In 1989, I took my first decision sciences course, and started coding in SAS at the age of 20. I greatly enjoyed pulling discoveries buried within mounds of data, although and even small datasets had many discoveries back then. At the root of every model I’ve built, even the simplest, was a solid understanding and foundational rigor of statistical theory. When computing simple statistics or developing descriptive models, I thought through the math behind the model and how this would impact the formation, application, and interpretation. It was about 30 years ago when I started my decision sciences journey, and I’m still applying techniques and building models to empirically solve problems, answer questions, overcome challenges that improve, reduce error, or otherwise benefit a situation. Over the past three decades, I’ve noticed trends and shifts, an evolution of sorts, in the foundational underpinnings of development and application within this interesting profession. I’ve come to the following conclusions that illustrate the evolution of the data science function over the past few decades:
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Solve your Talent Problems with Digital Labor & Content Services Solutions Every industry is facing accelerating disruptive challenges, but perhaps none more so than the insurance industry, one of the original “crossing the chasm" industries for content management. Consider the following data points highlighting the need for a more innovative approach to digital labor and content services: 1) “According to a study by McKinsey and Co., 25% of the professionals in the insurance industry will retire by 2018, leaving an enormous talent and experience gap in an industry that is already experiencing a worker shortage.” Property Casualty 360. Implication: Unless organizations can begin to access and interpret all of the "dark" data they have accumulated over the past three decades, they run the risk that a significant source of knowledge will exit the building when the baby boomers retire. The key to competitive advantage does not rest on simply hiring more workers, but on maximizing the productivity of existing workers through technology. 2) “Millennials will comprise more than one of three adult Americans by 2020 and 75 percent of the workforce by 2025.” Brookings Institution. Implication: Technology expectations in the workplace are about to change dramatically. Organizations with business systems that resemble those that might be found in a computer museum will find themselves at a distinct competitive disadvantage when it comes to hiring the best talent. 3) 57% (of millennials) say it is important to work for a company that provides flexibility to work outside the office. 48% say it is important to work for a company that is “very innovative.” While Gen Z is only slightly more familiar with the insurance industry than millennials, more than 60% of Gen Z and millennial respondents feel unfamiliar with the industry. The Future of Insurance: Bye-Bye Boomers, Hello Digital Natives. Implication: Inflexible systems that are not designed for a modern mobile workforce will further exacerbate the brain drain that will occur as the baby boomers leave the workforce. 4) Only 4% of Millennials consider insurance to be an attractive industry in which to work, with many describing the industry as “boring.” The Hartford’s 2015 Millennial Leadership Survey. Implication: Insurance companies face a perfect storm in the future battle for talent due to: Legacy systems that have not been updated and not taking advantage of the latest cloud and mobile technologies. A continuing set of compliance and regulatory obligations that are more complicated than the average industry and are not going away. An older workforce than average that will soon retire. A generation of new employees that has a negative view of the insurance industry. Based on these data points, it is clear that something needs to change. However, according to PwC’s 2017 Top Issues report, “the industry’s traditional conservatism and the dizzying pace of technological change has made it difficult to change.” This “traditional conservatism” is based, in part, on the fact that insurance companies do face more significant content management challenges than many other industries. Insurance organizations must deal with the reality that business must be transacted at the convenience and location of the customers’ choice, in a digital manner that is secure, accessible, and defensible. Regulatory compliance, customer responsiveness, and operational agility are key to maintaining and growing their customer base. It is also vital for insurers to establish and maintain a solid information ecosystem, based on a strong IG framework that meets or exceeds the regulatory and legal compliance requirements. Most importantly, all of this must be done within a business context of multiple and inconsistent content and process management systems, both “native-born” and acquired. The solution to this complex set of challenges is not business as usual. While there are many parts to the puzzle of attracting millennials to the insurance industry, one part of the puzzle is providing an innovative work environment, engaging technology, and pushing systems to address the emerging set of analytics and machine learning opportunities. While it may seem daunting, attracting millennials is vital for the industry’s success. RhinoDox CEO, Justin Ullman, puts it this way, “Let’s assume that the insurance industry can truly embrace a culture of rapid innovation and attract the talent needed to drive that. While the work ethic of millennials is often questioned, most people can agree that they are innovative, entrepreneurial and purposeful. Put that mindset to work and ask for their best ideas, and there isn’t a better demographic to help a legacy industry like insurance. As long as the right tools, processes and mindsets are in place, improvement is sure to follow.” The mission-critical nature of many legacy systems means you cannot just rip them out without unacceptable carnage. The key to this evolution is embracing low- and no-code process solutions, semantic enrichment, modern approaches to enterprise search, and more flexible document databases to extract greater value out of existing systems. ----- You might be interested in one of my recent e-books -- From Documents to Content to Data. PS. It's free.
