“House of Cards,” Netflix’s new hit political drama series, is not just good TV—it’s good TV because it was informed by Big Data. Netflix analyzed the viewing patterns of its 33 million subscribers to arrive at the following conclusions:

• Many viewers already liked the original BBC version of the series
• Movies featuring Kevin Spacey garnered a lot of views
• The work of director David Fincher achieved high start-to-finish stream rates

The result? “House of Cards” is now the most streamed piece of content in the United States and 40 other countries, according to Netflix.

Talk about a good business decision.

Netflix was able to analyze and cross-reference exactly what subscribers were watching, when, and on what device. They were also able to see when viewers would pause, fast-forward, rewind, and/or completely abandon watching a show. All of this information allowed Netflix to produce a show that viewers would not only like, but would also prove to be a solid investment for Netflix’s bottom line.

What does Netflix have to do with the Starview Enterprise Platform and Operational Intelligence, you ask? Here at Starview, we’re taking data in motion about our customers’ operations (as opposed to static data, like, the number of viewers that abandoned any given show) and similarly evaluating that data for patterns, correlations, similarities, and differences.

But, there is the crucial point of differentiation. Whereas Netflix execs evaluated their aggregated, static data over a period time to reach the decision to purchase “House of Cards,” the Starview Enterprise Platform allows users to build a decision making step right in to the analysis process, and trigger an action based on that decision, in real time, without disrupting the flow of other business processes.

To put this into a “House of Cards” perspective, it would be like if viewer input could change the course of the show while it was airing. This type of entertainment consumption may seem far off; but it’s exactly what the Starview Enterprise platform allows businesses to do, making a significant difference in industries like manufacturing, utilities, financial services, and telecommunications. These businesses especially need to be able to respond in real time and take action at the point of maximum influence in the business process, instead of waiting for static analysis while the opportunity at hand passes by.

While it used to be that there was no such thing as a sure thing in the entertainment business, data informed decisions are making that possibility more of a reality, and “House of Cards” is just the beginning. Operational Intelligence works in similar fashion by gathering, analyzing, and acting upon massive amounts of data while it is in motion for more efficient and timely decisions affecting your business’s bottom line. Think, how can your business benefit?

New York Times Technology Reporter Steve Lohr recently penned a fascinating look at how humanities studies, from literature to history, are being advanced by machine learning and algorithms adopted from the Big Data technology space. One of the studies he reports used statistical aggregation to determine which authors in the literary canon had the greatest influence on other authors. Analysis of writing style, themes and word use revealed that Jane Austen—not Charles Dickens or Mark Twain—actually had greater influence over other authors. Another study evaluated the most “quotable” movie quotes from enthusiast websites and found that many fans’ favorite lines consisted of unusual word choices embedded within sentences of ordinary structure—such as “I love the smell of napalm in the morning” from the film “Apocalypse Now.”

While studies like the ones mentioned above are exciting for a number of reasons—especially for those interested in the marriage of qualitative and quantitative analytics—I think they’re most interesting for the commonalities to what we do here at Starview. The only difference is that we’re taking data in motion instead of static data (such a book already written, or a movie already made) and evaluating it for patterns, similarities, differences, and in some cases, egregious anomalies that might otherwise be missed due to the sheer enormity of information accumulating at any given point within our customers’ operations. The Starview Enterprise Platform also allows users to build decision-making capabilities into that analysis, and, if needed, to trigger an action based on that decision.

Imagine for a minute that every published author throughout history were alive today, and continuously adding to their respective oeuvres. Now imagine that as every new phrase is put to paper, simultaneously each—millions of words at once—is evaluated against all the other phrases also being created simultaneously. Imagine that any red herrings that arrive must be identified as quickly as possible and either deleted or changed, all without disturbing the flow of other phrases still accumulating. Otherwise the book, or manufacturing process, or financial transaction, might never be complete.

You still with me? As unlikely a scenario as this might seem, it’s exactly what the Starview Enterprise Platform is capable of. The technology of our platform can be used in just about any scenario where rapid analysis and decision-making on data in motion is required. Stay tuned.

Wherever you stand on the political spectrum, it’d difficult be to say that Big Data did not play a pivotal role in the nation’s election just under a month ago. From The New York Times’ Nate Silver’s spot-on prediction of how the electoral votes would play out (based on deep analysis into information and trends), to the Obama campaign’s sustained use of information analysis to stay on the edge of trending topics among voters, the real winner this election year is data—mined, evaluated and acted upon.

Operational Intelligence works in similar fashion: massive amounts of data is gathered, analyzed and acted upon, all in real time. Obama’s “ground campaign”, which was adjusted according to constantly streaming feeds of information tapping into public sentiment and changing news events, worked according to the same principles as those that guide the way Big Data is used for Operational Intelligence in highly-sensitive, enterprise situations.

