With all the hype over big data we often overlook the importance of modeling as its necessary counterpart. There are two independent limiting factors when it comes to decision support: the quality of the data, and the quality of the model. Most of the big data hype assumes that the data
Are you eager to explore and understand your data? Do you want to improve processes and drive more revenue? Do you want to work with data using an intuitive, easy-to-use interface? This is now possible with visual analytics, technology which combines analytics with interactive visualizations. As published in a recent
No matter where I go in the world, I’m constantly amazed by the similarities in business challenges facing organizations around the globe. One of the most common is getting business users to actually adopt and use the insights gained from the great work performed by analytics teams and IT organizations.
As a linguist, I am fascinated with words, their meanings and use. So when I recently saw the words “learning analytics” in a conference paper title, I started thinking about the prevalence of the word “analytics” itself. In the last decade, we have preceded “analytics” with many modifiers referring to
The term “disruptive technologies” frightens many organizations, but to companies like Alibaba, Uber, Airbnb and Facebook, it’s the secret to their success. These four companies from unrelated industries have one major commonality: They don’t “own” anything, at least not in the traditional sense. What they provide is a digital service,
In my first blog post in the Education meets Big Data blog series, I explained the need for Statewide Longitudinal Data Systems(SLDS). In the blog post following, I shared an interview with Armistead Sapp, one of the authors of the book, "Implement, Improve and Expand Your Statewide Longitudinal Data System." In this
Machine learning is all about automating the development process for analytical models. One way to extend the use of machine learning is to broaden your library of machine learning algorithms. Another way is to scale your machine learning process by reducing the time required to process machine learning algorithms on
The 2016 summer games are less than a year away, and British sports fans are hoping for a performance that beats Team GB’s record medal tally at London 2012. That’s never been achieved by a country directly after hosting the Games. And the pressure’s on us in the GB Rowing Team given
“Garbage in, garbage out” is more than a catchphrase – it’s the unfortunate reality in many analytics initiatives. For most analytical applications, the biggest problem lies not in the predictive modeling, but in gathering and preparing data for analysis. When the analytics seems to be underperforming, the problem almost invariably
If you know me, you know two undeniable things (other than my love for froyo): I consider shopping a sport and I am an Analytics geek. Being an Analytics geek means that I see potential for using data everywhere, and never more than when it’s my data as a customer.
