Why would you want to visualize 5 billion rows of data?

Jim Davis
Jim Davis|May 7, 2012

A few years ago our new media team made this quick video to give you sense of just how big a billion is:

That’s big.

But now that you can visualize a billion dollars, can you imagine what a billion rows of data looks like? Or find value in those billions of variables? Not really. And that’s just one reason why data visualization is important for “big data.” Most of us can’t understand what we can’t see.

On the other hand, if you can visualize billions and billions of values by quickly distilling them into subsets, graphs and box plots, you can start to understand the data better. Essentially, visualization gives analysts a jump start on the modeling processes by making it easy to explore the relationships between variables and attributes. When you can actually see and query that much data, you can start to determine which fields mean something and which you should maybe discard.

Before high-performance analytics, you could only do these types of explorations on subsets of large data sets. Now, high-performance analytics make it possible to visualize all 5 billion (plus) rows of data.

What’s the significance? Whether it’s 1 billion or 5 billion rows of data, in-memory analytics is changing the way organizations look at and model data. This becomes even more important when viewed as part of a larger process. Discovery from the visualizations can be fed into more in-memory processes where advanced analytics, like marketing optimization, are applied to improve the business.

With all technology applications, the real benefit will come when high-performance processes are put in to context for business solutions, such as merchandise planning, assortment planning, fraud detection, and value-at-risk calculations. The visualizations are exciting and can provide the early insights, but dont' forget: improving business processes is the essential next step.

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Q2 2012 Intelligence Quarterly: High-performance analytics

Mikael Hagstrom
Mikael Hagstrom|April 13, 2012

Big data. Hyperconnectivity. Speed to intelligence. These central themes have served as common threads in previous issues of Intelligence Quarterly, a journal that examines the many ways analytics transform the world. Taken separately, these elements could be seen as just added complexities, but the convergence of the three is where true value exists.

In the second-quarter issue of Intelligence Quarterly, we explore the ways high-performance analytics knits all three together to give organizations the ability to know, the ability to connect in real time and the ability to innovate – all of which leads to high-impact insights, enables transformation and fuels growth.

Download your copy of Intelligence Quarterly to learn more about how high-performance analytics can help organizations take full advantage of big data, hyperconnectivity and speed to intelligence and, in turn, supply the power needed to take today’s challenges and turn them into unprecedented opportunities.

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You saved 92 hours. So what?

Jim Davis
Jim Davis|April 4, 2012

Let’s talk about the time savings involved with high-performance analytics. I’ve blogged before about the time reductions we’ve seen when analyzing big data with our high-performance analytics solutions. We’re quoting time savings of up to 92 hours, but what does that mean? Or, as I like to ask, “So what?” Well, it’s not just the time savings that matter. It’s what you do with that time – and what you’re getting within that time frame that matters.

It comes down to this: do you want to be reactive or proactive in your decision making? Let’s take an example from retail. A lot of high-performance analytics products can give you a report in seconds about last week’s sales or last season’s sales for every item in every store. But that’s reactive. How can you become proactive in the same amount of time? You predict which products to stock in each store for each season, including how many of each size and each color for every store.

If you can get THAT kind of information in a matter of seconds instead of days, you really are making a difference. Before high-performance analytics, when it took days to find the answer to that question, you had one chance to build the predictive model, send the results up the chain, get a response on how to proceed and maybe apply one or two changes to the model before making your final prediction and planning your entire quarter based on the results.

Now, if you can get those results in a matter of seconds, you can improve the model multiple times before sending the results for review. You can select different variables, ask more what-if questions and even provide multiple options for decision makers to determine how they want to stock each store for the season. You even have time to go back and forth for a few days getting input from buyers and store managers to improve the model based on their feedback. Or you can make a change in a few stores and add the results you see there back into the model for next year or next quarter.

When we talk about changing your processes based on the time savings from high-performance analytics, these are the changes we’re talking about. It’s a matter of bringing more input and more ideas into the decision making process, and having the ability to ask more questions of your data in a shorter time frame – all with the end result of making better and more accurate decisions.

Now, use our retail scenario to answer that original, “so what?” question. So what if you can save 92 hours in your modeling process? What does that mean? The obvious answer is that you can build more models. But that doesn’t mean you make 100 more predictions. It means you can improve your original prediction in 100 different ways. Or you can bring 100 new ideas to bear on the original model. Or maybe you can shorten your seasonal planning timeframe by 100 days. The ultimate result, the ultimate “so what” is that you can improve revenues for every store in the retail chain. You plan better for each store and each season, and you have higher quarterly earnings and less overstock to clear out before the next season starts.

