The problem with analytics

At SAS, we use terms like “machine learning,” “predictive modeling” and, of course, “analytics” quite a bit in our day-to-day business. Not surprising, given that we're the largest analytical software vendor out there. But have you noticed that these terms are popping up more frequently in news articles and blogs?

I have. Maybe because that’s the kind of stuff I end up reading, but I’m not so sure. I feel like these terms are popping up more and more; not only that, but when these terms are mentioned, it’s quite often in passing, or in a matter-of-fact way.

538946315I like this. I like the fact that people talk about analytics now, and not only that, they're talking about sophisticated analytics. That’s great. Really great. It’s talked about in a way that makes it sounds easy and achievable. Which it is. At SAS, we've done a lot to advance the use of analytics and to make the development of analytical assets easier and easier. We've even made the deployment of those assets as easy as possible.

But, and you knew this was coming, there's a problem. It’s still not easy to get the kind of analytics that the trade magazines and we at SAS talk about into a production process. Yes, we as a software company have done pretty much everything we can do to make it easy, but there's still a problem.

It’s you. Read More »

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Big data allows us to rethink data strategy

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Sometimes, a data swamp is exactly what you need.

Start with the end in mind -- wise words that apply to everything, and in the world of big data it means we have to change the way we look at managing the data we have.

There was a time when we managed data quality, and the main goal was to meet a metric that said data should be x% accurate. I’d argue that this is no longer relevant. Now, before I’m hunted down by all the data analysts out there, I’d like to clarify that I’m referring to managing data for data’s sake. Often we manage the value out of the data right when we need it most.

Does this mean I’m advocating that we cease performing any data quality work in the data stores holding that information? The answer is yes and no. That’s helpful isn’t it?

The answer became nebulous about the time new capabilities were created with the new big data architectures. Let me explain what I mean. Read More »

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Presidential election quiz

With the first debate between the two candidates behind us and the culmination of the US presidential election drawing near, who wouldn’t love to predict the winner? I don't have a crystal ball, but I do have the power of unstructured text analytics at my fingertips.

With the help of some good public data in the form of primary debate transcripts from the American Presidency Project and your input, I can tell you whether you’re likely to vote democratic or republican. Put my analytical powers to the test -- select your "hot button" issues from the two columns below:

Column A Column B
Focus on fighting ISIS Focus on ISIS in the context of wars in Iraq, Syria
Second Amendment/Bill of Rights provisions for guns  State legislation for gun safety/control
Illegal immigration, controlling borders Immigration reform, path to citizenship
Negative sentiment towards trade deals Wall Street and big banks regulation
Balancing the federal budget Affordable health care and insurance
Tax cuts Taxes to pay for public colleges

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Our world is flat, but our employees aren’t: How to manage and hire with analytics

“Every morning in Africa, a gazelle wakes up. It knows it must run faster than the fastest lion, or it will be killed. Every morning a lion wakes up. It knows it must outrun the slowest gazelle, or it will starve to death. It doesn't matter whether you are a lion or a gazelle. When the sun comes up, you better start running.”
-- Thomas L. Friedman, The World Is Flat: A Brief History of the Twenty-first Century

Why a flat world? Because in every industry at every level, the middle man is being cut out and consumers are going directly to the source. The formal term for it is disintermediation – and we’re seeing it everywhere: Twitter and Facebook disintermediate the news industry;  Uber and Airbnb the travel industry; Chinese e-commerce giant Alibaba the retail industry. Disintermediation is the the foundation of the sharing economy.

But how does this radical external transformation translate to internal transformation for your company? What does the ideal organization look like in a flat world?

The companies prospering in the future might be the ones that take advantage of decentralized, self-organized, globally distributed communities working together to produce value.

As the world flattens, we see organizations also flattening, challenging the intermediate levels. But they’re struggling to redefine the manager’s role. The org chart, while easy to understand, doesn’t represent how things get done or how innovation and value are created.

A better option is a network diagram. Managers can use network analytics to recognize, promote and efficiently distribute collaborative projects. Adjusting supply and demand at the level of their top collaborators will increase the success of the whole team. Read More »

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More data journalists – not data scientists

Recently, I was talking to a director of analytics from a large telecommunications company, and I asked her, “Do you think we have a skills shortage?” She replied, “NO, I think we’re just looking in the wrong place.” I wanted to hear more as this analytics expert may have just solved one of the biggest problems of 21st century: the shortage of analytics skills.


A good data journalist can solve a complex data problem and tell a compelling story of the value to the business.

She went on to explain that there’s a large pool of data scientists who can develop great models, extract intelligent insights, and solve complex business problems with analytics - but most lack the skill of telling a good story. Her theory and recent practice is to hire media people and out-of-work journalists to get better results with analytics. I remained intrigued, as she explained further:

A journalist is an investigator who gets a thrill from extracting information, but also has the skill to turn the facts into a compelling and digestible story. Likewise, a good data journalist is a person who gets a thrill from solving a complex data problem combined with telling a compelling story of the value to the business. Can these same skills be important to an analytically driven organisation? Seems so.

