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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How to innovate faster than the fraudsters

Fraud and security intelligence is probably one of the fastest growing areas for analytics. In fact, SAS saw a 44 percent growth in the sale of fraud solutions last year. Why is that? Two reasons:

  1. If you’re looking for innovation in the marketplace, a lot of the innovation is coming from the fraudsters. Just when you think you've got them, they’re changing their methods.
  2.  The ROI for reducing fraud is tremendous. If we can reduce fraud by 10 to 20 percent in a financial institution or even a retailer, that makes a big difference to the bottom line.

The type of fraud we’re seeing – and using analytics to prevent – is not unsophisticated. Today’s fraud schemes aren’t coming from a couple of people hacking away in a garage somewhere. These are big, global businesses. So organizations have to stay ahead of the next fraud scheme, and analytics can help them do that.

Stu Bradley, SAS Senior Business Director for the Security Intelligence Practice, demonstrated one example last week at SAS Global Forum. In 2013, explained Bradley, a network of criminals stole $45 million from ATMs across 20 countries in a matter of hours. How did they do it? By hacking two credit card companies, duplicating debit cards, and programming the cards to have unlimited balances and withdrawal limits at ATMs.

Sounds sophisticated. And it is. But an equally sophisticated system for predicting and monitoring fraud outbreaks could have stopped these criminals in their tracks.

In the coming months, you’ll continue to see analytics advancements in the fight against fraud and cybercrime.  As retailers and banks start to feel the effects of customer data breaches, analytics will continue to play a role in helping to slow those things down.

Your mission should be to innovate faster than the fraudsters. To do that, you need advanced analytics.

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A new information revolution

Business Transformation book cover

Eight years ago I co-authored "Information Revolution," one of the first books to describe a comprehensive approach to managing data for decision making. My co-authors and I were a little ahead of the game. At that time, social media was in its infancy, inexpensive data storage had not quite arrived, and processing speeds were a fraction of what they are today. Most importantly, the “one version of the truth” concept was in its infancy.

In the ensuing years, data growth has exploded and processing speeds have grown exponentially. Yet data is still stuck in silos. Organizations know they need one version of the truth, but they still struggle to get there. That’s why I’m thrilled that SAS global consultant Aiman Zeid has picked up where Information Revolution left off.  “Business Transformation: A Roadmap for Maximizing Organizational Insights” is not so much the sequel to "Information Revolution," as an evolution of thinking on the ideas we first wrote about. And it comes from someone who has been helping companies around the globe get a handle on how to achieve one version of the truth.

Aiman has seen firsthand what happens when organizations focus exclusively on the technical component of data -- they don’t make much progress. The technology needed to become a data driven organization can’t work without people, processes and culture in place to accept and work with that technology. Aiman does a masterful job of explaining this.

Putting the spotlight on people and culture

I am particularly pleased that Aiman highlights the culture issue, and the role of data scientists. Employing a high-level data guru can break the bad habit of making decisions based on gut feel. A Chief Data Scientist can help integrate data driven decision making into the company’s cultural fiber. Data scientists can also bridge the communication gap that prevents an analytical culture from taking hold.

Tom Davenport, in his Harvard Business Review article “Data Scientist: The Sexiest Job of the 21st Century,” describes a data scientist this way: “It’s a high-ranking professional with the training and curiosity to make discoveries in the world of big data. . . . Their sudden appearance on the business scene reflects the fact that organizations are now wrestling with information that comes in varieties and volumes never encountered before.”

Data scientists help organizations get the most out of their data, in part, by using business requirements to drive the information exploration and the application of analytics. A fact-based decision-making culture is no longer an option. It’s a requirement for businesses that want to stay competitive. Be proactive, use the Information Evolution Model. Let your data give you a fresh perspective on your business. See what’s working, fix what isn’t and set your sights on new opportunities.

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Tech watch 2014

If you’re like most business leaders, you've spent the last couple of years educating yourself about big data and analytics. After all, trend watching isn’t just for those of us who create and sell software.

As technology consumers, you have to be aware of what your competitors are doing and what technologies are available to help your business. Innovation is occurring so quickly that it’s worth dedicating at least a few minutes of your daily routine to reading and researching the latest tech trends.

