SAS brings a unique perspective to the study. The company has been in the analytics software business since its inception more than 35 years ago, long before the term “Big Data” gained currency and we were still talking about the “information explosion” and “data deluge.”

The team surveyed about 2,500 business professionals, most of them at the vice president or department or business unit head level. They also conducted more than 30 in depth phone interviews.

Over 100 countries were represented, primarily in the U.S., but with the U.K. and India providing significant input as well. The companies surveyed fall into three distinct categories: 60 percent are analytical practitioners incorporating some form of analytics into their daily business practices; another 29 percent fell into the “analytically challenged” category; and only 11 percent earned the coveted “Analytical Innovators” designation.

Co-authors from the MIT Sloan Management Review journal included David Kiron and Renee Boucher Ferguson. Leading the SAS effort for this study and on-going research into the topic, is Pamela Prentice, SAS Chief Research Officer. We recently talked with Prentice to get an overview of the study’s results and her take on how Big Data Analytics are evolving.

inside-BigData: What are the key characteristics of an “Analytical Innovator”?

Pamela Prentice: Compared to analytical practitioners, Analytical Innovators look at things differently, do things differently and generate different outcomes. What we found in the survey is that there are no hard demographics differences between Analytical Innovators –they basically come in all shapes and sizes. This was an interesting finding – typically you have larger companies with extensive resources that allow them to operate on the cutting edge. This is discouraging to smaller companies that can’t tap into comparable assets. But this is not the case with the innovators – it’s their attitudes and actions that differentiate them, not deep pockets. Essentially it boils down to mindset and culture driving the actions they are taking regardless of the organization’s size.

We found that Analytical Innovators value their data more than other companies. They do a better job of collecting it, analyzing it, and pushing it out into strategic functions. And they are driven by analytic decision-making. This translates into specific actions where the balance of operating from a “gut feel” versus analytics tips in favor of the latter.

Another key differentiator is the culture. In all these companies, some more than others, the use of analytics is a top down mandate from their executives. Now, it’s not essential that analytics be sponsored by the executive suite, but it certainly does help. We found through this and other research that executives with the mindset of driving their decision-making based on facts are further along on their analytic journey. This attitude also gives lower levels of management the freedom to experiment with different analytics.

The Analytical Innovators not only use more data; they use more different kinds of data. They really embrace the notion of Big Data Analytics and they are more mature that other companies in this regard – they have been working with analytics longer than most of the others. They are at a point where they have built out the requisite underlying infrastructure and they have the processes in place to use analytics in innovative ways. I think the biggest differentiator between the Analytical Innovators and the others is that the innovators are open to new ways of thinking.

inside-BigData: Are there individuals within analytically challenged companies that are trying to introduce innovative Big Data analytics?

Pamela Prentice: In the answers to the survey’s open-ended questions, it became apparent that a number of the respondents were struggling with their company, which was not moving as fast as they wanted it to. They are in a dilemma – they value data and know they should be using analytics, but their organization is just not there yet.

Some of these individuals are forming groups within these organizations that are analytically challenged. We refer to these pioneers as “analytical evangelists.” However, the downside of being in this position is that you don’t have the infrastructure and access to the data that is needed to move ahead a fast clip.

Even though the grass roots approach is difficult and the top down approach is prevalent, we certainly don’t want to discourage individuals or groups of individuals from taking it upon themselves to get on the analytics bandwagon. Many of these obstacles can be overcome if you start with small problems and help the organization move down the analytics path in an incremental way.

There have been a lot of successful grass roots implementations. For example, take LinkedIn. Even though they are a digital company much more steeped in data and analytics than your traditional enterprise – it still wasn’t broadly accepted by management that they would use analytics.

There was a grass roots effort to launch that hit LinkedIn feature recommending people you might know. But it wasn’t very popular idea with the executives. However, the individuals who were responsible for this area pushed and pushed and the executives finally said, “Alright, let’s give this a try.” The rest, as they say, is history.

