When you spend long enough writing and working in any industry, you inevitably see trends emerge and reach varying levels of maturity. Data governance is one such trend, as you can see from the following Google Trends chart:
.@philsimon lists the gravest data-quality errors.
I've been doing some investigation into Apache Spark, and I'm particularly intrigued by the concept of the resilient distributed dataset, or RDD. According to the Apache Spark website, an RDD is “a fault-tolerant collection of elements that can be operated on in parallel.” Two aspects of the RDD are particularly
Data quality has always been relative and variable, meaning data quality is relative to a particular business use and can vary by user. Data of sufficient quality for one business use may be insufficient for other business uses, and data considered good by one user may be considered bad by others.
I recently presented a webinar (via the IAIDQ) on the topic of 7 Habits of Effective Data Quality Leaders. To prepare, I looked back at the many interviews of leading data quality practitioners I had undertaken over the years. A common trait among all these interviews stood out – they
.@philsimon chimes in on this pivotal question.
As I explained in Part 1 of this series, creating a strategy for the data in an organization is not a straightforward task. Two of the most important issues you'll want to address in your data strategy are data quality and big data. Data quality There can be no data that is
One of the challenges my clients struggle with is figuring out how to execute against a proposed data strategy. The visionaries are always happy to participate in the process of assessing the current state and proposing a vision for the future. And adding business justifications and quantifiable metrics for success
Back before storage became so affordable, cost was the primary factor in determining what data an IT department would store. As George Dyson (author and historian of technology) says, “Big data is what happened when the cost of storing information became less than the cost of making the decision to
Creating a strategy for the data in an organization is not a straightforward task. Not only does our business change – our software solutions also change before we can ever get done with a data strategy. So, I choose to understand that a strategy has a vision, and my vision may change