One recent report worth reading is Forrester’s Top Technology trends to watch: 2014 - 2016. I like their list because they surveyed both IT and business leaders from more than 2000 organizations, which gives it a good balance. And they’re usually pretty accurate about these things.

Let’s run through Forrester’s top four, and then I’ll add a fifth of my own at the end:

  1. Mobile applications. Mobile has been No. 1 on Forrester’s list for the last three years, so this one isn’t a surprise. Smart phones are proliferating, and so is our dependence on them. How can your business capitalize on this trend? Start by learning more about mobile marketing companies like Weve and Zapfi, and apply their concepts to your business to get a few ideas of your own.
  2. Big data and real-time platforms.  We’ve been talking about this one for a couple of years too. But most business leaders are still wrapping their heads around the technologies available to deal with bigger and faster data streams and the new varieties of data. To really understand this area, you need to move past the high-level, reactionary articles on the topic, and start studying big data analytics and Hadoop.
  3. Application platforms. I’m convinced that the future of cloud and SaaS will be about two things. One, analytics services. And two, the ability to break products into smaller pieces so that business consumers can purchase only the capabilities that they need. You should be thinking not only about what you’re developing for the cloud but also what features you want to see in the cloud to help improve your own results. Learn more in this overview of cloud and big data.
  4. Customer intelligence and analytics platforms. This one might be No. 4. But we all know that getting it right requires smart technologies and strategies for the first three on the list. After all, your customers are mobile, they’re generating big data, and they’re doing more and more with cloud-based apps. How can you use analytics to reach them and understand their needs? Try reading these case studies from Family Dollar and Proctor & Gamble for some inspiration.

For the fifth item, I’ll diverge from Forrester and add a low-cost technology that I’ve been watching closely:

  1. iBeacon software.  Beacons are small wireless sensors that use bluetooth technology to cast a net around a physical space and trigger app activity based on micro-locations inside that net. They can be used to transmit flash sales, personalized offers, or directions to a specific location within a store, restaurant or coliseum.  The cost of entry is low enough for almost any business to experiment with this new technology, and the data analysis implications are as wide as you can imagine. This Gigaom article about beacons does a good job of explaining the potential uses outside of the obvious retail scenarios.

Those are the technologies on the top of my mind so far this year. What about you? What gadgets, apps and platforms are catching your attention? And how do you see your business benefiting from the ones mentioned here?

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Q1 2014 Intelligence Quarterly: Set your data free for the big data world

1Q 2014 Intelligence Quarterly coverHave you been keeping up with the total amount of data being generated over the last few years? We’re moving from petabytes and exabytes to zetabytes and yottabytes faster than anyone ever imagined. And most of our systems were designed for a terabyte world.

Eighty percent of the world’s data didn’t exist two years ago. If you’re still using the same IT and data management strategies that you used back then, it’s likely your systems are out of date.

Until recently, conventional IT strategies have focused on generating, moving and storing data, as opposed to putting the data to work through the use of analytics. Instead of just capturing big data, you should be finding the best ways to use and reuse it wisely.

So which of the traditional data management methods still provide value? And what new strategies should you be considering? This issue of Intelligence Quarterly bridges the gap between old and new, explains what still works, and examines what’s coming next so you can:

  • Develop a solid strategy for liquidizing your big data asset, so it can flow throughout the organization. Read our data management backgrounder and data governance tips for relevant background information.
  • Start analyzing data as it’s streaming in real time. We discuss event stream processing, explaining how to analyze streaming data to spot patterns and make decisions when it matters the most.
  • Use new, in-memory technologies for analyzing large quantities and different types of data. For example, CSI Piemonte is combining and analyzing data from digital libraries, health care records, sensors and the Web to improve public sector programs.
  • Visualize your data as soon as possible in the information life cycle. The new data bank application in Denmark shows how visual displays of data can be used to benefit citizens and make public policy more relevant.

The stories in this issue about retailers, insurance companies and public sector organizations illustrate the importance of having clean, clear, relevant data that you can use immediately.

