Learn more about data for good at Analytics Experience

Analytics Experience 2016 logoSAS was founded on the principal of using analytics to change the world. From fighting cancer and researching the Zika virus to changing the lives of Ghana women by teaching them how to code, SAS has remained committed to helping solve critical humanitarian issues using data and analytics.

What SAS has been doing for more than 40 years is now taking main stage in the industry as a data for good (#data4good) movement. Instead of just using data to boost the bottom line, organizations are looking for new ways to use analytics to make a difference.

As a way to put a spotlight on other companies supporting the data for good movement, you’ll see a big data for good presence at Analytics Experience 2016, Sept. 12-14 in Las Vegas. Jake Porway, founder of DataKind, will be one of this year’s keynote speakers. There will also be data for good breakout presentations from SAS, Dignity Health, Elliot Hospital Systems, San Bernardino County Behavioral Health Department and more. You can find all the sessions by filtering in the agenda for “data for good” in the mobile app. Read More »

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Partnering for prevention: Dignity Health and SAS combat sepsis

There’s been an uptick recently in corporations working together to improve patient experiences and even saves lives. From the recent buzz about drug developers coming together to combat Alzheimer’s, to health systems opening data to researchers, companies realize that partnering just might produce the greatest impact.

As highlighted in Dignity Health demonstrates the power of advanced analytics at HIMSS16, Dignity Health, the fifth largest health system in the nation, and SAS, the world’s largest privately held software company, have teamed since 2014 to use analytics to improve health care delivery.

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Are you ready for the Analytics Fast Track? Part II: The purchase option

In my previous post, Are you ready for the Analytics Fast Track? Part I, I shared details of the SAS and Intel proof of concept program, and promised to follow up with information about our “For Purchase” program.

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

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

Many organizations are looking for sandbox environments that allow their data scientists to play with data, model, explore and fail-fast. They want an environment that's connected to the corporate network, but isolated from production systems -- an environment specifically designed for speed and robust analytical analysis, but in a playpen area where analysts can experiment with many different modeling techniques.

The Analytics Fast Track™ for SAS® (AFT) has an option that allows you to purchase just such a software and hardware environment. SAS has determined a number of software stack use cases that have been built into virtual machines for fast and easy deployment, as well as maintenance.

For example, SAS can provide you with a six-month license of one of the identified software stacks, instructor-led training and SAS Professional Services' “Rapid Start” support. SAS and Intel work with you and the hardware vendor of your choice for the server purchase. We can work with IBM, Cisco, Dell and HP as part of the AFT hardware program. A typical AFT server configuration is 72 core, 3 TB of RAM with 25 TB of SSD storage.

When you purchase an AFT, you're getting industry-leading software and services from SAS, along with a fast server and support from your preferred vendor. After six-months, you have the option for one additional six-month license period, or the production license of the software stack and then you own the server to do with as you please.

The main goal of the AFT For Purchase option is to provide an organization with a state of the art software and hardware environment designed for today’s data scientist. Please contact your account team for any AFT For Purchase questions.

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The road to success with the connected customer and analytics maturity

In an increasingly connected world, the automotive industry is embracing opportunities from the Connected Vehicle and Connected Dealer to reach the Connected Customer. At the recent Automotive Analytics Executive Forum, we heard terrific success stories and far-reaching experiments aimed at facilitating the best customer experiences whether buying a vehicle or getting it serviced.

The connected vehicle will drive new business models and innovation

The connected vehicle and smart mobility are enabling new business models for the automotive industry. Having done analytics for decades, we at SAS have a long history helping our customers leverage analytics as the key differentiator for growth. For the connected vehicle, that means making your customer interactions more intelligent, and accelerating the pace of insights for personalized experiences, product development and improvements to quality, while enabling new business models.

Bob Proctor Quote

Global megatrends can radically change the mobility industry

McKinsey’s Andreas Beiter covered key highlights from their study on the Automotive Revolution – Perspectives Towards 2030. We learned how four disruptive automotive technology-driven trends (connectivity, diverse mobility, autonomous driving, and electrification) will expand revenue pools for Auto OEM’s by 30% (up to $1.5 Trillion). Read More »

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Smart grid analytics and what makes IoT special for utilities

sun setting behind utility wiresYesterday I opened up the Wall Street Journal and found the usual mix of ads from major technology vendors touting their IoT (Internet of Things) prowess, and claiming they all have the secret sauce to make all of our IoT dreams come true. Where do I sign up?!

Meanwhile, back here on planet earth, we're all looking at the massive potential for IoT, and figuring out how to best use this new flood of streaming data to benefit our organizations. For many companies in the utilities industry, that means looking at where and how to best leverage the massive IoT instance right in your backyard: the smart grid.

