Customers for life

Do you ever stop and think about why it’s so hard to get really great customer service? As consumers, most of us are making transactions all day long, but it’s rare that someone actually surprises or delights us. More often, we rack up negative experiences. Think of what it’s like to fly a US airline or wait for your telco company to come fix your cable, and you’ll be channeling the kind of pain I mean.

It’s funny that customer alienation happens so often, given that most organizations are sincerely trying to build a good brand and want nothing more than to create customers who stay for life. So why is there a disconnect? Where do they go astray?

I’d argue that’s because most organizations lack sufficient knowledge. To wow someone, you have to know them. And knowing customers is hard. Understanding what drives them is even harder.

Fortunately we have analytics, the sure-fire way for organizations to uncover insights that make for happy customers. If you want to turn your customer data to business advantage, there’s no better way than applying analytics to make data-based decisions. But building an analytics culture doesn’t just happen naturally. It takes time, persuasion and investment.

How exactly to get there is the subject of my upcoming talk at EMEA Analytics 2015 in Rome. You’ll have to wait for my presentation to hear my complete six-step checklist for building an analytics culture, but I can share one key point with you now, and that is that you have to make a distinction between analyzing your customers and engaging your customers.


Let me give you an example. Say you want to work on customer retention. You build a model to tell you which people are most at risk of leaving. You generate a list of those at risk and send it over to marketing. And then marketing targets those customers with an offer to retain them. That’s analyzing your customers. Engaging your customers involves a different approach.

Engagement means taking a step back and saying, “I’ve built this model and it tells me who’s at risk. But I want to know more. Why do we even have customers at risk in the first place? What are the underlying causes that are putting them there? And instead of accepting their departure as inevitable, what could we do differently to eliminate the things that make them want to leave?”

Under this approach, at the same time you send out an offer to the customers at risk of leaving, you also take a step back and consider how to improve your CRM process. Because looking at that bigger picture is the only way you’re going to delight the customer. And that’s the kind of mental shift you have to make if you want to create customers for life.

But that’s just the beginning of the discussion. I can’t wait to see you in Rome to describe more.

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Doing good with data and analytics

You don’t have to look any further than your smartphone to know you can use data and analytics for some pretty frivolous things. Consider something as simple as your socks. The Sock Sorter app uses sensors in your socks to help you find matches for every sock. Likewise, helps identify when your black socks are faded and ready to replace. Then, of course, you can sign up for a sockscription to have new replacement socks delivered every three months.

Child and grandfather holding handsOn the other end of the spectrum are the humanitarian uses of data that prove how much good analytics can do in the world. Socks are important, but I’d much rather hear about analytics helping provide shelter and water for earthquake victims.

At SAS, we get to hear stories of data doing good almost every day. I’ve told you about some of these heartwarming stories before – from the disaster recovery efforts mentioned above to the data-sharing projects that could help cure cancer.

For every one of these stories, there’s a technology angle and there’s the people angle. The people who are helped, and the people who are helping. The stories of these people are always interesting to me, and recently I’ve read three stories with a common theme: how can analytics be used to help assist society’s most vulnerable populations – and make sure that help is going where it is needed most? The three examples:

  • Foster youth in New York City are receiving the services they need to become functioning adults, partly due to a study showing that support programs for young adults can reduce homelessness and jail time.
  • Children in abusive homes in Florida are becoming easier to identify and assist, thanks to new research and assessment tools being used by the Department of Children and Families to reduce childhood deaths.
  • Young adults on public assistance are getting special attention in New Zealand, where research shows that investing in support at a young age can reduce the occurrence of lifelong welfare dependence.

From Florida to New Zealand, these programs show how analytics can make a difference in the world – and in the lives of many young people. But these are just three examples. Open data initiatives and data sharing platforms are making it easier than ever before to get involved with do-good analytics projects.

