Q3 2014 Intelligence Quarterly: Accelerate innovation with big data analytics

IQ_3Q_2014_cover_internationalSince I started publishing this journal of case studies several years ago, I’ve always written about the transformative impact of big data analytics. I’ve been sharing best practices and industry insights, but I’m now often asked, how? How can organizations transform for tomorrow while still focusing on success today? How can employees stop wasting time on the old legacy systems and move to current and future states without running the risk of disruptions?

In this issue of Intelligence Quarterly, I’ve set out to answer that question – showing you how to leverage big data analytics to come out ahead, no matter what your legacy is.

With big data comes big challenges and even bigger opportunities. Social data, mobile data and sensor data are just a few of the newer data sources that – when properly analyzed – can help improve your business in many ways. Yet many organizations find themselves entangled with dysfunctional legacy systems that get in the way of innovation.

It’s true that technology is the foundation for and the driver of innovation, but just like weeds can choke out new grass, outdated technology can stifle innovation. When you develop a culture that adopts newer skills and technologies, your ability to innovate increases. Then those innovations generate even more skills and technologies. The wheel keeps turning. And advancements keep happening.

When analytics skills and technologies are being applied, we see changes progressing in four steps:

  1. Modernizing with analytics gives you the ability to trust the numbers and rely on a single version of the truth. That’s what Italian credit agency Agos needed when it acquired another big lender, and what Wyndham Exchange & Rentals needed for its vacation ownership exchange, where the ability to create models based on information you already have is crucial.
  2. Integration of analytics into business processes can help spread analytics throughout the organization. The democratization of analytics happens through data visualization and automation. For example, Taipei Medical University uses visual analytics to dig deeper into daily revenue and expense trends. Visualization is widely considered to be a top technique for gaining the most value from big data. To close the gap between analytics and business users, visualize the data. Professor Bart Baesens explains more.
  3. Innovation, as we discussed above, is both a product and an enabler of technology. An important function of technology is that it gives us the ability to do things that previously were not possible. I have seen many mature analytics implementations in the telecom industry over the last few months. Processing speeds have gone from hours to seconds, while total cost of ownership has been reduced by millions of dollars. One CEO told me his company’s earnings increased 30 percent as a result of running 1,000 micro-campaigns created from 5,000 models daily! He promised to share his story with us in an upcoming issue.
  4. Transformation takes place when organizations have mastered modernization, integration and innovation. Businesses in this phase have reached a point where both integration and innovation occur in tandem. Two decades ago, online advertising was just an idea. Today, it is a fast-growing, rapidly developing industry thanks to the ability technology has given us to turn data into insight and new revenue streams through software as a service.

How can you modernize your organization? Consider how to leverage newer technologies like grid, high-performance analytics and Hadoop. Delve into your existing processes and see where analytics can be integrated seamlessly to increase value in your organization. Consider how you can use analytics to innovate in your industry with new products and services. Finally, use analytics to transform your company to create a new market that never existed before.

The time to modernize is now. Those who don’t risk being left behind in our increasingly dynamic and interconnected world. As you will see in the following pages, our challenges are growing more complex, but the potential for data to provide knowledge and insight to tackle these challenges is growing just as quickly – if not faster.

Unlike other technology-driven innovations, modernization does not start with growing your IT budgets. Most of our successful customers report substantial total cost savings from the very beginning. Modernization is about smaller, smarter, more strategic engagements that yield immediate value and provide a path to accelerate innovation.

With analytics entering a new, more powerful era, modernization is now at the forefront. Exploit the countless, value-laden opportunities for accelerating innovation using big data analytics. Consider how you can help your organization improve performance, transform your business and turn big data into big dollars. At the same time, you’ll create a better experience for customers and clients and a great place to work for your employees, one that stands out in every way.

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4 tips for modern CIOs

My first tip is a bonus: If you’re going to participate in a corporate Webcast series, find someone as honest and engaging as James Dallas to film it with you. Dallas, the former CIO of Medtronic, is not only interesting. He also knows a thing or two about implementing advanced analytics projects.