When was the last time you really had a conversation with your data? And what did it tell you? When you can break down gagged or constrained data structures and set your data free, it speaks more clearly and tells you things you would not know otherwise. That’s how the data starts to talk to you. It’s how you learn what your data has to say. And it draws your attention to questions you wouldn’t have thought to ask.

Unless you make the switch to managing data for high-performance analytics, your data will be an added cost, not an asset. With the right strategy, however, your data can be the competitive advantage you need to increase revenue and reduce costs.

So stop amassing data in old structures and start putting it at your fingertips. In the new digital economy decision makers need to connect the virtual with the practical – or the service with the product – and high-performance analytics does just that.

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From Santa to SAS, Macy’s delights customers

Rowland H. Macy was a shrewd businessman. Bearing a red star tattoo on his hand – the same iconic star that now adorns his chain of department stores – Mr. Macy was the first to usher Santa into his store as a way of luring Christmas shoppers back in 1862.

When he wasn’t rounding up rotund men and cloaking them in velvet, Macy was innovating in other ways. His store was first to create interesting and beautiful window settings. It was first to sell new items such as the tea bag, colored towels and the baked potato from Idaho. It was the first store in New York City to obtain a liquor license and the first company to promote a woman to an executive position.

That innovative spirit lives on today, as Macys.com uses technology from SAS to understand and please its customers – a strategy that helps it remain the most prolific retail department store in America.

But before I get into SAS, let’s stay on Santa. Tis the season, after all.

Father Christmas wasn’t always the jolly, bespectacled, white-bearded man you know him as today. Back in the 1860s, Santa wore a green hat with no trousers. In the early twentieth century, he fancied a yellow robe and hat. Later, at the apparent request of his physician, Santa picked up smoking, spending a stint hawking Lucky Strikes before Macy’s helped him kick the habit in the 1950s.

Macy’s had a knack for molding Santa’s appearance. In the seventies, they trimmed his unkempt beard and gave him the classic, red-robed look now burned into America’s consciousness. (His suit was later modified with straps after a little girl tugged his pants off on national television.)

150 years after Macy put Santa in his store, families continue to flock to Macy’s each year for a visit with old Saint Nick. It’s one of many ways Macy’s forward thinking has revolutionized the shopping experience. Today, that customer-centric approach comes in the form of SAS® Business Analytics, which Macys.com uses to understand its online shoppers and reach them with more relevant offers.

"Customers share a lot of information with us – their likes and dislikes – and our task is to support them in return for their loyalty by providing them with what they want, instantly,'' explained Kerem Tomak, Vice President of Analytics for Macys.com.

Further, as told in the success story, Macys.com uses SAS in tandem with Cloudera on Hadoop to automate reporting, saving the company $500,000 a year through increased productivity. This time-savings allows Tomak’s team to focus on more strategic initiatives rather than manually collating reports.

Get all Macy’s secrets to customer satisfaction in the success story. And while you’re at it, read about our big plans with Cloudera and Hadoop.

Wherever you are in the world, grab a hot chocolate, snuggle up by the fire and enjoy that warm glow of Christmas only Santa can bring. From everybody here at SAS, happy holidays.

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Why SAP and SAS? Why now?

I did a quick search on Twitter to see what’s being said about the new partnership between SAS and SAP. The tweets I found were positive – and I’m not surprised. SAS thrives on lots of data, and SAP creates lots of data. Why not marry your analytics environment with the environment where so much of your data is being generated?

 

 

 

A lot of SAS customers have tremendous amounts of data in their SAP infrastructures. Why not look at how to extend the value of that SAP investment? And what better way to do that than to integrate with the leader in advanced analytics?

From our perspective, it just makes sense to partner with one of the largest generators of transactional data. From SAP’s perspective, I’m sure it makes sense to give customers the power of predictive insight inside their high-performance platform.

If you think about applying forecasting or optimization techniques to that amount of data, you can start to imagine the possibilities pretty quickly. The analytics will be right there inside HANA to solve the most complex predictive analytics problems you can imagine.

Why not introduce that level of prediction and accuracy in the SAP environments, where customers haven’t seen it before? That’s where advanced analytics can really increase accuracy and provide benefit in the SAP environments.

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Is it time for a big data reality check?