If you’re an analyst or data scientist with an interest in using analytics to improve the quality of our lives, consider volunteering your skills for organizations like DataKind and DataLook. Or seek out humanitarian projects on sites like Kaggle.

You can also follow the Twitter hashtags #dataforgood to find opportunities for citizen data scientists.

Many of you are using analytics at work to prevent fraud, assess risk or improve marketing programs. These are worthy efforts, but if you want to do more, there are many options available for giving back.

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Creating customer experience, one contact at a time

FBlog3I’m still old school enough to enjoy reading the local News & Observer on weekends. I even like flipping through pages that leave my fingers smudged from newsprint. I was reading an editorial piece a while back, and a phrase leapt out at me. A correspondent wrote, “We are in the next phase of consumer demand called the experience economy.”

The experience economy. I’ve heard the term before, but this time something clicked. In my job, I get to do some pretty cool things, like think about ways we can make our customers happier. Or how to get better at how we support them. That’s why the words “experience economy” caught my attention. Because you can have an outstanding product and efficient service, but to really make customers happy, to give them a great experience, it takes people. People either make or break the experience.

A friend of mine flew on Southwest Airlines recently. She told me about waiting in long check-in lines where the mood was almost jovial. People chatted with each other and were basically good sports about the whole waiting thing. She noticed that the five or so Southwest employees set the upbeat tone, doing their best to make it a pleasant experience, talking with the travelers and connecting with them in small but human ways.

Across the room, a rival airline had the same long lines, but the employees looked miserable, frustrated and harried, and so did the line of passengers. Through that experience, her respect for Southwest went up a few notches. That’s the difference people make. It’s not just about eliminating lines, although that would be nice too. It’s about making the experience okay despite the lines.

Impact of one

Here’s the thing: We’re connected to each other more today than we’ve ever been. Our actions influence, impact and even change the experience of those we come in contact with. The attitude of those five airline employees mattered. How they treated Southwest customers that day influenced and maybe even changed their customers’ perception of the entire company.

So, if I’m running a company, an important question I have to ask is, what is the frame of mind of my employees? When someone answers a phone call, sends an email or ships a product, what do their actions say about the company? It’s on me to make sure the workplace supports employees and gives them the leeway to delight my customers. Otherwise, they will pass their frustration through to customers. In the experience economy, that’s bad business.

As employees, we need to remember that we are a physical representation of the company. We might as well wear a T-shirt every day proclaiming, “I am Company X.” The daily contact we have with a customer, no matter how small, could be pivotal. One action by one person could be the very thing that causes a customer to think that the company is either great to work with or one to avoid.

I’m still smarting from an encounter I had with one person. I’m a “diamond member” at a hotel chain that shall go unnamed, racking up 50 to 60 stays a year. I had a stay booked, but my travel plans changed on the day of my arrival. When I called to ask about cancelling, the hotel reservationist read me the policy: “No cancellations within 24 hours.” It didn’t matter how I pleaded my case or that I had 50 stays on record. And that diamond status? Worthless. I gave the employee every chance, even asked if a supervisor or manager might be able to help. The unapologetic answer was: no cancellations, no exceptions. Period.

I paid the hotel bill for that night. But that one employee lost the hotel chain the 50 stays I would have booked in the future – and my referrals.

Golden rule

The scary thing is, you might have that kind of impact and not even know it. Most companies have several streams of business going on at all times, and those streams touch customers. The unreasonable customer you are dealing with today in contracts might be the ideal customer that product development needs for a beta project tomorrow. It’s the old golden rule, to treat a person the way you would like to be treated. Try to do right by the customer, and you’ll never be wrong.

It’s a mindset. Our customers are human beings, and we need to keep them at the center of what we do. After all, they’re buying more than our products. They can buy similar products from other vendors in many cases. Today, they are buying the whole experience. The good news is that technology plus people is a winning combination for creating an experience to be proud of. If you want to find out more about how technology can help make the right connections between people, we’ve got customer experience management info about that, too.