In a series of four brief webcasts, Dallas and I discuss the value of analytics, and how to overcome four myths surrounding IT’s role with data and analytics projects.  Those myths are:

  • Myth 1: IT controls all data.
  • Myth 2: Technology poses the biggest challenge.
  • Myth 3: Everyone understands the value of analytics.
  • Myth 4: You can’t have analytics without IT.

The advice in the webcasts is wide ranging, so I've pulled together 4 tips here to give you a taste of the topics you’ll hear us discuss during the webcast:

For Myth #1: Embrace the messiness. Data is messy by its very nature. The enterprise data warehouse put structure around a lot of messy data, but now, along comes Hadoop without any structure at all: You just flatten your data out and dump it all in. The cultural change is letting go of the structure and accepting that part of this is going to be messy. Anybody who wants to innovate has to deal with messiness, blind alleys and failures.

For Myth #2: Think of Hadoop as an enabler. Too many IT shops are viewing Hadoop as another data warehousing option at a lower cost. As a result, every Hadoop vendor is putting a SQL front end on Hadoop. Why? Just so we can have another EDW at a lower cost? If you’re just treating it as a cheaper EDW, you’re missing the point. Hadoop data structures look very similar to SAS data sets, so they are already optimized for analytics. With the right combination of Hadoop and SAS, you’re making an investment in big analytics to answer questions faster.

For Myth #3: Ask better questions. As an industry many of the developers we hire in IT come from  Computer Science programs that train students mostly about systems like databases, workflow engines, and web servers.  A great programmer is likely to approach a business process problem like Einstein did, as a thought experiment. An example is asking a programmer to optimize a supply chain. They can write a simulation using lifo and fifo queues but may not know the opportunity to apply optimization algorithms.  It wasn’t until later in his career that Einstein realized the value of math and analytics in helping him answer some of the most vexing questions. IT should have those same skills. We need to train IT staff about the basics of machine learning, forecasting, and optimization if we want to help the business answer their most vexing questions about customer churn, portfolio risk or supply chain logistics.

For Myth #4: Balance the portfolio of what you do as CIO. Are you spending as much energy getting information out to users as you are getting information in? Companies invest millions in ERP systems, business payroll software and healthcare benefits technologies – to name a few. Are you balancing the work that goes into those systems with the data that can come out of them? What none of these systems solve is what SAS has solved all along: How do you get all that data, bring it back together and analyze it to get answers?

I encourage you to watch the analytics myths webcast series for more. Each segment is 10-15 minutes, and they also include clips of interviews with IT leaders in various industries.

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Developing tomorrow’s analytic talent

I speak with executives in every industry – in companies big and small – and they all have the same challenge: They’re overwhelmed with data. The problem? There’s a huge gap between the amount of data they possess and the number of employees who can analyze it. This lack of people power is causing companies to miss out on critical opportunities that are hidden in all that data.

Easy fix, right? Just hire more data experts. But those same executives tell me they have a hard time finding and retaining that valuable talent. My answer is, look to our universities.

Hire more college graduates

Do you hire for experience or potential? There are arguments for both, but I see future dividends in developing the analytical minds of the millennial generation. This effort must begin well before graduation day, and I’m excited to be part of the SAS team taking action to tackle this problem. We’re creating a talent pool to fill these jobs – and that will help SAS customers and partners succeed.

I recently attended Analytics Day at Oklahoma State University, an event I’ve attended for several years. Generating interest in technology careers is always my goal. It’s something I’m passionate about – I want every student to have the opportunity to become an analytics expert.

That’s a goal shared by all of us at SAS, and we’ve been working toward it for a long time. More than seven years ago, SAS and North Carolina State University launched the first analytics master’s degree. That was before the demand for graduates with big data skills increased dramatically. With such an upturn in the demand, we will expand our outreach.

