Commentary

Video from Festival of Media

The data generated by all sensors will change the way we live. Here's a video discussion with three leaders in the field of human technologies, artificial intelligence and data, about the possibilities of the quantified self. It was filmed during the Festival of Media in 2013.[read more]

Cash flows and outlays / shutterstock

IT leaders often express dismay at the process involved in not only forecasting for CAPEX needs, but then stepping through arduous internal CAPEX budget approvals. What’s all the fuss with CAPEX, and why is it so difficult to obtain?[read more]

business skillset / shutterstock

Employees with technical skills are critical to enable an analytics program, but the ability to communicate, relate and navigate throughout an organization—so called “softer skills”—are especially needed to propagate analysis and communicate the impact of data-driven decision-making.[read more]

Data science: sexier than ever?

A recent Harvard Business Review article argues that being a data scientist is the sexiest job of the 21st century. Data scientists help companies turn their data (big data and small data) into valuable insights. But do the people who have the sexiest job of the 21st century have to be sexy?[read more]

data archive / shutterstock

Preserving knowledge for generations is no easy task. Key components of this massive undertaking include decisions in technology, architecture, data storage, and data accessibility. What are steps to architect a solution to keep your own data safeguarded and accessible long-term?[read more]

Quant worker salary changes / shutterstock

Everyone knows careers in analytics are paying off, but just what kind of pay increase should you expect in switching roles? When evaluating salary change data over the past 18 months of nearly 200 candidates, I have seen both extremes and everything in between with base salary increases.[read more]

Apple's iWatch

Apple's next big innovation - a watch - will provide massive fuel for big data analytics. Wearable devices such as an iWatch will allow all of us to measure and quantify ourselves, which in turn generates massive opportunities for big data analytics on a global scale.[read more]

Excel failure / shutterstock

Two noted economists, Kenneth Rogoff and Carmen Reinhardt, recently made an Excel spreadsheet error that led to some grave miscalculations. But just because mathematical calculations are wrong, it doesn’t mean a particular idea isn’t directionally sound.[read more]

unstructured data/shutterstock

My contrarian’s view of unstructured data: it is, in large part, gigabytes of gossip and yottabytes of yada yada digitized, rumors and hearsay amplified by the illusion-of-truth effect and succumbing to the perception-is-reality effect until the noise amplifies so much that its static solidifies into a signal.[read more]

Predictive analytics

Eric Siegel's new book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, recasts the subject in a form that is “mapped” more closely to common perceptions of what we do in our brains and why we do it. Here's a review, written by Dr. Robert Nisbet.[read more]

grain of salt/shutterstock

For every successful case study listed in Harvard Business Review, Fortune or the like on “Big Data” success, there are likely thousands of failures. Hidden behind all these “success stories,” there is a figurative graveyard of big data failures that we never see.[read more]

Here’s a compilation of my five favorite YouTube videos on the topic of Big Data, including a talk about Hadoop and MapReduce given by Robert Scoble, the CEO of Cloudera.[read more]

Manning/shutterstock.com

Want to see your enterprise flourish? Take a page from the Peyton Manning playbook and bake the concepts of simplicity and precise execution in your business model.[read more]

Math can be hilarious (not that we here at SmartData understand it) but it's funny to see what mathematicians laugh at.[read more]

Now when I think about top lists, I just think of how many of them there are and how they annoy me. And I don’t get annoyed that often, so 1. These lists must be extremely annoying, or 2. I have a special annoyance spot in my heart, brain, liver or elsewhere saved just for them.[read more]