Never have the risks of disruption – and the rewards of nimbleness and agility and creativity -- been higher. SaaS solutions to enterprise problems – with far less aches and pains to business types -- have swept through corporate America. For years, Boomers had the benefit of numbers – after all, they still represented the majority of the workforce and dominated the C-Level within organizations. By 2020, those pesky Gen X, Y, and Z folks will be 80% of the workforce. The Boomers – and the institutional knowledge they have – will largely be gone. The question facing many organizations -- How do we create a business technology environment for the millennial majority that looks a lot less like a technology museum? Think about how out-of-control things have gotten relative to the core business challenges of managing, controlling and utilizing information. So how do organizations make sense of all this? How do they begin to update their core infrastructure – and minimize the amount of chaos that will inevitably ensue? How can they optimize the productivity of their human capital? The answer lies in 1) more effective management and utilization of the content and information housed – often locked away -- in existing systems; 2) rationalizing multiple content systems; and 3) moving from legacy to more modern platforms. Ironically, this problem is more challenging in industries that were leaders in the first wave of content automation. In the late 1990s, Enterprise Content Management became a mainstream technology (at least for large organizations) by first focusing on early adoptors eager to automate high-value, mission-critical, and document-intensive processes critical to gaining competitive advantage. Examples of these initial “breakthrough” processes include the new drug application process in the pharmaceutical industry, claims processing in the insurance industry, and check processing in the banking industry. The challenge of modernizing this legacy investment is somewhat akin to trying to change the wiring in a house while the power is still on. Find out more in my new Tip Sheet -- Do New Hires Think Your Organization Resembles a Computer Museum?
[Note: This is a guest post by Andrew Wells and Kathy Chiang. Andrew Roman Wells is the CEO of Aspirent, a management-consulting firm focused on analytics. Kathy Williams Chiang is VP, Business Insights, at Wunderman Data Management. They are the co-authors of Monetizing Your Data: A Guide to Turning Data into Profit-Driving Strategies and Solutions. For more information, please visit www.monetizingyourdata.com.]
11 Cool Quotes About Big Data “Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world.” (Atul Butte, Stanford) "Information is the oil of the 21st century, and analytics is the combustion engine” (Peter Sondergaard, Senior Vice President, Gartner) “War is 90% information.” (Napoleon Bonaparte) "Big Data will spell the death of customer segmentation and force the marketer to understand each customer as an individual within 18 months or risk being left in the dust.” (Ginni Rometty, CEO, IBM) “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” (Geoffrey Moore, author and consultant) “The world is one big data problem.” (Andrew McAfee) “I keep saying that the sexy job in the next 10 years will be statisticians, and I’m not kidding.” (Hal Varian, chief economist at Google) “The temptation to form premature theories upon insufficient data is the bane of our profession.” (Sherlock Holmes, fictional detective) "The most valuable commodity I know of is information." (Gordon Gekko, Wall Street) "If we have data, let’s look at data. If all we have are opinions, let’s go with mine." (Jim Barksdale, former Netscape CEO) “Processed data is information. Processed information is knowledge Processed knowledge is Wisdom.” (Ankala V. Subbarao)