SAS® Analytics U helps anyone become an expert

Our latest initiative to attract more talent is SAS Analytics U. SAS Analytics U is open to professors, students and academic researchers. Access to free SAS software paves the way for anyone to become an analytic expert. And a vibrant online community encourages collaboration with other SAS users. All of this support helps develop the deep analytical talent that companies are looking for.

Online courses also serve as an important instrument in creating more talent. SAS’ upcoming online and massive open online courses (MOOCs) will be open to anyone who wants to learn SAS programming, prepare for certification, increase marketability and enhance skills.

Analytics boot camp

The best and the brightest hate to be bored, so when SAS hires college graduates we put them through a rigorous, 12-week Technology Enablement Academy designed to immerse participants in core SAS technologies. Together with seasoned mentors, they immediately build a network based on relationships with two-way communication. The knowledge sharing benefits customers, employees and SAS. These new hires are quickly assigned to customer projects, solving challenges and testing the limits of their knowledge. We’ve seen these graduates have an immediate impact. They’re valued team members, not just the new kids on the block.

Between SAS Analytics U, our online courses and academic communities, we’re doing everything we can to help students get real-world technology and analytics experience. So get ready – soon you’ll be meeting the next generation of analytics leaders.

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Why business leaders can’t ignore modernization

I talked about modernization on this blog a few months ago, and I’ve continued to hear from customers about their plans to modernize their analytical platforms with new technologies for big data and advanced analytics.

With all this talk about technology, though, it’s easy to lose sight of an important point: Modernization is not just IT’s responsibility.

We’ve known for years that your analytics strategies should be driven by business needs. That’s almost a no brainer at this point, right? I don’t know anyone who’s building decision trees with dummy data and sitting around drinking lattes, waiting for a chance to use them.

The same is true with today’s modern architectures. Nobody is sitting on empty Hadoop clusters without plans to fill them. And nobody is staring at a blank visual analytics screen with hollow pie charts and vacant geographic reports.

Modernization should be about the value these technologies can provide to the business. Are you looking for visual analytics interfaces that are easier for everyone to use? Do you want to give executives up-to-the-minute results they’ve never had before? Do you want to cut fraud by looking at the influence of social networks? Maybe you’ve considered using mobile data to develop real-time marketing offers? Can you skim through streaming data to pull out the most relevant and predictive pieces for evaluating risk?

In each of these scenarios the IT component, while important, is not the most exciting part of the story. It’s the opportunity to improve the business in new, modern ways that is truly exciting.

Recent IDC research, sponsored by SAS, supports this point. When surveying analytical leaders, IDC learned that the greatest stumbling blocks to analytics success are organizational mindsets and culture, not technology.

But how can you improve the culture and mindsets in your organization? According to the same IDC study, business leaders and IT should work together to quantify the value of analytics and identify areas where modern data architectures can add value to the business.

So, if you’re still thinking of modernization as strictly IT’s responsibility, you’re missing the point. IT has a role to play and can benefit in terms of potential cost reductions, but the real benefit comes in realizing what modernization can do for the business.

I encourage you to look beyond the infrastructure piece of modernization. After all, you’re not just introducing Hadoop, in-memory capabilities and visual analytics because they’re the latest technologies. You’re doing it so that data scientists can jump start their modeling efforts, a broader user base can gain access to data, and decision makers can identify areas to build new markets or increase revenue.

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What can you build in a day?

It’s true. Rome wasn’t built in a day. But the latest app on your smart phone might have been.

How do I know? I recently spent a full day in New York City with a few dozen marketing executives who each built a smart phone app by the end of the day. My app combined location data and customer data to offer better seats to baseball fans in or near Yankee stadium.

Am I thinking about a career change? Or hoping to create the next Flappy Bird?

Not likely. But I did enjoy returning to my programming roots for a day.

The workshop was led by Decoded, a group whose goal is to introduce non-programmers to the potentials of the digital world. Their workshops remove the veil from application development and give participants a better understanding of what’s possible with just a good idea and a little bit of code.

