Tag: data quality

Data Management
Dylan Jones 0
How to communicate the role of data governance

Confusion is one of the big challenges companies experience when defining the data governance function – particularly among the technical community. I recently came across a profile on LinkedIn for a senior data governance practitioner at an insurance firm. His profile typified this challenge. He cited his duties as: Responsible for the collection

Analytics | Fraud & Security Intelligence
Ricky D. Sluder, CFE 0
Why Excel isn’t the solution for health care fraud, waste and abuse investigations

To prepare for the data challenges of 2015 and beyond, health care fraud, waste and abuse investigative units (government funded and commercial insurance plans, alike) need a data management infrastructure that provides access to data across programs, products and channels. This goes well beyond sorting and filtering small sets of

Data Management
Faramarz Abedini 0
Big "quality" data

Data quality issues don’t go away just because you have more data. Big data is sometimes considered exempt from the requirement to be integrated, cleansed and standardized. Unfortunately, chances are that the more data you have, the worse its quality will become.

Data Management
Jim Harris 0
Data quality to "DI" for

There is a time and a place for everything, but the time and place for data quality (DQ) in data integration (DI) efforts always seems like a thing everyone’s not quite sure about. I have previously blogged about the dangers of waiting until the middle of DI to consider, or become forced

Data Management
Dylan Jones 0
Can ESP bridge the data quality gap?

As consumers, the quality of our day is all too often governed by the outcome of computed events. My recent online shopping experience was a great example of how computed events can transpire to make (or break) a relaxing event. We had ordered grocery delivery with a new service provider. Our existing provider

Data Management
Stuart Rose 0
Data is King

In my last blog I detailed the four primary steps within the analytical lifecycle. The first and most time consuming step is data preparation. Many consider the term “Big Data” overhyped, and certainly overused. But there is no doubt that the explosion of new data is turning the insurance business

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