Whether you’re in the oil and gas industry and you’re looking into the data delivered from an immense network of pipelines and transportation hubs in order to determine the best way to quickly fix delays caused by unforeseen problem at one of your oil refineries; or you’re a candy maker looking to ensure not one piece of chocolate in your massive production facility gets wasted due to machinery malfunction, aggregating, analyzing and acting upon huge streams of disparate data as it occurs is more important than ever.

The devastation wrought on the northeast by Hurricane Sandy last week is still being tallied. As awful as the impact has been on the millions of people affected, the lives lost, the homes destroyed, and travel plans disrupted, it’s becoming clear how much worse the situation could have been. While it’s hard to imagine a storm stronger than Sandy, amazing feats in technology, from incredibly accurate models to smart grid technology, enabled officials to keep citizens informed, to act proactively and close public transportation networks and other utilities, and to evacuate people in low-lying areas. Technological advances in Operational Intelligence will also speed recovery efforts — estimated to cost upwards of $50 billion.

What does all this have to do with Starview? A lot, actually. The prediction model used to anticipate the direction, impact and effects of Hurricane Sandy is not at all unlike the kinds of models that can be created and run to test a range of scenarios using Starview’s Active Analytics Platform. As variables in the real world change (a storm picks up speed, changes direction, etc.), then those same variables would instantly be adjusted by the Active Model—an essential component of our platform—to update the prediction model according to the latest conditions. The difference is that hurricanes move relatively slowly in comparison to the events that the Active Model can be used to predict, respond to and adjust for in the enterprise as they occur. Imagine using the same capability of prediction models in your enterprise to respond to rapidly changing events such as customer usage patterns / network traffic versus quality of service and then doing something about it in real time.

While most of us have never seen a storm of such epic proportions as Sandy, climate change models say that this hurricane most certainly will not be the last of such epic proportions in our lifetime. Regardless of where you stand on climate change, these models are worth examining. Besides the information they provide, they are also analogous to the kind of insight that Active Analytics and other associated technologies (such as Raytheon’s anomaly detection software) can provide.

Plus, thanks to the machine-learning and automated decision-making capabilities present in Active Analytics, these processes will not only become more efficient over time, but can be adjusted and improved in real time. How’s that for silver lining?

Back in August, Harvard researchers reported that they had coded the entire contents of a book—including images, text and charts—into a genome, saved it, and successfully uncoded it back to its original format. Basically, what was once able to be saved on a floppy disk can now be saved on a single strand of DNA. Those terabyte portable drives? The data contained within one—entire libraries of information—could one day be stored in a test tube, or a petri dish. This is exciting on a number of levels. Just in the last few months, huge strides have been made in the ways organizations are utilizing Big Data—and it’s not limited to breakthroughs in storage. Processing, analyzing and acting upon massive amounts data in motion is just as big a challenge—if not a bigger challenge—than storing Big Data. A lot of the breakthroughs that have to do with handling Big Data in motion (another way of describing Operational Intelligence)are coming from areas that, like the DNA experiment, aren’t initially what come to mind.

Here are a few:

• Soccer: Adidas has introduced the miCoach, which monitors a player’s vitals, as well as his or her statistics in action, and instantly reports the information back to coaches. This is Big Data, in real time, on real human beings—imagine the implications if we all were fit with such monitors.
• Thermostats: “Smart” thermostats by Nest Labs are being used to help people save energy and more efficiently heat and cool their homes.
• Police Departments: The NYPD other urban police departments are using Big Data to map and cross-reference crime patterns around places, events, and other variables. I anticipate that taking this information and making it actionable in real time can’t be far behind—allocating law enforcement resources based on hot spots, time of day, and frequency of calls; tracking efficacy in response times; and measuring the best (and worst) possible outcome for crisis situations/in advance. This is Operational Intelligence at work in the world.
• What’s next? How else can Big Data be harnessed through Operational Intelligence to make the world a better place? We’re working on the enterprise side…what’s happening on your side?

We talk a lot around here about the speed at which the world operates. We also talk about the speed at which Big Data is multiplying and the speed at which businesses are responding to those trends. When we debuted the Starview Enterprise Platform last fall (now called the Starview Active Analytics Platform; more on that later), it was the only complete and packaged Operational Intelligence platform in existence. The platform itself remains revolutionary, and as far as we know, is the only complete solution of its kind that allows for collection, analysis, and action on rapid-moving, massive amounts of streaming data in real time, as well as the ability to run trial scenarios and build applications to meet the specific demands of those scenarios should they arise. We felt that Active Analytics more accurately describes these capabilities, and made the change. At the same time, the term Operational Intelligence has come to have greater significance in the Big Data market that is Business Analytics & Optimization (BAO). While they are similar, in an effort make what we do more clear (just as we strive to make Big Data more digestible and more actionable for the enterprise), we’ve chosen to pin our banner to Operational Intelligence.

