During a data quality assessment, one of my clients discovered that a large chunk of data that ultimately fed into their business analytics engine was sourced externally. After examining the contracts surrounding this data, I found that 100% of it failed to possess service-level agreements (SLAs) for the quality of
I was surprised to learn recently that despite the reams of laws and policies directing the protection of personally identifiable information (PII) across industries and government agencies, more than 50 million Medicare beneficiaries were issued cards with a Medicare Beneficiary Number that's based on their Social Security Number (SSN). That's
Historically, before data was managed it was moved to a central location. For a long time that central location was the staging area for an enterprise data warehouse (EDW). While EDWs and their staging areas are still in use – especially for structured, transactional and internally generated data – big
@philsimon says that it's downright silly to ignore the benefits of thinking about data-related issues in different and unexpected ways.
Data quality initiatives challenge organizations because the discipline encompasses so many issues, approaches and tools. Across the board, there are four main activity areas – or pillars – that underlie any successful data quality initiative. Let’s look at what each pillar means, then consider the benefits SAS Data Management brings
Balance. This is the challenge facing any organisation wishing to exploit their customer data in the digital age. On one side we have the potential for a massive explosion of customer data. We can collect real-time social media data, machine data, behavioural data and of course our traditional master and
We often talk about full customer data visibility and the need for a “golden record” that provides a 360-degree view of the customer to enhance our customer-facing processes. The rationale is that by accumulating all the data about a customer (or, for that matter, any entity of interest) from multiple sources, you
.@philsimon says that even seemingly useless information can be useful under the right circumstances.
Many people have the perception that data governance is all about policies and mandates, committees and paperwork, without any real "rubber on the road" impact. I want to dispel this viewpoint by sharing a simple example of how one company implemented data governance to enforce something practical that delivered long-term
In my prior posts about operational data governance, I've suggested the need to embed data validation as an integral component of any data integration application. In my last post, we looked at an example of using a data quality audit report to ensure fidelity of the data integration processes for