Thank you

It only seems fitting to wrap this up by expressing my gratitude to and for SAS’ customers. The Temkin Group 2015 report is out, and our customers gave SAS top marks for customer experience, scores I don’t take lightly.

To SAS customers: Thank you for your confidence in us. You have my commitment that we will do all we can to continue to earn your trust and to give you the experience you want and deserve.

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Beyond “back-to-school”: Giving everyone the Power to Know® through education

“Back-to-school” is a common theme this time of year, but learning isn’t something that is relegated to a certain point on the calendar or even a particular point in life – it’s a lifelong journey.

Whether you are in early education using mobile technology for learning, a student or adult learner looking for free SAS® software, or an educator looking for new ways to teach, SAS has something to offer. We support education because it is an investment in the future, not just for our company, but for the world.

Learn how SAS can help you on your lifelong education journey (click image)

Learn how SAS can help you on your lifelong education journey (click image)

The Internet of Things and the continued growth of big data will create millions of jobs for data scientists in the coming years. But before that data scientist comes knocking at our door – or the doors of our customers – he or she will have had to take college-level courses in computer science and/or analytics.

Before even getting into college, though, kids need to be comfortable and familiar with subjects all throughout K-12. We can and we must support learning at all levels.

There are many SAS education programs and initiatives making a difference in K-12 and higher education. More than 26,000 teachers and students signed up in August to use SAS Curriculum Pathways free digital learning resources, bringing the total number of users to more than 550,000 around the world. More than 350,000 professors, students and independent learners are taking advantage of the free SAS software and training  offered through SAS Analytics U. I encourage you to check those out and think about how they can help you and/or your children along their journey.

The accompanying infographic shows how those programs and others map to a lifelong commitment to learning.

I find it particularly rewarding to bring students and teachers to SAS to learn about how we can support their interests and goals.

Last week, I met with three budding data scientists, ages 10-11, who used analytics to learn more about their passions. Two focused on sports, one on pet adoption. We arranged a special day where they presented their research, met with a sports analytics expert as well as experts who analyze data on service dog breeding and endangered species preservation.


Kids can get excited about data and analytics if we can help them understand the relevance to their lives. This can put them on a path to rewarding careers in analytics or other STEM disciplines.

SAS hosts other events such as the annual Math Summit for teachers, various STEM days for students and numerous professor trainings where we help them integrate SAS into their instruction.

Next week, we will co-host a Data & Analytics Summit with Achieving the Dream, where more than 160 community and technical college representatives will learn how data and analytics can improve enrollment, course availability and student outcomes. Those schools are critical to building a talented workforce with the skills employers need.

From pre-K to the workforce and beyond, we should never stop learning. What’s the next step in your journey?

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Why you need thick data and thin data

137957211I’ve been running across the term “thick data” lately and even came across a definition earlier this week from Word Spy, an online glossary that highlights new pop culture terms before they’re cool.

So, what is thick data and why does it matter? It’s what we’ve traditionally thought of as qualitative data, and it could become even more important in the future as a balancing point to the thin data at the edges of the Internet of Things. Why? Because thick data can provide deeper meaning – the context, if you will.

After all, you can’t learn everything from 1s and 0s. Quantitative data can help you a lot, but how do you incorporate the softer stuff too? The human stuff that holds the meaning behind the numbers – answering questions such as why the numbers are what they are, and all the other stuff that’s not obvious from hard numbers alone.

A more practical example: Mobile marketing

Now that you have an understanding of thick data, why should you care about it for your business? Let’s look at mobile marketing. What I so often see as a consumer are brands targeting me with things that are absolutely ridiculous.

I can understand how they’re making assumptions about me based on the limited data they have access to, but it doesn’t make sense if you really know me. There are all kinds of possibilities when looking at that limited scope of data, but I might fall out on the wrong side of a decision tree, and rot there on the ground.