Believe it or not, a lot of my fellow attendees had backgrounds in computer science, and they still came away with a renewed appreciation and understanding of what’s possible in application development.

Decoded Customer Testimonial

I doubt that any of the attendees will turn into software developers as a result of the course, but some of them probably did go back to their organizations with ideas that are driving the IT department crazy.

But seriously, I’m glad I did it. It was a lot of fun, and it made me wonder what kinds of ideas businesses could come up with if they held similar workshops for analytics. 

You could spend the morning learning how to access data libraries and how to use the latest visualization tools. Or spend a morning teaching some basic statistical concepts and how to apply them to business problems. Then spend the afternoon brainstorming uses for analytics in your organization.

What kinds of ideas could you unlock from your company’s data with just one day of accelerated learning? You might be surprised at the results. 

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Interesting things about the Internet of Things

You’ve probably heard about the Internet of Things.  It’s this concept that devices connected to the Internet can stream data back and forth, essentially communicating with each other to make decisions or send notifications without human intervention.

I’ve seen a lot of articles lately about how this works, including details on IP addresses and protocols for creating applications and services that allow all of our things to interact with each other via the Internet.

You might have read similar articles and thought “So what? Now my toaster talks to my car and my iron talks to my smart phone? How does that help me?”

What’s driving this trend? And why should we pay attention? As usual, the value for businesses is not just in understanding how this works, but in paying attention to why it’s happening.

The standard examples for why you should care about the Internet of Things have focused on automation, connection and service:

  • Your iron knows to turn off automatically if you’ve left home in a hurry.
  • Your car knows when it needs an oil change and reminds you to schedule regular maintenance.
  • Your air conditioner knows to cut back when energy use reaches a certain threshold for the day.

All three of these examples are basic alerts and simple reactions. They’re useful, and they’ll provide a lot of functionality, but they’re not the most exciting part of the Internet of Things. To get to the interesting stuff, let’s recall this chart I’ve used for years that shows two main categories of analytics capabilities.


Remember this? It compares reactive analytics capabilities with the more advanced, proactive applications on the right. So, let’s ask ourselves, how do we add predictive capabilities to the Internet of Things?

And this is where things start to get interesting.

Our connected devices are all sending streams of data through the network. Whether it’s a car or a pacemaker, the data is flowing in a constant stream from the device to the network, and sometimes back to the device.

It’s a lot of data. But when you can apply predictive models to that steady stream of data, the models know to look for patterns or anomalies in the data. This means they can predict things like potential auto accidents, impending heart attacks or imminent oil spills, so you can take action to prevent them from happening.

See, that’s interesting. Now, extend these examples into your world, and you can see how understanding the activities and interactions between devices might help you better understand your business processes and your customers.

Once you understand the true potential, you know not to roll your eyes the next time you hear someone talking about the Internet of things. Although, you do have my permission to squirm when you see the acronym IoT ... or maybe that's just me.

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Analytics: the prescription for health care

The US health care industry is always getting a bad rap. It takes heat for being too expensive or not efficient enough or just too complicated. We know we need it, and that living long and healthy lives requires it. But we also know we love to complain about it and offer up our own brilliant ideas about how to change it.

DoseofAnalytics (3)When I hear “health care,” though, I have different thoughts. What I’ve seen is the amount of data the health care industry both receives and generates every day, every week, every year. And I know there’s a perfect match between this industry and all that analytics can do.

It’s time to take full advantage of this data, to bring in analytics to drive social change. I’ve seen it time and again – apply high-performance analytics to any tough problem, and inefficiencies are left in the dust. Solutions become clear. The path to innovation reveals itself. Chalk it up to the power of analytics.

That’s why I’m glad we are involved. This week, the SAS teams are bringing together more than 300 thought leaders from health care, pharmaceutical, biotechnology and insurance organizations at the 11th Annual SAS Health Analytics Executive Conference on May 14. Just based on the experience of these people alone, I’m sure there will be great discussions, and hopefully we can get to the bottom of some of the issues plaguing the industry.

