Tag: big data

Paul Kent 0
Share your cluster – How Apache Hadoop YARN helps SAS

Even though it sounds like something you hear on a Montessori school playground, this theme “Share your cluster” echoes across many modern Apache Hadoop deployments. Data architects are plotting to assemble all their big data in one system – something that is now achievable thanks to the economics of modern

Jim Harris 0
Data science versus narrative psychology

My previous post explained how confirmation bias can prevent you from behaving like the natural data scientist you like to imagine you are by driving your decision making toward data that confirms your existing beliefs. This post tells the story of another cognitive bias that works against data science. Consider the following scenario: Company-wide

Jim Harris 0
Can data change an already made up mind?

Nowadays we hear a lot about how important it is that we are data-driven in our decision-making. We also hear a lot of criticism aimed at those that are driven more by intuition than data. Like most things in life, however, there’s a big difference between theory and practice. It’s

Jim Harris 0
Bring the noise, boost the signal

Many people, myself included, occasionally complain about how noisy big data has made our world. While it is true that big data does broadcast more signal, not just more noise, we are not always able to tell the difference. Sometimes what sounds like meaningless background static is actually a big insight. Other times

Jim Harris 0
The ethics of algorithmic regulation

In my last three posts on data ethics, I explored a few of the ethical dilemmas in our data-driven world. From examining the ethical practices of free internet service providers to the problem of high-frequency trading, I’ve come to realize the depth and complexity of these issues. Anyone who's aware of these

Jim Harris 0
The low ethics of high-frequency trading

Imagine if your ability to feed your family depended upon how fast you could run. Imagine the aisles of your grocery store as lanes on a running track. If you can outrun your fellow shoppers, grab food off the shelves and race through the checkout at the finish line, then

Analytics
Nele Coghe 0
Hot hot heat map

Although I’m not particularly excited about football (I admit, I don’t completely understand what offside means), I did follow the last World Cup with more than average attention. Not only for the handsome players, but especially for all the fascinating statistics that appeared. It struck me that heat maps popped

SAS Colombia 0
Manténgase activo y con empleo en la era del Big Data

Los datos importan más que nunca. Las organizaciones en progreso como Netflix y La Universidad de Texas, entre otras, están usando herramientas de visualización de datos para encontrarle forma a las grandes cantidades de información que se generan diariamente. En el libro The visual organization, Phil Simon, reconocido experto en

Jim Harris 0
Mapping ethics in a data-driven world

In my previous post, I examined ethics in a data-driven world with an example of how Facebook experiments on its users. Acknowledging the conundrum facing users of free services like Facebook, Phil Simon commented that “users and customers aren’t the same thing. Maybe users are there to be, you know... used.” What about when a

Jim Harris 0
Facing ethics in a data-driven world

I have previously blogged about how the dark side of our mood skews the sentiment analysis of customer feedback negatively since we usually only provide feedback when we have a negative experience with a product or service. Reading only negative reviews from its customers could make a company sad, but could reading only

Jim Harris 0
Data science and decision science

Data science, as Deepinder Dhingra recently blogged, “is essentially an intersection of math and technology skills.” Individuals with these skills have been labeled data scientists and organizations are competing to hire them. “But what organizations need,” Dhingra explained, “are individuals who, in addition to math and technology, can bring in

Jim Harris 0
The data that supported the decision

Data-driven journalism has driven some of my recent posts. I blogged about turning anecdote into data and how being data-driven means being question-driven. The latter noted the similarity between interviewing people and interviewing data. In this post I want to examine interviewing people about data, especially the data used by people to drive

Jim Harris 0
Being data-driven means being question-driven

At the Journalism Interactive 2014 conference, Derek Willis spoke about interviewing data, his advice for becoming a data-driven journalist. “The bulk of the skills involved in interviewing people and interviewing data are actually pretty similar,” Willis explained. “We want to get to know it a little bit. We want to figure

Data Management
Alyssa Farrell 0
A must-read for petroleum professionals

Oil companies are being forced to explore in geologically complex and remote areas to exploit more unconventional hydrocarbon deposits.  New engineering technology has pushed the envelope of previous upstream experience.  No guidebook existed on how computing methodologies can contribute to E&P performance at reduced risk.  Until now. A new book

SAS Colombia 0
Cinco características esenciales de la computación en la nube

Puede llamarlo transformación, cambio de paradigma, evolución o revolución. No importa  qué nombre reciban: en la actualidad, los servicios en la nube están cambiando la realidad de los negocios. La posibilidad y necesidad de acceder a la información desde cualquier sitio, en cualquier momento y con prácticamente cualquier dispositivo, ha

Data Management
David Pope 0
Your "SAS view" on steroids

Data federation is a relatively new term used to describe a form of data virtualization. Data virtualization, however, is not new. It has been around since at least the 1960's when virtual memory was introduced to simulate additional memory beyond what was physically available on a machine.  While data federation is a

Jim Harris 0
A double take on sampling

My previous post made the point that it’s not a matter of whether it is good for you to use samples, but how good the sample you are using is. The comments on that post raised two different, and valid, perspectives about sampling. These viewpoints reflected two different use cases for data,

Jim Harris 0
Survey says sampling still sensible

In my previous post, I discussed sampling error (i.e., when a randomly chosen sample doesn’t reflect the underlying population, aka margin of error) and sampling bias (i.e., when the sample isn’t randomly chosen at all), both of which big data advocates often claim can, and should, be overcome by using all the data. In this

Advanced Analytics
Michael Thomas 0
Big data in your face

If you've followed technology news in the last few weeks, you've probably seen a proliferation of awestruck faces wearing blacked out goggles, similar to the image I'm using here. The goggles are Oculus Rift immersive VR headsets, and the news is the $2 billion dollar acquisition of Oculus by Facebook.

SAS Colombia 0
6 consejos para convertirse en un súper CIO

Debido al crecimiento masivo de las Tecnologías de la Información en la última década, el Chief Information Officer (CIO) ha pasado de tener una función de apoyo de back-office a una unidad de negocio crítica para las decisiones rentables del negocio. En la actualidad se ubica entre las cinco áreas con

Jim Harris 0
What we find in found data

In his recent Financial Times article, Tim Harford explained the big data that interests many companies is what we might call found data – the digital exhaust from our web searches, our status updates on social networks, our credit card purchases and our mobile devices pinging the nearest cellular or WiFi network.

Jim Harris 0
The dark side of the mood

As an unabashed lover of data, I am thrilled to be living and working in our increasingly data-constructed world. One new type of data analysis eliciting strong emotional reactions these days is the sentiment analysis of the directly digitized feedback from customers provided via their online reviews, emails, voicemails, text messages and social networking

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