Beyond the hype in yesterday’s Wall Street Journal was an interesting article by one of their columnists, Christopher Mims, who says the internet of things isn’t about things; it’s about services, which of course put me immediately in a defensive posture… “What does this guy know? It’s all about data!”

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5 steps to analytic modernization

Modern freeway lit up at nightSome organizations I visit don’t seem to have changed their analytics technology environment much since the early days of IT. I often encounter companies with 70s-era base statistical packages running on mainframes or large servers, data warehouses (originated in the 80s), and lots of reporting applications. These tools usually continue to work, and there is a natural—but dangerous—human tendency to leave well enough alone.

Vendors have produced a variety of new tools for predictive and prescriptive analytics, and they offer many custom analytics solutions for industry-specific problems like anti-money laundering in banking, or churn prevention in telecom. Visual analytics are easier to generate, are much more visually appealing, and even offer recommendations for what visuals would best depict a particular type of data or variable relationship.

New tools are leading to a dramatic increase in the speed and scale with which analytics can be performed, and much greater integration with business processes.  I often refer to this set of changes as “Analytics 3.0,” and many large, established firms have adapted these approaches. They make it possible to make analytical decisions in near-real time, which often yields benefits in terms of increased conversions, optimized operations, or other results. And the process of generating analytical models has become substantially more agile.

Not taking advantage of these analytics modernization opportunities has some substantial implications. The 5 step process I outline below will get you started on the road to analytic modernization. Read More »

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It’s not fair…

EV110-049Gender and race discrimination has been banned in most countries for many years, although gender did have specific exclusions for the insurance industry, where the risk for males and females could be shown to substantially different (e.g. females have a higher life expectancy than males). In the European Union (EU) such discrimination has been prohibited. So what does this mean?

For men, they pay less for life insurance and more for annuities

For women, they pay more for life insurance and less for annuities.

The EU has now turned its attention to the algorithms used for everything from which adverts to show online and product recommendations to image recognition and translation. Pretty much everything we do online has an algorithm working behind the scenes. The EU is consulting as to what should be in these algorithms and how to open them up, so that it’s not just a black box making a decision. Read More »

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3 Steps to big data success with text analytics

Text Analytics 2A huge proportion of big data is unstructured text (such as client interactions, product reviews, call center logs, emails, blogs and tweets). Organizations starting to invest in advanced analytics often overlook the value text analytics could add to the process. But when data scientists or analysts get to work exploring the available data to solve specific business problems, they often find that unstructured text contains the more comprehensive information.

In fact, the demand for text analytics has skyrocketed. Forrester finds that text analytics implementations have doubled since 2012. Every organization in every industry has unmet needs and opportunities – and therefore growing interest in tapping into unstructured text. Organizations across industries have adopted text analytics for a variety of use cases, and the ones that have been most successful followed these steps: Read More »

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Big data, IoT and data warehouse?

It's the age of big data and the internet of things (IoT), but how will that change things for insurance companies? Do insurers still need to consider classic data warehouse concepts based on a relational data model? Or will all relevant data be stored in big data structures and thus render classic data warehouses superfluous? Many insurance companies are asking these questions. To find an answer, we have to consider some relevant IoT and big data analytics approaches for the insurance industry.

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Big data analytics had to come before IoT

internet of things

You could say we've been working toward the internet of things (IoT) since computers were first invented. Look at how airplanes have changed from flying by wire to now, quite literally, flying by IoT (or connected plane).

The connected car is another example of how big data analytics is the driving force behind the application of all things IoT. This is why I believe big data analytics had to evolve first, prior to tackling all the cool issues, services and products being driven by IoT.

Looking back through history, you could say that past "IoT" issues were addressed with the technology available at the time, including: the telegraph, telephone, traditional car, the Turing machine vs the German Eniac -- up through present day applications like smart grid, the digital oil field, smart (connected) car, smart appliances, connected factory, connected house, smart building, smart city, etc.

IoT is a driving distributive force because it's bringing, and will continue to bring, change, just like analytics. That makes sense because analytics is the key component for implementing better automation and deriving insights and decisions out of the massive amount of big data and streaming data we can now store and analyze to help drive actions in real-time or near real-time.

But what ultimately drives analytics and IoT is not the computers/robots/sensors or automation. At the heart of all this technology are the people who are necessary to achieve and make use of it all. As two of my colleagues, Tamara Dull and Anne Buff, have previously written to be successful with big data and analytics you need to make sure you get the right people on the bus. I would say this applies to being successful with IoT projects as well.  Learn more by downloading their white paper: Getting the Right People on the Big Data Bus.

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