Guest Post: Chris Checco, Razorsight President & Chief Analytics Officer – “Put Your Big Data to Work”

Chris CheccoChris Checco serves as president and chief analytics officer at Razorsight, the maker of cloud computing-based analytics tools for providers and other customers in the telecommunications industry with headquarters in Reston, Va.

The more-than-16-year senior level executive joined Razorsight in 2012 after serving as lead management scientist for public sector analytics at Accenture, where he helped design and launch that firm’s public sector analytics practice.

Checco earlier served as division president for advanced analytics at Agilex Technologies and developed analytics solutions for telecommunications customers at DHSoft Technologies. He also managed profit and loss for Agilex’s civilian business.

Checco earned an MBA in international business from Georgetown University and has awarded and pending patents for systems and methods in the areas of risk, threat and data assessment, and situational awareness.


By Chris Checco

With today’s round-the-clock hype, it’s popular to cast Big Data as a natural disaster. To hear some experts talk, Big Data is bearing down on us like a blizzard, threatening to bury budgets and smother productivity.

Big Data has nothing to do with Mother Nature. A better analogy might be that, for many, Big Data is like a sleeping giant. Problems stem not from the data itself, but from reliance on platforms that were never designed to prod the “big guy” into action. Many so-called “Big Data Analytics” platforms are, in fact, just old school business intelligence (BI) and data warehousing (DW) systems left over from the 1990s.

As with any typical BI/DW system, asking questions requires some hypothesis to start with, hence the questions asked just prompt more questions, rather than provide answers.


For Answers, Try Advanced Analytics

razorsight logoYou don’t have to put up with inadequate platforms. There’s a better alternative: advanced analytics apps expressly designed to probe Big Data for answers that immediately address your most important objectives.

One field in particular – the emerging practice of predictive analytics – takes Big Data to the next level. Predictive analytics is unique. It provides the capability to project precise outcomes based on behavioral trends for groups or even individuals.

Companies of all types are now using predictive analytics to know where the market is headed, shelve unproductive products and campaigns, develop new products tailored to customer interests, invest appropriately in marketing, operations and network build-outs to ensure a positive result – and, in the end, boost higher customer satisfaction and loyalty, and improved financial performance.

Can government agencies do the same? They can.

While the immediate comparison of profit-and-loss enterprises to government may not seem on-target at first, there are strong corollaries. How government might use and benefit from advanced analytics:

  • Cost savings. In the current economic environment, a key concern of all departments is cost over-runs, both current and future. Traditional analytics can spot problem areas once those problems have occurred, normally in a one-off fashion. Predictive analytics goes several steps further, letting the user identify a variety issues before they occur, based on the behavioral patterns revealed from deep within the data. Armed with this information, an agency could act preemptively to curtail the potential for waste and abuse or cost increases before they happen.
  • Innovation. In the commercial arena, companies routinely use predictive analytics to inform product development based on hidden usage patterns, and to reveal cross-sell and up-sell potential, by customer sector, geographic group or the individual. The same could apply to government. For example, analytics might spot an error in tax forms that confused taxpayers in 2012, reduced returns – and is on track to cause an increase in processing time and cost for 2013. The fix: a streamlined form and education. In another scenario, visitation patterns at the Grand Canyon and other national parks could clue parks into human and operational resources. Result: lower carrying costs and more efficient resource allocation.
  • Customer Experience Management. For government agencies, “customers” are citizens whose votes for elected officials can directly impact annual funding. Here, the customer experience is every bit as important as in industry, and analytics can play a central role in ensuring satisfaction. Case in point: The individual checking out a platinum “metal plan” under the new Affordable Care Act is more apt to come away happy if the system assimilates, in advance, her past spending levels on health insurance, any recent changes in health history, and her likelihood to quickly blow through a high deductible.
  • Fraud Detection and Prevention. In the telecommunications industry, the cost of fraud annually tops over $40 billion worldwide. While that figure represents a comparatively small percent – 2% — of total $2 trillion industry revenue, it adds up. Imagine the savings to government if predictive analytics was deployed to root out and predict potential fraud in key entitlement programs, for instance, eliminating improper payments to unqualified recipients. Social Security Administration Disability payouts might be appropriately sized if the system could identify trends, for example, distinguishing the future truly sick and disabled from those simply out of work and filing for disability to make ends meet.

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