doesn't really need to be fixed right to ensure success of a specific project.  So, the data issues are put into production and forgotten.  Everyone always says, “We will go back and correct this later.”  But that […]

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Compliance finally gives data quality the platform it deserves

Regulatory compliance is a principal driver for data quality and data governance initiatives in many organisations right now, particularly in the banking sector. It is interesting to observe how many financial institutions immediately demand longer timeframes to help get their 'house in order' in preparation for each directive. To the […]

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Crowdsourcing data improvement: Part 2

In this blog series, I am exploring if it’s wise to crowdsource data improvement, and if the power of the crowd can enable organizations to incorporate better enterprise data quality practices. In Part 1, I provided a high-level definition of crowdsourcing and explained that while it can be applied to a wide range of projects […]

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