All posts by Benjamin Baer

Analytics & Optimization How to Innovate Like a Start-Up with Decision Management


For decades, businesses have been leveraging technology to automate large parts of their operations – accounting, manufacturing, supply chains and customer relationships. In the most recent chapter of this story, business and government agencies have been doing amazing things with cloud computing, Big Data and analytics. However, it is estimated that 85 percent of organizations are still unable to exploit Big Data for competitive advantage. There is a gap between the promise of Big Data and the reality. While there is indeed a ton of available data, it’s still hard for most companies to parlay it into useful insights, let alone operationalize it within their business decisions. After all why should you bother with analytics without actions? Today we are introducing a major leap-forward in the practice of prescriptive analytics and decision management. The latest version of FICO® Decision Management Suite provides everything businesses of all sizes need to rapidly develop innovative... [Read More]

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Analytics & Optimization Origination by Any Other Name


For many years FICO has been an industry leader in consumer and small business credit origination.  We’ve developed a number of analytically driven applications to speed the time from customer application to close. If you’re in the financial services business, you probably are very comfortable with the term origination.  If not, you may not even know what the term means. Origination may be unique to banking and credit, but it’s also a term that virtually every business in the world is challenged with whether they call it origination, new account opening, onboarding or something else.  In a nutshell origination means to bring a new customer onboard or establish a new relationship. For example, you apply for a loan.  You submit the necessary paperwork – tax returns, paystubs, bank statements, etc. – and the bank determines your credit worthiness and the terms of your loan. For other industries, the origination process could include... [Read More]

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Analytics & Optimization Big Data: Should You Collect it All?


A few years ago, following his success in predicting the results of the 2008 election, poll analyst Nate Silver wrote a book called The Signal and the Noise.  The book outlined his approach to understanding the limitations of polling to represent an elections ultimate outcome.  By better understanding what and to whom pollsters ask questions, he devised a methodology to focus on polls that, in aggregate, best represented the voting electorate and, more importantly, avoid those polls that historically skewed their results to benefit particular constituencies. The title of the book best summarized the lesson:  Identify the meaningful signals and avoid the noise.  This is precisely the problem that Big Data presents to analytic professionals (and non-professionals) today. In a world where you can collect everything, should you? Probably not. Recently, ZDNet’s Stiligherrian wrote an article in which he discussed the Big Data “collect everything mentality” that grounds many Hadoop... [Read More]

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Analytics & Optimization Finding Patterns in the Random Holiday Season


As we settle down with our families to enjoy the winter holidays, many of us will be celebrating various traditions. For many, the culmination of the season is waking on December 25th to the excited cheers of their children as they see their Christmas horde waiting under the tree.  These children believe that Santa Claus, despite carrying what must be a massive load, manages to fly on a sleigh powered by eight reindeer to every home on the planet in one night. The jolly old elf climbs down the chimney and places each present carefully underneath the Christmas tree.  The children wake up on Christmas morning and, ta-da, there are presents. As it turns out, there is a word for this belief:  Apophenia.  According to Wikipedia, Apophenia is defined as the experience of seeing patterns or connections in random or meaningless data. Apophenia was first used to define the “unmotivated seeing of... [Read More]

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Analytics & Optimization Machine Learning and the Terminator Apocalypse

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In the movies, the machines will become our enemies. In The Terminator, the rise of the machines leads to an apocalyptic future; in 2001: A Space Odyssey, Frank is murdered and Dave is locked out of the spaceship by HAL; and in The Matrix, humans are used as a power supply after the sun is blacked out. No wonder we’re a bit afraid of the machines. While these machine-controlled futures are rather unrealistic, many businesses today are in danger of an increased reliance on machine learning as the end all, be all of data analytics. Let’s face it, data science talent is scarce. And as more businesses are forced to consider full integration of analytics into their decision-making processes, a machine learning-only solution with limited human oversight is tempting. But this approach exposes us to potential risk … eerily similar to those in the movies. Here is how the scenario... [Read More]

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Analytics & Optimization Why We Built Our Own Analytic Cloud


Not too long ago, deploying advanced analytics required massive investments in IT infrastructure and application software, creating a sort of “analytic elitism.” Over the last year we’ve been focused on democratizing analytics, so that organizations of all sizes can base their operational decisions on data. And to do this we needed to build our own cloud infrastructure.

But why? Why did we decide to build our own infrastructure rather than leveraging a hosted provider like Amazon Web Services (AWS)? This was hotly debated within our company and essentially it came down to three very important reasons:

Cost: While AWS, Microsoft Azure, the Google cloud and other cloud hosting providers garner significant attention and mindshare, first and foremost their value is speed of resource delivery. When and if a customer needs server and computing resources, the network for these services can be deployed and accessed in very little time. Speed is a critical value for hosting service customers. By partitioning and deploying compute resources in minutes or an hour...

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Analytics & Optimization Big Data Analytics and Decision Management Set to Revolutionize the Customer Experience


  By Benjamin Baer How did the worst attended Major League Soccer (MLS) team turn it around, now packing an average of 19,709 people into its stadium that seats 18,467, with a waiting list for its 14,000 season tickets? It did it by turning insights (data that told it what was keeping its fans away) into actions (making its stadium feel more like a couch). Sporting Kansas City developed a popular mobile app, which turns a fan’s smartphone into an in-stadium DVR, delivering the advantages of watching soccer at home to its fan base attending its games. The app also gathers reams and reams of information on its fans by tracking their activities inside the stadium – with their approval of course – which enables it to continue improving the fan experience. Sporting Kansas City serves as good microcosm for extrapolating, more broadly, how savvy businesses will be able to use Big Data analytics and improved decision-making to revolutionize the customer experience.  Today Big Data analytics and decision management technology is helping to drive...

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Analytics & Optimization Has Business Intelligence Hit the Big Data Wall?


Analytics is becoming a competitive necessity to businesses. Knowing what decisions to make, when to make them, and how to optimize them for maximum impact is the difference between an industry leader or an also ran.  Whether the decision is about pricing, packaging (it’s only within the last ten or so years that technology companies have considered the color of the new PC or consumer device), new market penetration, or new products, it’s about making business decisions consistent with well-articulated and understood strategies backed up by data.  Today, many businesses are turning to tried-and-true Business Intelligence (BI) software to solve their analytics challenges. BI is an umbrella term that refers to a variety of software applications used to analyze an organization’s raw data. As a discipline, BI is made up of several related activities, including data mining, online analytical processing, querying and reporting. But when it comes to Big Data, is BI hitting a wall? While many traditional BI vendors have moved to adopt clustered data...


Analytics & Optimization Myth Busters: The Analytic Talent Crunch Will Constrain Big Data Innovation


Earlier this month, we posted our first in a series of myth busters, inspired by the Discovery Channel’s television show MythBusters. Over the next several months we’ll tackle hot topics related to Big Data, analytics, customer engagement and mobile technology and we’ll determine whether the topic can be confirmed, is plausible or is busted (not true).  Thomas Davenport and D.J. Patil brought the data scientist into the national spotlight in last October’s Harvard Business Review article titled Data Scientist: The Sexiest Job of the 21st Century, and reported that job postings for analytic scientists have jumped 15,000 percent between the summer of 2011 and 2012. Many believe the “shortage” will only get worse. McKinsey & Company predicted a 50 to 60 percent shortfall in analytic scientists in the US by 2018. And Gartner echoed this sentiment predicting that only one-third of 4.4 million global big data jobs will be filled by 2015. By definition we have a talent crunch on our hands. But will it constrain Big...

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