That’s a whole lot of “so what?” And it’s not all about the time savings. Time – thanks to the power of high-performance analytics – is just the factor that makes the rest of it possible.

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Get smart on the Smart Grid

Jim Davis
Jim Davis|March 27, 2012

The Research Triangle area is getting attention as a hub of innovation for the Smart Grid.  Some facts:

  • Nearly 60 companies in the Triangle are working in various aspects of the Smart Grid Industry.
  • Twenty of those companies are headquartered in the region.
  • From software to sensors and routers, the area is teeming with innovations that will be central to the smart grid transformation.

I know some of you are asking: “The smart what?  Smart phone? Smart boards?” No, the smart grid.

Utilities are rolling out smart meters on homes and businesses that give more insight into energy consumption and also give customers better control over energy costs.  These meters spit out data all day long.  So do the new sensors on the transmission lines that can detect changes in voltage before an outage.

What the utilities plan to do with that data is the subject of an upcoming two day Networked Grid conference here in the Triangle.  Undoubtedly, the data is bigger than these utilities have ever collected before.  Plus, the business environment is increasingly complex.  Antiquated power distribution systems, pressure to integrate renewable resources, and global fuel price volatility have all increased the financial and operational risk of this industry – and it’s an industry whose products we as energy consumers rely on every day.

It is an industry in the midst of transformation.  There are plenty of lessons to be learned from other industries but also from energy peers around the world. At the conference, I’ll be sharing my insights on “Big Data and Analytics.”

If you’re attending, you’ll see why the Triangle region is the place to be for Smart Grid.

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Big data is nothing new, but high-performance analytics is

Keith Collins
Keith Collins|February 24, 2012

The most exciting thing in the industry right now is not “big data.” It’s “big analytics” or — as we like to call it — high-performance analytics.

Speaking from the perspective of somebody who’s been in this industry for many years, big data is not a new issue. We’ve been through this. We called it the Enterprise Data Warehouse (EDW) before, and everybody debated on how big their data was. It’s not about how big it is. It’s about what you’re going to do with it. That’s why the exciting thing is big analytics – because it’s the analytics that help you do something with all of that data.

We’re going to start to see an amazing transformation as people understand the value of what they can do with the data they have. There’s been a real challenge as we look at the old architectures of our traditional EDWs, because those that were organized around transactions weren’t appropriate for analytics. This has been part of the heritage of SAS. We’ve known for a long time that you really have to organize the data for the way you want to get information out. Now this theory is being validated in the era of big data. More and more people in the industry are understanding the importance of organizing your data in order to process it as quickly as possible.

Yes, it’s great that you can take a customer in retail from optimizing a problem with 270 million SKU combinations and going from 30 hours of process time to two hours. Fantastic. The time savings is important, but what you’ve really done is provided the organization with the ability to do scenario analysis. That’s even more important.

Now that same retailer can really work to see if they have the best plan. Before, they worried about getting through the weekend so they could just have the inventory stocked through Monday. Now they can go through the simulations, they can actually see what the different opportunities are to shift their market and to get ahead of their competitors.

As this example illustrates, one of the things I’m really excited about as we approach high performance analytics is the focus on the next business problems to solve. Now we can ask the question, where are the bottlenecks in the organization? And that’s really changing the outlook of our customers as they look forward. Take the example of how many accurate models can be created. We’ve had customers that went from being able to do just 50 models to doing a thousand models with the same staff of five analysts.

Why does that matter? Again, it’s not the speed so much as the ability to ask – and answer – 20 times more questions and then change the business as a result. So now the percentage change you can make in the business – the lift in sales, the identification of the fraud and so on – goes up. So now you are really having a bottom-line impact on the company.

With high performance analytics, we’re already seeing customers change their thought processes, change their businesses and change their approach to the data.

Banks are changing how they look at risk in risk portfolios, which allows them not just to understand risk at the end of the day but at the point where the transaction is occurring. Likewise, understanding fraud in the public sector is becoming easier. Or in the healthcare industry, we can actually do text mining across emergency medical service logs and start to identify disease outbreaks weeks earlier than you could by looking at hospital records. These are opportunities that we have now with this type of processing power.

