Analytics for IoT gets smart cities moving in the right direction

LA Highway lit up at duskLiving for 10 years in California and 12 in Washington DC with a commute into downtown, I’ve spent a lot of time in my car.  While road and traffic signal infrastructures are designed to do their best to facilitate my drives on an average day, these infrastructures are historically pretty terrible at dynamically adjusting to changing conditions.

Whether it’s Southern California drivers screeching to a halt on the 101 because a drop of rain hit their windshield, or the Secret Service closure of everything west of 14th Street in downtown Washington because of the visiting President of Djibouti, drivers and city managers alike know how hard it can be to dynamically manage our infrastructures.

Imagine if transportation officials could dynamically adjust speed limits according to detected weather, accidents, or other road conditions.  Could traffic signal timing and duration be dynamically managed to optimize traffic flow in a congested urban center?  What if motorists could be dynamically routed to nearest available parking, saving citizen time and reducing road congestion?  Efficiency challenges like these exist across the spectrum of municipal infrastructures, and the internet of things (IoT) provides a host of new opportunities to make our cities smarter through the intelligent use of data from the connected environment.

Read More »

Post a Comment

Navigating the open waters of machine learning

I’m an avid open water swimmer. In order to succeed in open water races I must do two things:

  • I must sight properly in order to swim the straightest line possible in the right direction. The straighter the line, the less I have to swim.
  • I must use a powerful, yet efficient stroke. The proper stroke is imperative. I must use a powerful stroke to propel forward in the water, but must do it efficiently to conserve energy for long swims.

Enterprises analyzing their data must sometimes feel like they’re drowning or swimming aimlessly in the open waters. Luckily, SAS provides solutions and computing architectures to help organizations succeed in the “open waters” of data mining and machine learning. Read More »

Post a Comment

What you need to know about programmatic advertising

481489727Once upon a time, the only way to buy and sell advertising inventory was based on the relationship between advertiser and publisher. The exchange was manual, with advertisers paying publishers an agreed-upon price for every impression. The process involved a lot of phone calls, spreadsheets, negotiation and re-negotiation, all of which took a lot of time and effort.

Now, there’s another option: Programmatic advertising.

So what is programmatic? It’s the use of software that uses data to inform the buying and selling of advertisements. More specifically, programmatic is the buying of target individuals. How? By using big data to determine the right ad for the right consumer at the right time. Programmatic software allows for automation, to varying degrees, and therefore requires considerably less human effort than the traditional method. Read More »

Post a Comment

Are you ready for the Analytics Fast Track? Part I

Meet "rolling SAS," part of our Analytics Fast Track program.

Ever hear the phrase: "If the mountain won't come to you, you must go to the mountain"? Well, at SAS, we bring the mountain to you. The Analytics Fast Track™ for SAS®, affectionately called “rolling SAS,” is a partner program between SAS and Intel that showcases the incredible performance of SAS running on the elite Intel® E series chip set.

The AFT provides SAS account teams around the world the ability to quickly develop and deliver customer use-case specific PoC’s onsite.

Why is this so important? Data security. Companies don't want to risk their data, particularly personalized information, by sending it out for a PoC, so they lose out on getting a PoC that's specific to their data and needs. So, we simply take the mountain (secure SAS® software) to you.

The AFT is designed to be stand-alone (i.e. it does not connect to the customers’ network) and comes with its own dedicated wireless router.  The customer provides an “external drive” with the necessary data to be copied onto the server once the server arrives onsite. Once the proof is complete, the software and data are wiped clean before the server is returned to its central hub.

A typical AFT server configuration is 72 core, 3 TB of RAM with 25 TB of SSD storage, running SAS as a VM with a node Hadoop cluster. By deploying SAS via a virtual machine, the AFT environment can be up and running in just a few hours with the architect team developing the proof.

SAS currently has 10 plus AFT servers strategically located around the world. A typical proof runs about 7-10 days depending on the amount of prework that can be accomplished prior to the AFT arriving at the customer's location. Reserve your AFT today -- contact you support team for reservation specifics.

Now that we've covered using the AFT for a proof of concept, be on the lookout for Part II, where I'll cover the “For Purchase” option of the program.

Post a Comment

Getting started with IoT

Analytics Experience 2016 logoThe Internet of Things (or IoT, as some like to call it) holds a number of benefits for many organizations: revenue growth, smarter decision making and efficiency. Experts are predicting anywhere from 20 to 50 billion devices will be connected to the internet by 2020. That’s almost here! Are you ready? And how are you getting started with IoT?!