How can brands advance traditional data mining and statistical analysis to improve some of these digital promotions? Maybe you look at marrying structured data with unstructured data, including both qualitative and quantitative insights – creating a soup of both thick and thin data, thus using different types of data to improve the way you practice your art.

Thin data might be a burst of information with limited context. It’s not all the information that may affect a given scenario, like what offer might be relevant to me when I’m in my local grocery store late at night on a weekday. It may just be one piece of the equation. Look at iBeacon data streams, for example. This geo-targeted data knows when consumers are close by, or inside, a specific location. It’s proximity-based data and it’s valuable, but there isn’t a lot of marketing insight that can be derived just from that dimension of data, except that you’re there. Or, your device was there. We have to combine that information with something else that’s more robust to make smarter use of the data.

Thin data is valuable to collect and explore in large quantities, but the danger in using thin data alone comes with making direct offers without context. As marketers, let’s not blow it. Let’s recognize that, while all this stuff is coming at us, we still have to be good stewards of the data.

Make sure you know something about who you’re talking to before you send a message to the consumer. Learn to listen to all the data before you speak or initiate a conversation. If initially you come out with an offer just because you have a device that’s within a geo-fenced area, that communication might come back to haunt you. And, naturally, make sure you’re not invading privacy or misusing the data you have.

Data streams generated in the Internet of Things are giving us access to more and more data every day, but our thick data from posted videos, photos, notifications and conversations is growing too. How can we use both the thick and the thin to benefit the consumer and the brand?  If you can get that right, your opportunity to innovate and approach your marketing campaigns differently will be huge.

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3Q 2015 Intelligence Quarterly: How to lead the digital transformation

IQ_3Q_2015_cover_internationalThere it is, staring us in the face: the answer that will forever transform banking as we know it and propel the industry into the digital age. 

As a leader of the bank, you now have two options:

  1. The first is to put big data into the liability or even the risk bucket, and fight it with primitive tools such as costly, rigid and complex core banking and RDBMS systems.
  2. The second is to embrace big data and come out in front of the regulatory-driven compliance tsunami.

Embracing big data will empower the front office staff with the controls they need to make decisions at the point of the transaction and, at the same time, eradicate the complexity fueled by silo-based point solutions.

What is your choice?

To date, 90 percent of your colleagues are choosing option one and only dipping their toes into option two out of plain fear. The latest issue of Intelligence Quarterly focuses on the 10 percent who are boldly embracing option two.

How are they doing it? Many are taking a factory approach to big data and analytics, constantly trying out new ideas in a big data lab, and then taking what works and repeating it with precision. In particular, the factory approach processes information and turns the raw material (data) into something useful.

More often than not, banking executives will point out that they have big data projects started, and they are working to cut through the complexity. With very few exceptions, however, they struggle to embrace the digital age and attempt to solve growing data-intensive problems with yesterday’s tools and approaches.

When it comes to big data, who is in charge?

Any given person, department or function alone cannot change the bank. To really change the bank, it will take the efforts of IT, risk, retail and other departments all working together.

IT does automation and infrastructure, not optimization, nor does it industrialize the model management. It’s not unusual to find dozens of custom-built solutions with millions of incompatible rules and hundreds of copies of the data floating around in the bank. Combine the short-term focus with these silo-based point solutions, and we have a picture of true complexity.

Find out how banks are moving away from this level of complexity and shifting to a strategic state. Part of this process involves moving away from vendor consolidation and asking vendors to take on more by providing software as a service or even as an appliance. Instead of asking the vendors that contributed to the problems in the first place to do more of the same, banking leaders are asking: What will it take for you to replicate results to larger parts of our increasingly complex value chain?

The way that banks look at IT is changing. Indeed, a major banker I respect recently asked, ”If I spend $300 million annually on AML alone, is it not reasonable to think that I should be receiving AML as an appliance?”