Ultimately, you and I are going to benefit from these applications of analytics too. Our economies are stronger when the banks have a better understanding of risk. Our taxes are lower when the government lowers its fraud expenses. And our communities are healthier when disease outbreaks are pinpointed and treated earlier.

That’s why I’m excited about high performance analytics – and why you should be too.

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Q1 2012 Intelligence Quarterly: Energy transformation 2.0

Mikael Hagstrom
Mikael Hagstrom|February 15, 2012

Intelligence Quarterly - Energy transformation 2.0In this year's first issue of Intelligence Quarterly, we examine the role of analytics in the energy sector of today – and the transformations it could bring to bear for the future.

Although news headlines as of late have centered on the economic crisis, the energy discussion is not one that can be separated. Energy is the world’s largest industrial sector, and consumption levels continue to rise, adding to cultural, political and economic tensions around the world.

The articles found in the Q1 2012 edition of Intelligence Quarterly articulate the many ways we can transform the energy sector, including reducing energy consumption, increasing supply and enhancing our ability to deliver cleaner energy. Change and innovation are needed from all angles – from demand to supply to risk. Data is being referred to as the new oil, a valuable resource of the 21st century; part of the equation is using analytics to capitalize on the data asset by making the grids, and indeed the whole supply chain, smart if not intelligent.

As you will read, it is becoming increasingly apparent that analytics is the single contributor holding the greatest potential as we enter the digital world. Download your copy by clicking on the image above.

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Is big data over hyped?

Jim Davis
Jim Davis|February 3, 2012

We’ve definitely hit the point in the “big data” hype cycle where people are looking critically at the term and asking what all the fuss is about. Some pundits have even speculated about a big data bubble that’s sure to burst after everyone realizes the term has been over-used, and technologies like in-memory processing and high-performance analytics (HPA) are not actually in high demand.

Folks, there’s no bubble here. You can’t put the data back in the bottle, so to speak. Big data – however you define it – isn’t going away and it isn’t getting smaller. It’s going to keep growing.

Maybe the term “big data” will change or devalue in significance, but you can’t poke holes in the concept itself, which is this: there are significant business benefits to be gained from storing and analyzing large volumes of data more efficiently.

That doesn’t mean big data is an issue for everyone or that high-performance analytics is the answer for everyone.   But there is a business case to be made for the use of high performance analytics in many arenas, and those that see the whole idea as overhyped are looking at it too narrowly.

Here’s how I like to look at it: High-performance computing is, simply, an enabler. Most importantly, it enables you to get answers faster than before. But – and this is important – high performance computing is only as good as what you’re computing. If you’re getting summary statistics about your business portfolio, HPC can give you those reports faster. However, if your system is predicting risk exposure on thousands of assets, you’re going to get those predictions faster than before. Or, if you’re optimizing markdowns for millions of SKUs at hundreds of retail locations, you’ll be able to optimize those prices more quickly. That's high-performance analytics.

You see the difference?

A lot of big data proponents are promising things bigger, better and faster. But if the information you’re getting is backward looking, it’s still going to be looking at the past when you get it in a shorter timeframe. You’re still only understanding the past faster than before. No matter how fast you go with summary statistics, you’re never going to get to the future.

Only predictive analytics like forecasting and optimization will bring you out of the past and into the future. When you use high performance analytics to predict things like risk, customer satisfaction or marketing optimization, you’re getting your predictions sooner than before, and you can react more quickly. When you’re computing forward-looking results, the speed really can make a difference.

At its most basic level, high-performance computing reduces the time dimension. You have to decide what types of answers you want more quickly: standard reports or predictive analytics.

Once you know that, you can start asking questions and making decisions that could change your business. Or your world. I don’t think that’s hype. It’s as real as you can get, and the organizations that get it first are going to be the ones that make it further into the future.

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Davos 2012: How can we embrace opportunities in this risk-filled environment?

Mikael Hagstrom
Mikael Hagstrom|January 27, 2012

A question that is on the forefront of many discussions in Davos this year is how do we create inclusive growth amidst a volatile environment fraught with global risks? The problems are common and shared by governments and businesses around the world; how can they develop strategies to rebalance, identify long-term opportunities for sustainable growth, and provide an environment where innovations and new business models can be cultivated? How can they respond faster and in more economical ways to the needs of citizens and consumers?