Leading the discussion are two luminaries from the field. Dr. Farzad Mostashari is a visiting fellow of the Engelberg Center for Health Care Reform at the Brookings Institution and the former National Coordinator for Health Information Technology. If anyone understands the issues facing health care and sees a path forward, it’s Dr. Mostashari.

John Crowley is Chairman and CEO of Amicus Therapeutics, a biotech company he formed to develop a drug that could save his children from a life-threatening disease. In 2010, his story was turned into a major motion picture, Extraordinary Measures, starring Brendan Fraser, Harrison Ford and Keri Russell. He’ll help put a personal face on topics that are too often considered only in the abstract.

Beyond our keynote speakers, we also have a strong roster of other leaders in the field, and possibly the best part of the day will be hearing real-life best practices from customers and SAS experts. The interactive technology showcase should be pretty cool, and there will even be a special "how-to" session on building an analytics-driven culture.

The event itself is invitation only, but don't feel left out. You can follow the conference proceedings via webcast on May 14 from 8 a.m-4 p.m. ET. Tune in, and see for yourself why health analytics is the right prescription for health care.

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Baskets full of benefits from analytics

111-Harry-DavidWith as much as I travel, I have to confess that I’ve become a bit of a food snob. And for good reason, I might add. Two days in a Chilean hospital will make anyone stick with what they know and trust. After that experience, it was just me and my McVitie’s for the next few days, thank you.

Maybe that’s why there’s just something so great to me about giving, or getting, a gift basket. When I’m the giver, it’s a fast and easy way to make sure my present hits the mark with custom-picked goodies. When I’m the getter, I know it’s a treat hand-chosen with my tastes in mind that keeps on giving as long as my self-control allows.

American retailer Harry & David is a known and trusted leader in gourmet gifts, including baskets, towers and more. Simply say the name and anyone within earshot is likely to start salivating as they think about pears, nuts, truffles and the “killer app” of snack foods: Moose Munch®.

But in a down economy, quality treats that pamper and please are some of the first extras to go, and that was certainly true for customers of Harry & David. The 2008 downturn led to a 2011 bankruptcy for the company as loyal customers had to tighten their gift-giving belts – but now it’s back in a big way. And the gourmet gurus did it with the help of SAS.

Harry & David was already good at meeting customer needs, as proven by its long history of market leadership. But with mismatched systems and disconnected data sets, they found they were having to work harder and harder to hear what their customers were trying to tell them. So they invested in more advanced analytics to look at the customer life cycle. And what they found was that some customers are thinking about the brand year-round, while others only want to use Harry & David for specific occasions.

As Paul Lazorisak, Vice President of Customer Marketing, notes of the latter group, “They love us at Christmas, but only at Christmas. Try to have a conversation with them about Mother’s Day or Easter and you not only waste resources, you risk driving them away.”

Very precise segmentation is the answer. SAS helped Harry & David identify the customers with the most potential and move them up the value chain. As a result, customer retention has improved 14 percent, sales per customer have risen 7 percent, and the number of loyal, high-value customers has gone up 10 percent.

Fine food is one of life’s greatest pleasures, so helping Harry & David deliver their unique brand of joy to more and more people just feels good. I love partnering with companies throughout the Americas to make a great thing even better. Hmmm, maybe I’ll take a moment to celebrate that warm, fuzzy feeling with some Moose Munch.

Read the full version of the Harry & David success story at sas.com.

Follow me on Twitter: @CarlTFarrell

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What is modernization and how will you modernize?

SAS has been around 38 years. The way that we apply analytics to business problems continues to evolve, and the hardware and software available to us has changed dramatically as well.

We are in a phase now where modernization can lower costs and improve processing speeds. Businesses that modernize their infrastructures and analytics environments can take advantage of the latest advances to process large amounts of data in a timely fashion.