In the study we make recommendations as to how evangelists can bring analytics to their organization. For example, first find the geeks in the organization who have the talent and the desire to introduce the innovative use of analytics and build a community. Start with small tangible projects where you can pretty quickly show the benefits of using analytics. You don’t want to try to “boil the ocean” and not have anything to show for your efforts, you want a quick easy win. This is a somewhat simplistic recommendation but it really does work.

inside-BigData: How are the Analytical Innovators using the technology to solve specific problems?

Pamela Prentice: Problems that the Analytic Innovators are addressing using Big Data analytics differ from company to company depending on their analytic strategy. But all of them have become more strategic in what they are doing.

The majority is using big data analytics to make real time decisions. The second most common application is increasing the organization’s understanding of its customers. This use of the technology applies across all industries with a wide variety of customers.

The more analytically challenged organizations are just using analytics for operational purposes such as reducing costs and realizing other efficiencies. On the other hand, the innovators have gotten to the point where they can drive more strategic actions using analytics. It was clear from the interviews and the surveys that this is predicated on the development of a mature information value chain. They are much further along on the path to quickly obtaining good, accurate reliable data, analyzing it effectively and efficiently, and getting it out to the right people.

I should mention that the term “Big Data Analytics’ means different things to different people and their level of sophistication can vary greatly as well. For example, some organizations believe they are running an effective analytic strategy using Excel while others are using the latest in Big Data analytic tools. Also activity can range from isolated project work to enterprise-wide activities addressing overarching issues.

inside-BigData: Despite all the hype, aren’t we really right at the beginning off the adoption of Big Data analytics? How do you see the field evolving?

Pamela Prentice: Our research shows there is an ongoing evolution. We (SAS) have been in the business of analytics since the company was founded more than 35 years ago. Over the past three or four years what we see studying this analytics market is that increasing numbers of companies are adopting what they define as analytics. Also, the use of analytics is spreading throughout the organization.

In a lot of organizations the implementation of analytics starts at the department level – the executives might say “let’s try this out in a specific area.” What we see over time is that increasing numbers of organizations are moving from departmental level implementations to cross-divisional and then enterprise wide as their use of analytics becomes more mature. I think that will continue – the tools they are using are becoming more sophisticated; it’s an evolution from spreadsheets to business intelligence and dashboards and then on to descriptive and predictive analytics and more sophisticated applications.

But we must recognize that there will always be folks who are analytics averse – who think their intuition is better than relying on what the data shows.

inside-BigData: Can you give me a few examples of companies that are Analytical Innovators?

Pamela Prentice: The Nielsen Company is a good example. Data has always been at Nielsen’s core, but there was a lot of discussion about the 1200 households that basically ruled the ratings regarding what was on the airwaves. Well, Nielsen has changed – they are now very 21st century and have turned into an Analytical Innovator. They have created the “Nielsen Twitter TV Rating” for the US market. Under this agreement, Nielsen and Twitter will deliver a syndicated-standard metric around the reach of the TV conversation on Twitter. This is scheduled for commercial availability at the start of the fall 2013 TV season. Nielsen will watch this activity and then respond in real time. They have accepted that social media is here to stay. The old rating diaries system, dating to the 1950s and actually still in existence, are still used along with set meters to obtain those ratings. But their days are numbered.

Or take Disney – a SAS customer – and their MagicBand. This is a very traditional company that is highly innovative and creative. As part of their MyMagic project they will be issuing MagicBands – wristbands that their guests wear that contain all kinds of information. As they travel through the park, the band provides information about what they see and where they are, and allows them to charge their purchases. It also provides a gold mine of information about the individual customer that Disney can use to make its services even more attractive. For example, here’s a cool thing: if the parents okay it, Disney characters like Sleepy, one of the Seven Dwarfs, will be able to greet your child by name by using a hidden sensor that scans the wristband.

inside-BigData: What SAS solutions address the Big Data analytics market?

