Information Management Blog

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Blog Feature

Artificial Intelligence  |  automation  |  data  |  digital transformation,  |  machine learning

Can Machine Learning Defeat the Venerable Structured Template?

The venerable template allows structured form data to be accurately extracted. In the document capture industry, the concept of templates where you specify the location of each data element is a tried-and-true strategy for structured forms. If the form is standardized, giving the software the precise place to look for data will almost always result in better performance over alternatives such as rules-based approaches using keywords or patterns. Even with unstructured data such as on invoices, we find that many organizations have opted for a template approach after finding that more flexible, rules-based approaches fall short. The result is a tremendous amount of upfront effort and a lot of maintenance.

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Blog Feature

Artificial Intelligence  |  automation  |  data  |  digital transformation,

Document Automation: Having Your Cake…

Having your cake and eating it, too, is a proverb that’s almost 500 years old, which means you cannot have two incompatible things at the same time. So many examples of situations exist where you face two mutually exclusive options. Let’s take document capture. Document capture software is designed to automate document-oriented tasks such as sorting or extracting key data. In order to achieve that automation, you must spend time to configure the software to identify documents and reliably locate and extract that data with a high enough degree of accuracy that your staff need only be involved in verifying a small percentage of it.

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14 Steps to a Successful ECM Implementation

Making an ECM implementation successful requires planning and attention to detail. The best way to create the right solution is to identify organizational goals and priorities. Learn how to manage a successful implementation in our free guide.

Blog Feature

big data  |  data  |  digital transformation,  |  information professional

Four Ways the Data Scientist Has Evolved to the 21st Century

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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Blog Feature

analytics  |  automation  |  business transformation,  |  data  |  digital transformation,

Guest Post - Digital Transformation: Adapt Your Business Environment

“Digital Transformation is a game changer,” and “leaders embrace digital transformation”—but is it really a game changer and have we actually embraced it?

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Blog Feature

big data  |  data  |  data analytics  |  ecm  |  semantic web

4 Data Points that Should be on Every Insurance Executive’s Radar

  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.

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Blog Feature

AI  |  Artificial Intelligence  |  analytics  |  automation  |  data

Guest Post - Artificial Intelligence: Huge Opportunity or the Zombie Apocalypse?

What worries us most is often not what actually gets us—that is, causes us our biggest problems. The enormous human capacity for worry can result in productive focus and taking action. Artificial Intelligence (A.I.) inspires our imaginations, invokes our worst nightmares and touches our deepest fears. Near-term, it threatens to take away our jobs and leave us with no way to feed our families.

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