But what is modernization? By definition, it’s the process of adapting to modern needs or habits, typically by installing modern equipment or adopting modern ideas or methods.* I see three important points here. Let’s break them down:

  1. Adapting to modern needs or habits. Everyone is talking about big data. Everyone is talking about fact-based decision making. Are you doing that in the most efficient way? Are you taking advantage of the most modern options available today?
  2. Installing modern equipment. Let’s look at your technology infrastructure. Do you have platforms in place to reduce time to decision? Do you have access to the right data? Are you able to handle large amounts of data?
  3. Adopting modern ideas or methods. This isn’t just about your infrastructure. It’s not just about hardware and software. There’s a big cultural change that needs to happen in the organization to be prepared to take advantage of all this. You have to commit to using analytics and putting your trust in the results.

Your modernization efforts will not take place overnight. The cultural work and technology decisions will take time, but some of these newer technologies like Hadoop and cloud computing really do make the entry paths easier and more affordable than ever before.

No matter what your infrastructure looks like now, there’s a path that makes sense for you. It might be a grid architecture. Or a fast, software-as-a-service deployment. Or a full-scale high-performance analytics installation. Consider your options and do what’s right for your business.

*Definition from Oxford dictionary

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Q2 2014 Intelligence Quarterly: Big data and the power of prediction

Intelligence Quarterly Q2 2014Business leaders have always made predictions about the future of their organizations. The difference today is that our predictions no longer have to be based on gut feel and inexact analyses of the past. With big data and predictive analytics, we have the ability to leverage collective knowledge and larger volumes of data. As a result, our predictions can be fact-based, not based on the experiences of one person.

Predictive analytics can be used in two powerful ways: for prevention or for creation. One is about stopping the undesirable from happening, and the other is about fulfilling desires.

First, let’s look at prevention. When banks can predict what leads to fraud, they can take steps to stop fraud before it happens. When public safety officials can predict what leads to crime, they can lower crime rates by curtailing the elements that lead to crime. When telcos predict the factors that lead to losing customers, they can step in to prevent churn before those factors align.

The clear advantage with prediction is that you are not merely reacting to fraud, crime or churn after the fact. You are taking action earlier to help reduce the factors that lead to fraud, crime and churn. You are preventing it from happening in the first place. I like to call this “predict to prevent.”

On the creation side, prediction can help you anticipate customer needs and fulfill those needs before demand strikes. Retailers can deliver products that customers want before they can even articulate the desire. Utility companies can anticipate spikes in energy use and produce the right amount of energy before demand increases.

More importantly, as economies shift from a product to a services focus, “predict to create” can give organizations an even bigger advantage.

Thinking back to the Industrial Revolution, consumers were suddenly able to purchase things they didn’t have before: cars, shoes, televisions and refrigerators are just a few examples. As consumer goods became produced on a mass scale, there were enough products for nearly everyone with the means to purchase them.

Now, in the digital revolution, the focus has moved from the product to the experience. Goods are still plentiful, but there’s a stronger demand for customer service and personalization. As a result, the feelings surrounding a brand can become even more important than the products. To compete in this new environment, companies are bundling products with services to create experiences, both online and off. Analyzing consumer and behavioral data has become one of the best ways to satisfy consumers, by determining not just what they want, but when they want it and how they want it – creating the complete package.

In this issue of Intelligence Quarterly, we’ve included multiple stories that illustrate how to use prediction for prevention and creation, including:

  • A hospital in Norway predicts what factors lead to patient injuries and prevent accidents and adverse reactions from occurring, resulting in huge improvements in patient safety (Page 3).
  • Public safety programs in the UK are analyzing public sources of data to predict and prevent terrorism, cybercrime and gun violence (Page 16).
  • A mobile marketing company predicts consumer preferences by analyzing location data and mobile activity, and creates relevant offers for registered users based on their preferences and whereabouts (Page 19).

With advanced analytics and the predictive capabilities of SAS, you can accomplish similar goals. Open the covers of this journal to learn how to use your data to prevent fraud, crime and churn – and to create product and service bundles just in time for demand to strike.

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