How can the risk department help lead the change? Throwing manpower at the problem will not break the growing wave of regulatory-driven compliance. Instead, we must let go of our investigative warehouses and the idea of offshore back offices. For an example, read how the chief model risk officer of Discover Financial Services embraced an analytical factory concept to cut through CCAR requirements with little effort. Others banks are using the analytical factory approach to empower front office staff to take ownership and responsibility of decisions at the point of the transaction.

Most heads of retail banking also are automating their customer interactions instead of optimizing the customer experience across touch points and channels. Why embrace hyperfragmentation through rigid, channel-based structures and systems when you could drive consistency across channels? Instead, you could use analytics to calculate risk-adjusted performance per client while creating personalized experiences and taking the costs out of the system.

Cut through the complexity

The best way to improve client performance while personalizing services to each client is to embrace the technology required to cut through the complexity. Unlike the rules-based technology of the past, ownership of the analytical factory calls for a new set of skills, be it social, demographic, economic or any other faculty conceivable. For example, SAS is working with one global, systemically important bank (G-SIB) that hired a team of astronauts to help find the extreme outliers in financial investigation data.

My favorite case study is about a UK bank that empowered the front office staff to make credit decisions. It asked, why not equip the customer-facing banker to make the credit decision that can be better for both the client and the bank? It makes sense, especially compared to the alternative of reducing the time it takes the risk department to process an internal request.

Why, then, are other banks not copying this approach? My guess: They applied the wrong skills to the job. The answer to too many databases isn’t to create another and call it an enterprise warehouse. The answer is a factory approach, as one insurance firm discovered when it separated risk models from the data, through the creation of a model factory. What do you know about your customer? Probably much less than this major insurer, since they now model customer behavior and analyze client perceptions to improve the customer experience and measure risk-weighted performance at a reduced cost.

Who should lead the digital transformation? It touches all aspects of the bank and needs a true champion at the top. The CEO should personally lead the way and change the bank — because the future of the bank is at stake.

And the best way for the CEO to lead the bank into the digital age is with a factory approach that automates, scales and manages data to support collaboration between departments and streamline analytics projects throughout the bank.

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Cybersecurity and the doomsday case for analytics

cybersecurity_imageTechnology 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 everything is connected, everything can be attacked. I’m talking about the unique brand of mayhem caused by the really bad guys – the kind of people who want to bring down a stock exchange, tamper with nuclear weapons or spoil a city’s water supply. If it sounds like the plot of a James Bond movie, it should, because cybercriminals can and do create chaos on a cinematic level.

Whenever I hear these stories, I have mixed emotions. As a citizen concerned with protecting everything I hold dear, I share the very same concerns that you do. But I also have a front-seat view of developments in the field of cybersecurity, so I understand the amazing power of analytics to address what is surely one of the most complicated computer science challenges of our times. And that makes me optimistic.

Cybersecurity to the rescue

The way that we will thwart the evil masterminds is analytics. We’ll fight technology with technology.

The key to winning is prevention. The nature of the cyberthreat means it’s no longer enough to reinforce the perimeter. Stopping data breaches means assuming that criminals are already inside. In this new reality, analytics serve not as a barricade to keep criminals out, but an alarm that sounds when the virus they’ve implanted awakes.

Today the combination of event stream processing, Hadoop, in-memory analytics and visual analytics make it possible to react in near real time, helping you spot the bad guys and foil their attempts. SAS has recently unveiled a cybersecurity solution to do just that, which will be available this fall. It works by searching hundreds of thousands of records per second and billions per day to spot the inevitable threats.

Plenty of companies try to build fences to keep people out, but we don’t do that. As soon as the virus wakes up and begins to do its thing, SAS will find it immediately and alert security teams – before the doomsday scenario plays out. The thing about villains is, they never seem to rest. They’re wily, they’re malicious, and they’re going to keep coming at us. The stakes are very high.

When it comes to foiling intricate plots, we’re going to need serious brainpower and cutting-edge analytics. That’s why I’m so passionate about bringing SAS’ expertise in cybersecurity to customers around the world.