It’s been more than a year since I asked the question, “Is big data overhyped?” My answer today is the same as it was then: No. Big data is not overhyped. It’s real and it’s growing.

Do we need a reality check, though? Do we need to talk about how we got here, and who should be most concerned about big data?

Big data didn’t just happen. It’s been growing as the use of computers, smart phones and the Internet has grown. It’s been growing as more and more devices are outfitted with smart meters and sensors and GIS transmitters. It’s been growing for decades.

What was missing, until recently, was the ability to gain meaningful and useful insights from big data. But now, low-cost storage and in-memory computing have converged to help organizations make proactive decisions about the future marketing, product, and customer decisions with big data.

Of course, not everything is big data. We know that instinctively. There are still a lot of analytical problems that you can solve without big data storage or big data analytics. Fraud detection. Quality control. Basic data mining. Most of these things can be accomplished without big data.

So who does have big data and big analytics problems? Well, banks, retailers and pharmaceutical manufacturers are some of industries facing the most obvious big data challenges, especially the larger companies in these industries with hundreds of thousands of customers and hundreds of thousands of products or treatment options. Smaller retailers and even regional banks might not have to scale quite as high.

But here’s the most important thing to remember, even if you don’t have big data in house: Every organization has the potential to benefit from big data. Why? Because so many of today’s big data sources are public. Think open government data. Think weather and meteorological data. Think Twitter. The data is out there, it’s free, and it’s waiting for you to analyze it.

A few examples:

  • A hospital in the Netherlands is incorporating weather forecasts to predict increases in pulmonary problems for patients with lung disease.
  • A GPS manufacturer incorporates government and meteorological data into its systems to augment the data it already receives from drivers and traffic patterns.
  • A chemical manufacturer stores as much Web data as it can get its hands on to better understand the use of its plastic products around the world.
  • Economic analysts are analyzing trending topics on social media to predict changes in unemployment rates, before national unemployment rates are released.

Even if you’re a small player in your industry, if you’re the first to store and incorporate some of these open data sources into your existing analytical work, you’ll have an advantage over your competitors. It might mean you’re dealing with big data for the first time, but data visualization and open source storage options are making the entry point feasible for almost any organization.

Does everyone have big data? Not even close. But is there big data opportunity for almost everyone? Yes, there is. And that’s the reality, not just hype.

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These shoes were made for walking

As head of SAS Americas, I walk a lot. In and out of meetings, onto planes, sometimes over competitors and always into Jim Goodnight’s office whenever I’ve got good news to share. Plus I pace on stage.

All that walking requires a good shoe. I’ve found the best shoe is one that fits your path in life. And you won’t find a better-fitting shoe than at DSW – thanks to SAS.

With SAS® Size Profiling and Pack Optimization, the second-largest shoe retailer in the US is now analyzing sales at the store level, and shipping each of its 377 stores the most popular sizes and styles for that location. Sounds simple, but an analytics-based inventory platform of this caliber is unique. The results have been fewer stock-outs and markdowns. DSW profits from having your shoe in stock, and you walk out a happy customer.

It’s been a sincere pleasure working with EVP Harris Mustafa on this project. Harris put it to me like this. “We used to believe that having a great assortment of shoes and boots was all we needed to do. We now see how much incremental business we have gained by having the right sizes of those shoes and boots available in our stores and online. We couldn’t do that before SAS.”

I’m proud of that.

Here’s how it works. Historically at DSW, shoes were shipped in standard 12-packs. It didn’t matter if a store needed only sizes 7 and 8 of a particular style, they would get a pack with sixes and nines too. With no optimized, sustainable or automated system in place, customized packs were a bear. With SAS, DSW now takes a data-driven approach to managing inventory, and the results have been, well … check out their stock value since partnering with us in 2011.

Granted, DSW isn’t using brainwaves to determine shoe prices quite yet, but the SAS customer intelligence platform seems to be working nicely for them.

I’ve got hundreds of stories like this. Companies partnering with SAS to do better business. As they arise, I’ll share them on this blog. Meanwhile, read the full version of the DSW success story at sas.com.

Until next time, walk boldly my friends.

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Have you used analytics to better serve your customers? If so, leave a comment. I’d love to hear your story.

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