In my previous post, I looked into the magic mirrors of business leaders, more commonly called dashboards, as one example of how data visualization is used. In this post, I want to look at what we use to look — our eyes — and how they process whatever data we
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As Beth Schultz recently blogged, Data Visualizations Beg Your Attention. And as Noreen Seebacher recently blogged, A Picture Explains a Lot of Data. Although I agree with both concepts, and I recommend reading more than just the titles of those posts, I couldn’t help but wonder if what should be begging more
In my previous post, I pondered how the inevitable lag time between the definition of requirements and the delivery of solutions is exacerbated by the business world fluctuating dramatically in short periods of time. Today’s business requirements may not only be different than yesterday’s business requirements, but today’s business requirements
As Steve Jobs once said: “You can’t just ask customers what they want and then try to give that to them. By the time you get it built, they’ll want something new.” The inevitable lag time between the definition of requirements and the delivery of solutions is perhaps the primary reason
It has been a while since I shared a story about DQ-IRL (i.e., Data Quality in Real Life, a few of the past stories included The Seven Year Glitch and Data Quality, 50023). While listening to a recent broadcast of NPR’s weekly news quiz program Wait Wait... Don't Tell Me!,
Jim Harris (@ocdqblog) asks whether we can measure the half-life of data.
Does data have an expiration date? Jim Harris (@ocdqblog) explains.
Jim Harris (@ocdqblog) presents a statistically significant resolution for 2013.
Could fear of loss be affecting your data governance? Jim Harris (@ocdqblog) explains.
Jim Harris (@ocdqblog) reveals the Seventh Law of Data Quality.