In my previous post, I talked about how a bank realized that data quality was central to some very basic elements of its initiatives, such as know your customer (KYC), customer on-boarding and others. In this blog, let’s explore what this organization did to foster an environment of data quality
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
One of the significant problems data quality leaders face is changing people's perception of data quality. For example, one common misconception is that data quality represents just another data processing activity. If you have a data warehouse, you will almost certainly have some form of data processing in the form
After working in the data quality industry for a number of years, I have realized that most practitioners tend to have a rather rigid perception of the assertions about the quality of data. Either a data set conforms to the set of data quality criteria and is deemed to be acceptable
Over the course of the last eight years, I've interviewed countless data quality leaders and learned so much about the common mistakes and failures they've witnessed in past projects. In this post I wanted to highlight five of the common issues and give some practical ideas for resolving them: #1: Not connecting
How well do you know big data in the retail industry? Want to find out? Read the following statements and pick which one is false: In the retail industry, big data is still five years away from becoming mainstream. In 2013, large billion dollar retailers spent an average of $75,000, or
While not quite at the level of big data, data discovery is attracting a good bit of attention these days. I explore both topics in The Visual Organization and Too Big to Ignore. It's only fair for people to ask if their legacy reporting tools support big data and data discovery. In short, the
THIS IS HARD TO DO! In our agile world we seem to never get the data model completed until two weeks after we are in production, and every project plan wants to waterfall the completion of this deliverable. I think it may be due to the rapid way we gather and refine requirements. For
You’ve read the research reports and seen the statistics. You’ve attended the conferences and heard the case studies. You’ve read the online articles and kept up with expert opinions. Your organization has even done a few big data sandbox projects – some successful, some not. Yet the jury is still
The "Internet of Things" is the latest buzzword characterizing the machine-generated big data that has outstripped our ability to derive value from it. Think of UPS delivering 16 million packages every day through various hubs and all the logistics and decisioning that goes into that. But how does an organization