The answers may be right at our fingertips.

While the economic complexity and challenges have been growing, another area has also been growing – and growing exponentially. I’m referring to the amount of data being generated every minute of every day. The ongoing digital revolution has resulted in the generation of data in vast quantities, most of which is captured by commercial or public organizations.

Every swipe of a credit card, every click of a mouse, every grocery item scanned, every comment posted online, all add to the massive data pile referred to as big data. Personal location data is exploding worldwide as well because of the availability of GPS systems embedded in mobile devices. If we realize that data is an asset, then we can also realize the importance of analytics to uncovering the answers that lie within this new asset class.

Today’s hyper-connected world continues to accelerate the speed of change and, combined with big data and the social media development, has the potential to enable a data-driven transformation of the public sector and, in essence, all other sectors as well.

Growing deficits combined with increased volatility and heightened fragility will force change. Converging transformational changes such as analytics will allow for accelerated pace of innovation and increasingly empower us to draw on the collective knowledge. In this way, hyper-connectivity is a catalyst for growth. Economic growth and social benefits can be driven through the use of technologies such as analytics that innovate off and leverage from the collective knowledge and big data.

The WEF report, “Personal Data” calls personal data the new oil – a valuable resource of the 21st century, a position that I concretely support. It is my belief that analyzing large, hyper-connected data sources will close the gap between how much knowledge is available and how much value can be gleaned from them.

The real value comes when analytics is applied to the new, unstructured data sources, combined with traditional data, to help make knowledge-based decisions that will benefit organizations, stakeholders and the broader global economy. We have the data at our fingertips; it’s time we recognize the value that lies therein to improve and sustain our quality of life.

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SAS = analytics. Analytics = hot.

Jim Davis
Jim Davis|January 19, 2012

The analytics market is hot. We’ve been saying that for a while now, and so have many industry experts. If you didn’t believe it before now, check out our newly released 2011 financial results and see if that doesn’t convince you. SAS earned US$2.725 billion in revenues last year, a 12 percent increase over 2010.

If you really want a picture of how hot the analytics market is, there’s no better indicator than SAS sales. After all, SAS has 35 percent market share for analytics, and the other nine analytics vendors behind us don’t own 35 percent of the market combined. In other words, our competitors are doing some work with analytics, but their revenues come from other areas as well.

Need another indicator? Our head count for 2011 was up 9.2 percent globally. What does that mean? It tells me, again, that the value proposition of analytics is hot. We initially had plans to grow head count by 4 percent last year. We never thought we’d grow by more than 9 percent, but demand for analytic solutions has been high, and we had to hire to keep up with the demand.

You know, I really like this time of year. January is underway and New Year’s resolutions are still hanging on. Our sales staff is in town to kick off another great year, our revenues are up again – and one of our favorite workplace awards has been announced.

This year, we ranked #3 in FORTUNE’s Best Companies to Work For list. We’ve been on the list every year since its inception and this is the ninth time we've been in the top ten.

The bottom line this year - and every year - is that SAS is appealing to customers and employees not only for its software and solutions but also for the genuine relationships we foster. Long-term business deals are based on the relationships that our employees build with customers, and that’s why the FORTUNE award is so important.

Technology alone cannot sustain you as a business. If we want to maintain our 36-year streak of growing revenues, we know the relationships we build with employees and customers are just as important as our solutions. As more and more technologies become commodities, those relationships will become even more important.

Right now, SAS has both: the best people and the best analytics. It’s a great place to be. So, watch out 2012. Things are only going to get hotter!

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Retail: the “sophisticated numbers game”

Jim Davis
Jim Davis|December 23, 2011

Are you a last-minute holiday shopper? Or the type that has everything bought, wrapped and tagged before Thanksgiving? It's safe to say, I do all of my shopping the week before Christmas.

Whether you’re shopping late or shopping early, you’re probably looking for the best deals - and so are retailers. They want to make sure you’re happy with the price you pay while they still make a profit. What’s the secret to making sure customers get a deal and retailers don’t mark down too much? Analytics, of course.

This marketplace podcast, “The secret life of discounts,” from American Public Media does a great job explaining how customer data, seasonality and regional demographics can all be used to help optimize markdowns, promotions, product placements and more.

Enjoy the insights, the shopping and the holidays. See you in 2012.

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