How energy is tackling big data and the Internet of Things

183825003With presidential candidates targeting alternate energy development, the Clean Power Plan officially in place last month, and the rapid evolution of connected devices and the Internet of Things -- life in the energy sector is even more interesting than usual. To catch up on the latest developments, I sat down with one of our energy experts, Alyssa Farrell. Here are the highlights from our conversation:

How has the energy sector evolved over the past few years?
Alyssa: The energy sector is seeing new entrants, often competitive, along many parts of the utility value chain. As a result, relationships with customers are more important than ever. Whether it’s improving reliability or delivering personalized energy services, today’s utility executive is focused on improving customer experience and customer satisfaction. To deliver on that expectation, utilities need to modernize internal systems to capture new customer insights regardless of which operational system created the relevant data.

How are utilities making use of big data and the Internet of things”?
Alyssa: Utilities have had a big data challenge for many years, but they solved it by continually increasing the size of their operational technology systems. The Internet of Things is bringing those large operational technology systems together with IT (networks and analytical software). For utilities, this can lead to enhanced “sense and respond” capabilities, placing more real-time intelligence in the hands of grid operators. Another example is enabling more personalized energy services, such as Jason Handley from Duke Energy recently described in this YouTube video.

How are utilities responding to regulatory requirements regarding smart meters?
Alyssa: Many regulators have insisted that smart meters are critical to achieving national energy efficiency goals. In reality, while smart meters give utilities the opportunity to establish peak pricing programs (if allowed within the tariffs) changing customer behavior is proving to be very, very difficult. Customers expect more dynamic feedback from their energy provider, often in the form of mobile or in-home displays. So as utilities move ahead with smart meter deployments, they’re also thinking beyond the meter to providing interactive customer displays or partnering with third parties who specialize in mobile information delivery.

With more and more data available for analysis in the energy sector, how do you see decision-making in the future?
Alyssa: More data is always a good thing, if you’re SAS! More data means that you have increased confidence in the decisions because there are more data points to use when building predictive models. At the executive level, increased confidence in energy forecasts, commodity trading and asset decisions will have a direct impact on your bottom line. In addition, you have more data to build richer graphical displays, whether on an interactive map, scatter plot, or tile chart. So we say, bring it on!

The energy sector has heavy investments, an amortization period between 30-50 years and a market with semi-annual “convulsions,” sometimes caused by regulatory obligations, or by market reactions (eg. oil crisis). So, how are organizations responding?
Alyssa: We see the business model of utilities shifting away from a heavy reliance on assets as an investment vehicle. The volatility in the commodity markets certainly support a diversified fuel portfolio. And market analysts predict that growth in the utility sector will come from monetizing customer relationships. All of these factors are influencing utility investment decisions, often seeking to reduce long-term operations and maintenance costs. As a result, leveraging predictive analytics is critical to manage the risk of asset failure and to extend the full lifecycle of the asset.

With the growth of environmental concerns and alternative energy development, what impact do you see on operations management?
Alyssa: The variability of production from distributed energy resources such as wind and solar definitely impacts operations. Budgets are squeezed, cost recovery isn’t a sure thing, maintenance now has a wider range of technical issues to diagnose and repair.  From a reliability perspective, improved weather forecasting, in many cases, can facilitate better planning for when to bring the resources online. This will smooth the transition on and off the grid when the resource is available. The end solution is going to require advancements in battery storage, intelligent switches, improved analytical capabilities, a higher redundancy of power production with peaking generation from natural gas, and certainly other innovations that aren’t even commercially viable yet.

Consumers are increasingly concerned about energy efficiency, and energy companies have the ability to propose appropriate tariffs according to consumers’ consumption profile. How will this affect the relationship between companies and customers?
Alyssa: We’ve been hearing the term “trusted energy provider.” I think this is a goal for many utilities. It’s a win-win scenario for the both the utility and the end consumer, where emissions targets are achieved and energy costs do not increase as a percentage of total household spending. To achieve this “trust,” customers may need to share more data with their power provider. And utilities must share long-term plans with their customers.

Want to learn more about smart metering and big data in the energy sector? Check out this white paper: Smart Metering: Big Data and the Value of Analytics.

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The Internet of Things at your local library

Will the Internet of Things (IoT) create a web of connected devices that make our lives better or an infinite infestation of annoying devices invading our privacy for no good reason? I don't know.

I do know that the answer is going to depend less on the technology and more on the people involved in creating a connected world. We could ask the people who will decide all of this but most of them are busy with their 5th grade homework.

Of course, they are not all that young. Many are already in middle school, high school, or college, but they are the ones who will shape the IoT. The Maker Generation. A group of do-it-yourself self-starters who don’t have any respect for the professional and academic boundaries between hardware and software or art and science. They will decide what the IoT will be. If you don't believe me, ask Mark Zuckerberg.

The question is: How can we educate this next generation to use the tools and technologies they will need to make a positive impact? And not just some of them, but all of them. If we are going to preserve the chance that the IoT will be “of the people, by the people, and for the people,” we are going to need to be sure that we provide open access to these technologies to all of them, every student, every learner.

In the world of software, the answer is simple: The Internet. We can get SAS in the hands of any student in 5 minutes or less at no cost to any student. SAS is already on-demand.  Just a handful of clicks and SAS will spin up an instantiation of SAS Studio for any student or lifelong learner. 

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Sustainability: Where’s it heading?

From the public debate over Amazon’s workplace culture to the Security and Exchange Commission’s approval of the CEO-to-worker rule, it’s been an interesting few weeks for those of us who care about sustainable organizations. Clearly people have strong feelings about what companies owe society and how front-line workers should be treated.