Why? The insight Starview provides its customers is just that—intelligence. The Starview Active Analytics Platform is designed not just for analysis, but action. Automated intelligent action, action that is informed by experience or simulation, has been possible for a while now. But never before has it been possible in real time without human intervention. However, we’re not the only ones using automated intelligent action the way we do—we’re just the only ones using it all at once in the enterprise environment.

What about the consumer environment? Already Google is testing self-driving cars using pre-programmed decisioning that operates based on the stream of real-time information it gathers as it goes about road conditions, other drivers, traffic lights, and so on. (Despite the many vehicle code violations that I’m sure will be adjusted when the time comes that the world is ready for real-time Operational Intelligence to hit the roads).

What’s next? I can’t wait to find out. As more automated, intelligent solutions are made available to the public, it will only be easier for people to understand the incredible capabilities Starview—and companies like us—are already offering the world.

What is the true value of Big Data and Business Analytics?

Better insight and strategy is definitely one answer.  But we believe that value needs to be measureable, so we focus on hard numbers – whether that is in increased revenue (from higher value services for example), reduced costs (from better operating efficiency) or higher customer satisfaction (more repeat business, or lower churn).

Some of the best applications for delivering a measureable Return on Investment are the ones where there is a need for rapid, Real-time Analytics, and action based on Big Data is obvious.  Some examples are:

• Network Optimization in Telecommunications
• Real-time Trade Analytics and Intra-day Risk Management in Financial Services
• Monitoring and protecting high value assets in Oil and Gas
• Highly reliable distribution networks in Energy and Utilities
• Continuous asset scheduling in Transportation Logistics
• Dynamic Capacity Optimization in Discrete Manufacturing & Supply Chain

But what about some other less obvious uses for Real-time Analytics?

Lately, I’ve been thinking about election results.  Though I try to stay out of the fray, I do know that we are in an election year. The pundits and pollsters are off and running with daily, even hourly, predictions as each state checks off another voting day in the Republican primaries. All this talk of numbers and aggregating data on everything from voter demographics to tallying votes come Election Day, is easy to dismiss. After all, primaries are just that—primaries—and according to recent statistics, the very early ones have little bearing on the final outcome and selection of a candidate. Unless of course, you are the one tasked with sorting and analyzing the massive amounts of seemingly unrelated data. Then, it becomes a very fascinating study indeed.

How, you might be thinking, could Real-time Analytics be used to improve elections? After all, politicians never seem to do anything fast.  Well, it turns out there are some pretty interesting election requirements.  Let’s start with electronic polling. In the 2008 presidential election, more than 130 million people turned out to vote. That is a massive amount of data generated over a short period of time. What happens if a voting machine is unreliable? Or if in the influx of data, statistics from a specific municipality are suddenly “lost,” as happened recently in Florida?

When you have the right Real-time Analytics tools in place, all of the data from reporting precincts nationwide can be aggregated. And thanks to Starview’s  one-platform approach, data can be analyzed at a much faster rate than traditional methods that require numerous systems: one to collect and store data, another to analyze it, another to report on it, another  that makes a decision and orders an action based on the analysis should any problems arise, and still yet another  that triggers that action. With Starview, this all happens within one system.

How would that one system know what to do with highly-specific election data? Well, months before the election, the system would be configured with analytics and rules that are required to identify and respond to issues relating to the election data. In the case of a platform designed to optimize the way votes are tallied and ensure nothing slips through the cracks, engineers could customize the rules to handle scenarios such as: “If a machine in Kansas shows signs that it will fail, then another machine can be delivered to replace it before it fails and the appropriate administrator will also be simultaneously alerted to the change.” This is a highly simplified example, but it gives you an idea of how such rules work. Coding like this in Java can be slow and painful, but Starview’s Enterprise Platform comes with the Star ™ language.  Star is a high level language that provides easy to use semantics for processing continuously changing data streams.  For engineers tasked with Big Data and Analytics projects, it is very easy to adapt to.  Star™ allows for “pattern matching” and “If/Then” rules to be programmed in an easy and simple way. With Starview, it’s possible to take the various brands of voting machines and software that exist nationwide and acquire and transform the data in to a common Active Model that represents election processing world view.

Wait, you say—if it’s not broken, why fix it? Well, as more and more voting locations embrace computer terminals instead of punch-card terminals, more variables will be introduced.  With Starview, the platform can be adjusted accordingly and quickly.