Learn more

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Disaster relief efforts show promise of analytics and seemingly unrelated data sources

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 temporary shelter and helps coordinate the efforts of other relief agencies that provide food, clean water, medical care and security.

IOM is currently assisting thousands of victims in the earthquake-ravaged areas of Nepal. SAS is helping IOM analyze shelter data to help better allocate resources, based on the work we did with them following Typhoon Haiyan in the Philippines.

Using SAS Visual Analytics, IOM can see where the high-risk shelters are, based on factors such as:

  • A dangerous mix of overcrowding, unsafe drinking water and solid waste disposal problems.
  • High numbers of families still living in makeshift shelters.
  • Rapid growth of certain vulnerable populations in a short amount of time.
  • Higher concentrations of diarrhea, fever and skin disease among older people.

As new data comes in, new insights are revealed. As you would expect, Kathmandu is the focus of the bulk of relief efforts. However, after visualizing data on young children, it was revealed that a nearby district had more small children, ages 1-5, and in particular, five times the number of infant girls as Kathmandu. This smaller district had a larger need for diapers, formula, children’s medicine and other supplies for nursing moms. These were quick, but important, insights to guide relief efforts.

Concentration of females under age 1 at IOM evacuation centers (click to enlarge)

Concentration of females under age 1at IOM evacuation centers (click to enlarge)

How can global trade data inform disaster relief?

There’s another side to the Nepal data story, though. In April, SAS announced the launch of SAS Visual Analytics for UN Comtrade, which made 27 years of international trade data available using data visualization software. How is this helping with the Nepal earthquake response?

IOM is building temporary shelters for displaced people in Nepal and needed to understand where/how to quickly procure sheet metal roofing (CGI) before monsoon season.  People are sleeping out in the open due to the fear that more aftershocks will bring buildings down on them, so protection during monsoon season is a big concern.

Using UN Comtrade, we were able to show IOM a graphic of the top exporters of CGI. Some of the findings include:

  • Neighboring India is the world’s largest producer of CGI roofing sheets that are wider than 24 inches, but India rarely sells it to Nepal.
  • Nepal is actually the world’s 7th largest producer so historically there’s good capacity for CGI fabrication in Nepal. Consequently, some of the supply can be sourced locally.
  • There are other potential sellers in the region like China (2nd), Thailand (8th) and Vietnam (9th).

    Top exporters of CGI roofing sheets, via SAS Visual Analytics for UN Comtrade (click to enlarge)

    Top exporters of CGI roofing sheets, via SAS Visual Analytics for UN Comtrade (click to enlarge)

Brian Kelly, who is leading the Nepal response for IOM, shared his thoughts. “Shelter is so important to helping the 63,000 displaced families create a level of stability and protection, especially with monsoon season upon us. With the UN Comtrade information, we were able to secure materials more quickly and, literally, put roofs over peoples’ heads.”

A new era of data-for-good

These projects just scratch the surface of what’s possible when new data, and those that know how to use it, are applied to humanitarian needs. Organizations such as DataKind and INFORMS, through its new Pro Bono Analytics program, are rallying data scientists to lend their time and expertise to helping people around the world. And there are many more data sets out there that could help with relief and other humanitarian efforts.

It’s an exciting time to be in the world of big data and analytics. We’re just beginning to understand how technology can tackle society’s “grand challenges.” Please share your ideas on what unlikely data sources might help with disaster relief. And, how can we bring the world’s analytics talent to bear on these challenges?


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Bringing Hadoop into the mainstream

Jim Goodnight, Mike Olson, Herb Cunitz and Jim Davis discuss Hadoop.

Jim Goodnight, Mike Olson, Herb Cunitz and Jim Davis.

Remember when the morning talk show hosts started talking about Twitter? That was weird at first. But now, even your small, home-town news stations have a Twitter handle, and so does your boss, most likely.