When the SEC rule finally goes forward, publicly traded companies will have to disclose what their CEO makes relative to the median compensation of their workers. It seems like something any company could easily figure out on a spreadsheet, but it actually took five long years for this measure to see the light of day. While it won’t take effect until 2017, the rule is already causing quite a stir in the corporate world. Some think it will bring real change, while others find it useless or even potentially harmful.

As for me, I feel hopeful. Count me among those who are shocked by the fact that as of 2013, US CEOs were making 300 times more than typical workers (up from 20 times as much in the 1960s). The divide is growing, and that’s a problem for society. When gaps are big, people tend to fall through.

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How can we make SAS blogs better? Take the survey

old blog home page

Remember when the SAS blogs looked like this?

How long have you been reading SAS blogs? And do you have thoughts on how we can improve them?

In 2007, we launched the SAS blog program with just one blog and a handful of bloggers. Today, we have more than 30 blogs and hundreds of active bloggers. As we continue to grow, we want to make sure it's still easy for you to find the content you need to become better SAS users and analytical leaders.

That's why we're asking for your input in this survey about the SAS blog program. It should take about 10 minutes to complete, and it gives you an opportunity to tell us what you want to see and do on the SAS blogs.

Whether you read every post via RSS or just check in occasionally to read a few updates from your favorite blogger, we want to hear your thoughts. Think about what you've seen on other blog sites, and what you want to see more of on our blogs - and let us know.

Take the SAS blog survey now, and share it with your colleagues. Read More »

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Behind the scenes with SAS R&D

While much of what they say is over my non-technical head, one thing is clear: SAS research and development team members are clearly excited by the new capabilities they have added to the newest release of SAS Analytics.

Their pride of accomplishment is plain to see -- even by me -- in this video montage that has 14 developers and testers describing what they see as the most important features in their product portfolio. Their commitment and enthusiasm shines.

Among the products are: the SAS Forecasting client, SAS Enterprise Miner and the brand new SAS Factory Miner. New open source analytics integration with SAS has also been added to meet evolving customer requirements.

Be sure and watch to the end of the short video to see another example of how the leader in analytics exceeds expectations.

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Why should you use visual analytics?

Are you eager to explore and understand your data? Do you want to improve processes and drive more revenue? Do you want to work with data using an intuitive, easy-to-use interface? This is now possible with visual analytics, technology which combines analytics with interactive visualizations.

As published in a recent TDWI Best Practices report, Visual Analytics is one of the hottest trends in BI because it empowers your employees to analyze more data faster and more frequently -- resulting in the discovery and sharing of new insights, and who doesn’t want that!?

Are you still a bit sceptical about incorporating visual analytics into your standard reports? And how it will be governed and maintained? Let me explain how visual analytics perfectly complements your other BI initiatives when applied correctly.

Let’s start from this quadrant where we have Ad-Hoc Discovery/Prototyping (read visual analytics) and industrialized reports on one axis and governance on the other. There are two areas where you as an organization want to be active -- and two areas you want to stay away from: The upper left and lower right. You DO NOT want high governance when you want to discover data and prototype -- and you don’t want low governance on your company’s industrialized reports.


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¡Viva el tango! (and the adoption of analytics!)

Business adoption of analytics and technology is like a tango.No matter where I go in the world, I’m constantly amazed by the similarities in business challenges facing organizations around the globe.

One of the most common is getting business users to actually adopt and use the insights gained from the great work performed by analytics teams and IT organizations.

I recently had the privilege of visiting the vibrant and cosmopolitan Buenos Aries. This city embodies a wonderful mix of cultures, perhaps best seen it its famed tango.

As someone keenly interested in helping organizations transform themselves and gain maximum value from their data, my conversations with South American business leaders got me thinking about how to solve this challenge. And about the tango.

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Communications service providers: Are you ready for digital transformation?

TP0001-090The term “disruptive technologies” frightens many organizations, but to companies like Alibaba, Uber, Airbnb and Facebook, it’s the secret to their success. These four companies from unrelated industries have one major commonality: They don’t “own” anything, at least not in the traditional sense. What they provide is a digital service, and these service offerings have transformed their respective industries.

These companies exemplify disruptive technologies because they each identified a way to meet a consumer need that was outside of their industry’s traditional activities (i.e. value chain), or delivered services in a new way. For example, with Uber, instead of hailing a cab you simply punch a few keys on your mobile device and a car is waiting for you by the time you exit your building. Read More »

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Education meets big data: key steps in preparing for your SLDS

In my first blog post in the Education meets Big Data blog series, I explained the need for Statewide Longitudinal Data Systems(SLDS). In the blog post following, I shared an interview with Armistead Sapp, one of the authors of the book, "Implement, Improve and Expand Your Statewide Longitudinal Data System."

In this post, we discuss the key steps in preparing for your SLDS. If you are in a state that is just getting started, or doing major renovations to your SLDS, these steps can help you address the key concerns to set up your system for future growth, use, and success. Read More »

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What’s new with machine learning?

Finger touching network gridsMachine learning is all about automating the development process for analytical models. One way to extend the use of machine learning is to broaden your library of machine learning algorithms. Another way is to scale your machine learning process by reducing the time required to process machine learning algorithms on large and complex data.

Every day, I hear organizations asking:

  • How do we get people with the skills to apply machine learning successfully to solve business problems?
  • How do we integrate analytics into our business processes, helping us get data-driven answers from these models to decision makers at the right time?
  • How do we create an analytics culture where all decisions are based on the analysis of data?
  • How can our analytical talent collaborate more efficiently?

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