There’s no question that getting into the IoT business is going to be complicated and time consuming.  Amber MacArthur, technology leader and president of Konnekt, believes companies aren’t ready for the next evolution of the Internet of Things. In this video MacArthur explains, “At the core of the Internet of Things, it’s all about data.”

Read More »

Post a Comment

Stepping up: The rise of the platform economy

The rapid growth of the digital economy has put it on course to account for 25 percent of the world’s entire economy by 2020. Platform business models represent a large proportion of the overall total but what do we mean by platform? MIT Professor Michael Cusumano defines it as follows: “A platform or complement strategy differs from a product strategy in that it requires an external ecosystem to generate complementary product or service innovations and build positive feedback between the complements and the platform.”

The platform economy has transformed the way goods and services are produced, shared and delivered. Gone are the days where individual firms are competing for customers. A newer, flatter and more participatory model has emerged, whereby customers engage directly with each other.

Quid pro quoMobileApp

There are stark differences between the traditional business model, where value creation is linear and one-way, and the platform model. The platform-driven business model demands efficiency, is two-way and continuous.

Take for example, Uber, a company that's thriving in the new digital economy. An Uber driver creates value by announcing their availability and sharing their location. Sharing this information helps match them with the right user. When a user accesses their phone and requests a driver, the user shares his location and is then paired with the driver who's closest to the user’s location when the request is submitted. The platform business model represents a great opportunity to create growth in the digital economy. According to Harvard Business Review: “With a platform model, the critical asset is the community and resources of its members.” There's a clear shift from the control of resources to orchestrating them. Essentially then, Uber is really just a transaction broker. Read More »

Post a Comment

Why IoT deployments need design thinking

470283497Design thinking is, broadly speaking, allowing user experience, or even users, to drive design. It’s a profoundly human-centered process, with commentators using words like ‘collaborate’, ‘experience’, and even ‘empathy’ in their descriptions. Steve Jobs is said to have used it in creating the iPod and iPhone, because it brings together product design and human behavior.

So, what does this have to do with Internet of Things (IoT) deployments? The connection is big data and, perhaps more importantly, how to get the right insights from it. Read More »

Post a Comment

3 ways you can compete in the collaborative economy

Analytics Experience 2016 logoPowered in equal parts by data, mobility and innovation, the collaborative economy has changed the way we think about automobiles, travel accommodations, office space, living space – and so much more.

When anyone can open an app to borrow money from a crowd, order a gourmet dinner from a neighbor, or rent an oceanfront bungalow from a stranger, where does this leave more established businesses?

Can companies built on traditional business-to-consumer models survive in the peer-to-peer economy? More pointedly, will all businesses suffer the fate of the yellow cab companies? Or is there a way for larger enterprises to embrace – and benefit from – the collaborative economy? Read More »

Post a Comment

Turning your data historian into a futurist

Under-utilized technology creates a drag on an organization. The ability to get more out of the tools you already use can increase the value of an existing investment, and that value grows as processes become more efficient and decisions are based on firmer foundations.

Consider the facilities engineer at an oil refinery. She might run 12 weeks of historical data out of her data historian to monitor process performance, examine productivity gains or losses, or understand why an equipment failure occurred. It’s an invaluable tool for examining past performance. But what if she could harne460818751lorezss these massive amounts of process control data, gathered from every sensor in the operation and logged in the historian, to enable forward-looking decisions?

Data under-utilization goes on across the oil and gas value chain. Very large quantities of data are stored – at high cost – but they are used for small tasks, usually by a single user in a spreadsheet. It's a missed opportunity to enable more users to surface a range of outcomes from the same data, using an analytics platform. Read More »

Post a Comment

Community college answers complex ‘why’ questions with data visualization and analytics

Reporting can reveal last year’s graduation rates or this semester’s completion rates at a local community college. But drilling further into that data to ask why students aren’t graduating or why they aren’t enrolling requires more complex analysis.


Karl Konsdorf, Director of Research, Analytics and Reporting at Sinclair Community College

At Sinclair Community College in Dayton, Ohio, college administrators and professors are using data visualization and analytics to improve completion rates, optimize class sizes and assist graduates with job placements that pay well.

Karl Konsdorf, Director of Research, Analytics and Reporting at Sinclair Community College says, “Every year, we see improvements in the number of students transferring in and earning degrees. And we are seeing fewer dropouts.”

To hear more about Sinclair’s use of analytics, read the full interview with Konsdorf below. 

Read More »

Post a Comment