“Big data” took a similar route into the mainstream vernacular. At first, we heard pundits saying that only the banks had big data. Or only big government needed to worry about big data. But then, before we knew it, 60 Minutes and The Atlantic were running regular features discussing big data.

I’m not sure if Hadoop will ever hit that level of mainstream attention, but it has become an everyday topic with the leaders I talk to at conferences and customer events.  And the Hadoop naysayers are getting harder to find.

Why is that? Four reasons:

  1. Organizations are seeing Hadoop as more than just a dumping ground for their data. They’re approaching it with strategic business problems and learning how to treat it as an analytics platform.
  2. The early adopters who took the risks with the platform are seeing real results, and now everyone else is realizing it’s time to catch up.
  3. Today’s data volumes make it impossible to ignore Hadoop. We talked about this when discussing the Internet of Things, which is an undeniably huge growing source of big data.
  4. Hadoop is becoming enterprise hardened and easier to implement and maintain. Vendors like Cloudera and Hortonworks are developing ecosystems around Hadoop that improve its stability and offer layers of governance and security that make it a viable option for even the most conservative companies.

Recently at the SAS Global Forum Executive Conference I discussed some of these topics on a panel with SAS CEO Jim Goodnight, Cloudera co-founder Mike Olson and Hortonworks president Herb Cunitz.

Jim was the first to say he's seeing more customers use Hadoop for analytics, and the other panelists agreed, mentioning Hadoop use cases from MasterCard and the Financial Industry Regulatory Authority. Herb and Mike both talked about how the technology that started out in many IT shops is now catching the attention of business leaders too.

Watch the video below to hear us discuss the growing use of Hadoop in the cloud, and learn one thing that Jim says is stupid to do with Hadoop (hint: it involves a straw). Fair warning, if you stop watching too early, you won’t hear why boards of directors are suddenly paying attention to Hadoop now too.

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US Senate takes up fight against patent trolls

med500004 (1)With the recent introduction of the Protecting American Talent and Entrepreneurship (PATENT Act), the US Senate set aside partisan politics to take on a problem that plagues all industries, but especially high-tech.

In front of Congress, in the media and in a previous blog post, I have decried the current patent litigation landscape in the US. Simply put, patent trolls produce nothing, employ practically no one, and yet they threaten US innovation and economic growth.

The number of patent lawsuits is at historic levels, and promising to increase again in 2015. According to United for Patent Reform, an organization of like-minded companies and trade association of which SAS is a member, “The number of patent lawsuits filed in the first quarter of 2015 was up 30% over the number filed in the fourth quarter of 2014. The percentage of those suits filed by patent trolls was also higher in this quarter than in the last (62% vs. 57%).”

Having been the target of such wasteful and frivolous suits, SAS is on the front lines of the battle both in the courts, and in the legislatures seeking a solution to this legalized extortion. I applaud the senators who have taken up this fight.

The legislation, S. 1137, is sponsored by Senate Judiciary Committee Chairman Chuck Grassley (R-IA), Ranking Member Patrick Leahy (D-VT), Senate Majority Whip John Cornyn (R-TX), Sen. Chuck Schumer (D-NY), Sen. Orrin Hatch (R-UT), Sen. Amy Klobuchar (D-MN) and Sen. Mike Lee (R-UT).

The introduction of this legislation demonstrates their leadership in the complex and critical area of patent reform. The bill attempts to protect defendants and consumers from frivolous and damaging lawsuits by clarifying the litigation process, increasing transparency and adding more risk for the plaintiffs, while recognizing concerns raised by other patent stakeholders. The proposed changes will make great strides in protecting American job creators from patent trolls while reaffirming America’s commitment to innovation, entrepreneurship and consumer welfare.

Now that there is meaningful legislation pending in both the Senate and the House of Representatives, I encourage Congress to act quickly in moving the legislation forward to bring common sense back to patent litigation.

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