Pamela Prentice: Pretty much everything SAS does addresses this market. If you need it, we have it. And we have just announced a very exciting new product – SAS Visual Analytics. The product combines self-service business intelligence with the industry’s most widely used analytics so organizations can explore all relevant data quickly and easily. SAS Visual Analytics is geared to companies that want to capitalize on Big Data and take their place as Analytical Innovators.

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Ayasdi today announced that it has received $10.25 million in a Series A funding led by Khosla Ventures and FLOODGATE. Ayasdi has created the world’s first Insight Discovery platform, a technology that combines advanced mathematics, computer science, and visualization to help organizations find breakthrough insights in large and complex datasets that lead to impactful outcomes.

The answers to today’s most important scientific, business and social problems lie in data,” said Gurjeet Singh, CEO of Ayasdi. “The biggest challenge in Big Data today is asking the right questions of data. The power of Ayasdi is its unique ability to automatically discover insights–regardless of complexity–without asking questions. Ayasdi’s customers can finally learn the answers to questions that they didn’t know to ask in the first place. Simply stated, Ayasdi is ‘digital serendipity.”

Read the Full Story.

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In this video, Joe Boissy from 3VR presents: Big Data Video Analytics – From Big Picture To Reality.

3VR, the video intelligence company, enables organizations to search, mine and leverage video to bolster security, identify and mitigate fraud and better serve customers. 3VR’s Video Management Software, Network Video Recorders, and Video Analytics allow video surveillance systems to reach their true potential and deliver a measurable and sustainable return on investment. 3VR is the video surveillance standard for hundreds of global customers, including leading banks, retailers, governments and law enforcement agencies and owns CrimeDex, an online community of thousands of fraud, loss prevention and law enforcement professionals dedicated to stopping crime.

Read the Full Story.

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In this video, Google’s Ryan Boyd presents: Crunching Big Data with Google BigQuery.

Google BigQuery is a Big Data analysis tool born from an internal technology known as Dremel. BigQuery enables developers to analyze terabyte data sets in seconds using a RESTful API and a SQL-like query language. We’ll demonstrate how you can incorporate Google BigQuery into your own applications, and how queries are processed underneath the covers. We’ll also show examples using public data sets and demos built by other developers like you.

Boyd describes Big Data as 500 Million rows or more, and the Google BigQuery web service lets you do interactive analysis on multiple billions of rows.

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Over at the Wall Street Journal, Ben Rooney writes that Tableu software is making Big Data more accessible by putting sophisticated and powerful data visualization tools into the hands of anyone comfortable using a spreadsheet.

Cognitive bias is a problem for smart people. You start with this presumption that the world looks like, The smarter you are the faster you do that because that is what being smart is, forming conclusions from data,” he said. “But the problem is that sometimes you can be really wrong. If you can use data, and particularly if you can visualize that data, then you can test your theories.”

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There seemed to be a gap in the market that I noticed in terms of there wasn’t really an ability to create interactive and real-time data analysis,” said founder Justin Langseth.

The idea garnered Langseth top honors at a TechBuzz competition in May, where 28 upstarts pitched to a room of investors. Augaroo was named Best of the Buzz, and Langseth said the company could finalize an initial round of venture funding this summer. Read the Full Story.

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In this video, Jer Thorp from the New York Times labs talks about how he creates beautiful data visualizations to put abstract data into a human context. At TEDxVancouver, he shares his moving projects, from graphing an entire year’s news cycle, to mapping the way people share articles across the internet.

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In this Ted Talk video, David McCandless turns complex data sets into amazing diagrams that highlight unseen patterns and connections. McCandless suggest that good design is the best way to navigate Big Data and change the way we see the world.

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This video provides a brief explanation of Plot.io’s rendering system, and how you can use it to build novel data visualizations on the fly without having to pick and choose from a canned list of ‘charts’.

Learn more at http://www.plot.io.

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Nathan Yau

You can spend a lot of time with icons or fancy print, but the graphics are interesting because the data that the visuals represent is interesting.  It should always be data first.  Certain graphics get eyeballs because they show something that wouldn’t be seen in a table. Visualization is less about the individual values and more about the distribution of them over time and space.

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