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
Small data is akin to algebra; big data is like calculus.
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,
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
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
.@philsimon on the reliability of social numbers.
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
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
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
There are companies that have no data quality initiative, and truly do believe that if they see no data problem. In effect, they say that if it does not interfere with day-to-day business, then there is no data quality problem. From what I have seen in my consulting experience, it usually
