Data Science
While the field of data science is not tied directly to Big Data, advances in one tends to produce advances in the other. Big Data increases our ability to harvest and process data, while data science allows us to dig into it for insights.

Government Stats Are Ready for Change (Book Review)
For those of you similarly interested (obsessed?) with the changing role of government statistics relative to the explosion of highly dimensional private sector data, I recommend having a look at Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy from the National Academy of Sciences. It’s an easy read and offers a solid

AIOps – The Type of ‘AI’ with Nothing Artificial About It
The term AI (Artificial Intelligence) is being thrown around left and right these days. Many companies claim they have an AI play even when they don’t. But there’s another type of AI—an algorithmic approach to intelligence—that is smart and is emerging as the type of AI that IT organizations of

Programming with R – How to Get a Frequency Table of a Categorical Variable as a Data Frame
Categorical data is a kind of data which has a predefined set of values. Taking “Child”, “Adult” or “Senior” instead of keeping the age of a person to be a number is one such example of using age as categorical. However, before using categorical data, one must know about various

Seizing Opportunities with Data-as-a-Service Products
We’ve published a white paper, where we look back at the big data and business intelligence trends over the past years and highlight examples of successful Data-as-a-Service products with deep dives into Social Media Monitoring, Self-Service BI and Visual Data Discovery and Analytics Merging the Physical and Virtual Worlds, complete with lessons

How Augmented Intelligence Helps Businesses Grow
It used to be a maxim that expanding too fast was the quickest way to kill a successful business. Rapid growth brings risks as well as opportunities. But utilizing augmented intelligence, an already-popular technology which surfaces patterns in data without humans having to look at it, means that organizations can

Data Science vs. Data Analytics – Why Does It Matter?
Data Science, Data Analytics, Data Everywhere Jargon can be downright intimidating and seemingly impenetrable to the uninformed. While complicated vernacular is an unfortunate side effect of the similarly complicated world of machines, those involved in computers, data and whole host of other tech-intensive sectors don’t do themselves any favors with

Corporate Self Service Analytics: 4 Questions You Should Ask Yourself Before You Start
Pyramid Analytics and Big Data Expert Ronald van Loon are hosting a free webinar on March 23rd. Register now and find out how to adopt a data-driven approach that will help your organization grow with predictive analytics. This webinar has been tailored to meet the needs of corporations in the DACH

If you care about Big Data, you care about Stream Processing
As the scale of data grows across organizations with terabytes and petabytes coming into systems every day, running ad hoc queries across the entire dataset to generate important metrics and intelligence is no longer feasible. Once the quantum of data crosses a threshold, even simple questions such as what is

The Problem With (Statistical) False Friends
I recently stumbled across a research paper, Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US, which piqued my interest in derivative uses of data, an ongoing research interest of mine. A variety of deep learning techniques were used to draw conclusions about relationships

Infographic: A Beginner’s Guide to Machine Learning Algorithms
We hear the term “machine learning” a lot these days (usually in the context of predictive analysis and artificial intelligence), but machine learning has actually been a field of its own for several decades. Only recently have we been able to really take advantage of machine learning on a broad