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Four key data monetisation opportunities

What if you could predict with near-perfect accuracy what you’re going to sell and when your customer is going to buy?  Right supply, right time is the goal German manufacturers have set themselves, without reducing the configuration options customers expect.DM4 pic_lr

Having almost completed stage 1 of their plan – changing processes and ways of working off the back of internally monetising their data – they’re now looking externally to close the gap.

So where are these opportunities to monetise data?  They can be broken down into internal and external activities.

I’m often asked why we consider internal activities as data monetisation, rather than simply better business management.  There are several important reasons why we need to look at internal data monetisation efforts. For example, government agencies don’t have the ability to sell their data, however, the value of combining different agency data sets is evident for understanding fraud, terror activities, tax evasion and student-loan default.

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'Data for good is in the DNA of this organization'


Jake Porway, DataKind

Wheat rust. You may have never heard of it, but in a matter of days, this fast-moving, silent-killing plant disease can completely annihilate a critical wheat farm in Ethiopia. Wheat rust’s newest nemeses? A legion of volunteer superheroes in the Data for Good movement.

When Jake Porway, Founder and Executive Director of DataKind, bounded onto the stage at Analytics Experience this week, he spoke of a few of these superheroes who recently came together, hackathon style, in Seattle, Washington, to look at satellite data and infrared models just to locate all of the likely wheat farms throughout Ethiopia. Integrations with Google Maps and visual analytics allowed them to find and predict which of those farms had wheat rust – with 80-percent accuracy, by the way – and where this devious disease was headed next. This information is now allowing partner Bill & Melinda Gates Foundation to contact farmers in practically real time to suggest shifting crops or applying preventative pesticides.

All this from a team of volunteers working together for just two days.

“This work has bigger impact than you might imagine,” said Porway. “For organizations that don’t have access to this stuff, it’s like magic. It’s incredible. It’s like sci-fi.”

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What’s your big data problem?

topics in big dataAnalytics Experience 2016 featured more than 100 breakout sessions and talks covering numerous topics in big data. You can watch many of those talks from our Analytics Experience 2016 video portal, where select keynote and session talks are archived. To give you a taste of the content you'll find there, here’s a look at two of those sessions from two very different companies – Dow Jones and UnitedHealthcare – which transformed their data strategies with SAS®. For Dow Jones, the answer was the cloud. For UnitedHealthcare, Hadoop. When it comes to data problems, there’s no one-size-fits-all solution. The beauty of SAS is that it’s adaptable to all different sorts of problems.

Building practical analytics at The Wall Street Journal

In many ways, Dow Jones, which publishes The Wall Street Journal, MarketWatch and Barron’s, is a traditional publisher trying to transition to a new media landscape. In response to a shift in how its customers consume news today, the company has been forced to make its own changes. Jeff Parkinson, Vice President of Customer Operations at Dow Jones, spoke during an Analytics Experience breakout session about how the business has transformed the way it uses data to understand and support its customer base.

Working from legacy systems, Dow Jones faced enormous challenges, with multiple analytics teams working on the same analysis. “To get a standard data model out there – how many subscribers we had in a particular region – would sometimes take us weeks,” said Parkinson.

Parkinson knew the company needed a major change, but he didn’t want to lose the code that his team had already written in SAS®. After consulting with their SAS Account Executive, the company decided to supercharge their program with SAS/ACCESS® Interface to Amazon Redshift on AWS.

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What’s your digital health care plan?

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Jared Cohen, president of Jigsaw, at Analytics Experience 2016

Historically, generations could expect to experience one – maybe at best – major technology disruption or transformation in their lifetime. That’s simply not the case today. Today it is more difficult than ever before to know what will transform the technology landscape in the next month or the next year. And that dilemma is what Jared Cohen, president of Jigsaw, calls “the new world disorder.” Cohen took the main stage at Analytics Experience 2016, to explore the unparalleled advances and unprecedented threats we need to be prepared for as the physical and digital worlds merge.

“Technology is changing the world, but the world is also changing technology,” said Cohen. “But while those changes are extraordinary, they only tell us a fraction of what’s happening. We really need to be asking ourselves how all of this technology is impacting our world.”

Consider North Korea, a place where citizens receive the death penalty if caught with a smartphone, yet tens of thousands still risk their lives daily to have access to mobile devices. At the same time, in Brazil, where the Amazon jungle is being used by criminals and traffickers to hide slave-labor camps, drones are being used to fly over the jungle to uncover such camps, and in the last year 700 camps have been discovered. These environments force us to rethink the power of technology, its capabilities and the risks and lengths people will go to maintain access to the digital world.

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An analytics revolution underway: Insights from Analytics Experience 2016


“What we are experiencing from analytics today is nothing short of a revolution.” SAS CEO Jim Goodnight at Analytics Experience 2016

“What we are experiencing from analytics today is nothing short of a revolution,” said CEO Jim Goodnight, who spoke at Analytics Experience 2016 and set the stage for the conference’s executive panel. “Right now, my primary mission is to ensure people understand the limitless possibilities that lie before us, given the power of analytics.”

After decades of growth, the field of analytics has truly arrived, maturing into a discipline deeply embedded into all of the best decision making. The executive panel, which was moderated by Executive Vice President and Chief Marketing Officer Randy Guard, took a deep dive into analytics and what it will take for companies to move from the promise of analytics into the reality of its competitive advantage.

Here are some of the highlights from the session.

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