Now that another summer of 12-hour family road-trips to Maine and Ohio, pricey engineering and basketball camps for the kids, and beating the heat at the beach are over, I've taken a fresh look at what people are focused on with their data – and what SAS is providing in the data management space.
¿Alguna vez ha pensado cuántas decisiones importantes debe tomar en la vida? Por suerte no son tantas: a qué colegio o universidad ir, casarse o no casarse, tomar un trabajo o no, en dónde vivir… etc. Nunca habrá una “penúltima” respuesta para estas decisiones, pero todas ellas necesitan de nuestros
A few of our clients are exploring the use of a data lake as both a landing pad and a repository for collection of enterprise data sets. However, after probing a little bit about what they expected to do with this data lake, I found that the simple use of
While discussing ways and means to improve Sales and Operations Planning (S&OP) and forecasting, many a time business executives ask “What can we do with social media?" This was definitely NOT a usual topic in S&OP forum just a few years back! Most of the time, I push back the
It’s been very hot here in Northern Italy: electricity provision has struggled to keep up and we’ve had frequent power outages in the area, even within our apartment building. A bit inconvenient? Don’t get me started. I feel like my home appliances have turned against me, taking me back to
Jorge Luis Sánchez, Decano Facultad Ingeniería Universidad Javeriana La ciencia de datos se ha convertido en un concepto muy usado últimamente. No tiene una definición clara ya que varias organizaciones y personas lo han clasificado de diferentes maneras, incluso algunas lo han etiquetado simplemente como una ciencia más, pero esto
In my previous post I talked to John Cassara about the growing threat of mobile payments and how mobile phones can be used to launder illicit funds globally. I spoke with him again recently on the topic of financial intelligence. Here are the highlights from our discussion. So what is
.@philsimon offers his definition of the term.
Bigger doesn’t always mean better. And that’s often the case with big data. Your data quality (DQ) problem – no denial, please – often only magnifies when you get bigger data sets. Having more unstructured data adds another level of complexity. The need for data quality on Hadoop is shown by user
“Un científico de datos es una persona que es mejor estadístico que un ingeniero de sistemas y que es mejor ingeniero de sistemas que cualquier estadístico”, Jorge Quiroga, CEO de Blacksip. Ese es el tono en el que se habla hoy de los científicos de datos. Tono que si le
In the oil and gas industry, analytics are used to improve both upstream and downstream operations, from optimizing exploration and forecasting production to reducing commodity trading risk and understanding customer's energy needs. If you plan to derive value from the digital oil field, big data, and analytics, one of the first things
Oh, how times have changed during my 20-plus years in the insurance industry. Data wasn’t a word we used much back in the 80s and 90s, unless of course you worked in those arcane and mysterious IT data centres. Even amidst the computerisation of the insurance industry in the 80s, many
Oh, how times have changed during my 20-plus years in the insurance industry. Data wasn’t a word we used much back in the 80s and 90s, unless of course you worked in those arcane and mysterious IT data centres. Even amidst the computerisation of the insurance industry in the 80s, many
In my previous blog post, I discussed the benefits of a Statewide Longitudinal Data System (SLDS) and shared a SAS book on the subject: Implement, Improve and Expand Your Statewide Longitudinal Data System by Armistead W Sapp III and Jamie McQuiggan. Today, I'm sharing a conversation I had with one of the book’s authors,
You have to be "in it to win it" as they say. This is becoming the case for many organisations that need to start using data to make better, evidence-based business decisions. Today, using analytics is not so much a data lottery as a data necessity. Some businesses may not
During a lighthearted moment in a serious conversation, Howard Schmidt, cyber security advisor to multiple presidents, told a Wall Street Journal interviewer that relying on a government agency as your primary backstop during a major cyber security breach is akin to calling Ghostbusters: you might not get the help you
The amount of data being produced and captured from the plant floor today is staggering. When you add it all up, a factory can easily generate over 34 Terabytes of data per day, or nearly 9 Petabytes a year - that's 9 Million Gigabytes! Yet, with all of those systems, and all
We’ve been talking about data recently at the Analytic Hospitality Executive. I’ve advocated to use whatever data you have, big or small, to get started today on analytic initiatives that will help you avoid big data paralysis. In this blog, I’m going to get a bit more technical than usual
Durante los últimos años, Tamara Dull, directora de tecnologías emergentes para SAS Best Practices, ha escrito mucho sobre Big Data. Sus conclusiones son interesantes: pensamos que el big data es algo nuevo, pero la verdad, hemos lidiado con él durante años; el correo electrónico, las fotos, los videos, los archivos
What's that productivity related quote by Charles Dickens? "My advice is never do tomorrow what you can do today." For years, machine learning has been written about and discussed widely with a focus on the benefits it will bring in the near future. But guess what? The future for machine learning
I'm gearing up to teach the next "DS2 Programming Essentials with Hadoop" class, and thinking about Warp Speed DATA Steps with DS2 where I first demonstrated parallel processing using threads in base SAS. But how about DATA step processing at maximum warp? For that, we'll need a massively parallel processing
I’ve had a lot of discussions with business leaders around the discrepancy between big data investment fears and successful use cases. Most of them say that "the quest for the golden use case" takes too much time and is usually not successful in the end. Ultimately, this quest can lead to
We recently met up with Paul Bennett, a member of the GB Rowing Team and current World Champion, and Laurie Miles, Head of Analytics for SAS UK & Ireland, who has been analyzing the team's data. They chatted about data, the life and mind of an elite sportsman, and uncovered some
Technology has brought the world a great deal of good, but the downside is that we’re increasingly vulnerable to some seriously scary stuff: Terrorists taking control of airplanes through the in-flight entertainment system. Governments breaking into secure systems and stealing identities. Thugs messing with the steering of self-driving cars. When
I am a runner – a trail runner to be more precise. There are many trails that I enjoy running near where I live, but what I really anticipate is the opportunity to explore a new trail. Whenever I travel, my trail shoes go with me and I try to
The smart grid is a technology infrastructure that adds intelligent capabilities to the electricity distribution system. When you apply analytics to the smart grid data, you can automate and improve operations, maintenance, planning and customer satisfaction - among other processes. As utilities continue to upgrade meters, transformers, and add new sensors and equipment,
With all of the discussion about big data these days, it is easy to think that every problem is a big data problem. Yes, there is a lot of data out there these days, and of course we all love a nice big data set, but you don’t always need
As monsoon season begins, many Nepal earthquake victims have shelter over their heads thanks in part to an unlikely intersection of two SAS global development projects. The first project is with the International Organization for Migration (IOM). IOM is the first responder to any crisis that displaces people. IOM provides
I just spent much of the past week watching and trying to ride waves on the North Carolina coast. Small waves, mind you, nothing spectacular and certainly nothing that you would consider edgy or life-altering. Nothing that big wave surfers like Laird Hamilton, Garrett McNamara and others of their substance
What does the future of analytics look like in your organizations enterprise architecture? Does it include thinking about a two speed approach to analytics which includes both: An agile rapidly changing analytics platform for innovation (a lab) seperated from operations and broad enterprise audience usage A slowly moving systematic enterprise analytics platform (a factory)