As voter information becomes digitized, a one-platform system serves as a safety net.  Rules can be customized to weed out voter fraud, such as duplicate voter registrations, people voting on behalf of deceased persons and other scenarios that undermine the efficacy of a democratic system.

Starview’s Enterprise Platform is perfectly aligned to maximize on the many opportunities that are sure to arise in this rapidly changing, data-drenched world. We’ll continue to delve into these possibilities from time to time. In the meantime, feel free to challenge us in the comments section.

We’re asking you: Name a scenario and let’s see if Starview can help solve, or improve it.

With gas prices rising and little end in sight, green energy is getting more attention again. While many folks are keen to debate policy and what energy independence means, in the world of big data and analytics, we can’t help but think of the informational implications that any changes to the energy industry as we know it might bring. And it’s a lot closer than you think.

Just this week in southern California, the Los Angeles Department of Water and Power announced that an enormous solar power facility in the Mojave Desert is set to come online this summer, ahead of schedule. And, while a proposed hydrokinetic wave farm off the coast of Los Angeles, which seeks to generate electricity from the ocean waves, suffered a big setback this week, it’s only a matter of time before some very smart engineer figures out a way to harness the ocean’s power without compromising the environment or federal and state standards.

The advent of renewable energy sources is exciting. Yet it also poses huge challenges for those tasked with managing each source of electricity and allocating that energy accordingly. With generators based on solar power and wave power, variables (such as the night and cloudy days or low tides and storms, respectively) can shift dramatically. If the energy grid is not set up to deal with such scenarios—to predict them in advance, and adjust energy allotments within limited temporal margins—black outs and other disruptions can result.

This is where a Business Analytics & Optimization Platform (BAOP) comes in. The great thing about BAOP is that it’s the way of the future, now. Just as cities are preparing for the potential green energy brings to their infrastructure, they are also considering ways to manage the enormous amount of data that comes with these new energy sources; data that needs to be analyzed and acted upon and possibly even stored for limited amounts of time. We’ve got the technology to handle exactly these kinds of scenarios, available now.

Are you listening, Los Angeles? Props on the new solar project—we look forward to not just the problems, but the wonderful solutions it’s sure to bring.

Recently, Jimmy Kimmel challenged viewers of his show to wait for a crucial moment during the Super Bowl, then unplug their TV sets and record the reactions of spouses, family and friends as the screen went black. The playback was hilarious for Kimmel viewers, though the collective response from the victims of the prank was less than jovial.

At least those fans were able to plug their sets back in and resume watching; residents of a small New England town were not as lucky. They lost power during the first quarter and didn’t see it restored until the game was nearly over. The Public Service Company of New Hampshire (PSNH) would later explain that a faulty underground cable caused the outage that resulted in 300 Patriots fans missing the bulk of the Super Bowl.

PSNH may have saved the game if it had leveraged Business Analytics Optimization (BAO). BAO encapsulates expert organizational knowledge and insight and automates real time decisions at the point of greatest impact, based on real time fluid data. With the new platform approach to BAO, known as BAOP, companies have a unified, cohesive view with “actors” replicating each and every system, device and touch point that can affect the business process. In the case of PSNH’s faulty cable joint, an actor would have detected a maintenance issue based on pre-determined factors, and already-established actions could have then addressed the problem by rerouting data or alerting maintenance crews well in advance of an actual event.

We hope that, in the future, more utilities will have solutions in place that enable them to take action preemptively so that no sports fan has to endure what the residents of Rye, New Hampshire went through. Their lights came on just in time for them to see the Giants score the game-winning touchdown.

Novelist Robert Harris was recently interviewed on National Public Radio about his new book, The Fear Index. The premise of the book was inspired by the surprise—and automated—crash the financial markets took in 2010 after a series of computer-generated trades set to trigger according to certain market scenarios set off a domino-effect of frantic selling. While this kind of occurrence is rare, and the markets recovered rather, it touches on a much larger topic of concern—and fascination—to anyone who works in predictive analytics solutions, automated decision-making, event-driven architecture, or other aspects of the Big Data phenomenon that drives the way much of the world’s business is conducted today.

Harris’ novel takes the fear surrounding the financial markets, and the power of a computer run amok, and spins a fantastic thriller around it. While this is the stuff of fiction, it’s not that hard to imagine. What struck me as particularly poignant to what we do at Starview—and to automated decision processes and operational business intelligence software like that made possible with the Starview Enterprise Platform—was the power that Harris affords computers. And we’re not just talking about robots run amok in some AI-like fantasy. Artificial or not, intelligence is a powerful thing. And while it can be scary, it’s also incredibly inspiring and affirming to work in a field that is also improving businesses—and having a positive effect on not just processes, but profits—in the meantime.