In the second of a two-part series, we dive into what exactly location-based data can unlock, the reality that coronavirus may be here to stay, and what organizations can do if a data-overhaul is not an immediate, or near term, possibility.
Last week, in part one, we examined the critical role that location-based data plays in an organization’s response, planning, and reaction to crisis situations. One of the ramifications of the COVID-19 pandemic being that it is a location-by-location challenge, specific geographical information can be key in navigating the patchwork of the United States’ federal response to the outbreak.
This is the dark side of federalism: it encourages a patchwork response to epidemics. States and localities may decide to implement aggressive disease-mitigation measures, but need not do so. The defining feature of the U.S. response to Covid-19 therefore continues to be localized action against a threat that lost its local character weeks ago.—The New England Journal of Medicine
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In the first of a two-part series, we examine the critical role that location-based data plays in an organization’s crisis response efforts and how compounding crises lead to an even more immediate need.
While initial outbreaks of COVID-19 hit densely-populated, urban areas of the United States the hardest, the coronavirus is now beginning to surge across less populated parts of America.
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Over the past 4 years, 3 of the 4 major Risk Management Information System (RMIS) vendors have been acquired by private equity investors. That amount of consolidation, in what was previously a very stable market, really is astounding. What does vendor consolidation mean for RMIS users? And more importantly, what does it mean for you and your team?
The impact of RMIS market consolidation is almost certain to rank low on any risk manager’s list of concerns. There are more critical issues on which to focus—renewals in a commercial property insurance market that continues to harden, adequately evaluating and insuring against cyber risk, taking a more proactive approach to loss prevention, or developing an ERM program, to name just a few.
Those who depend on their RMIS for basics such as consolidating a handful of TPA feeds and reporting on claims data, RMIS mergers and acquisitions may barely register. For risk managers and their team members who use their RMIS on a day-in and day-out basis, the potential effects of consolidation—in particular, the ability or willingness of a RMIS vendor’s owners to invest in improvements in technology and support, rather than preparing for future sale—are worth considering.
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The onboarding process can be challenging for both TPAs and their clients. Migrating data from one claims management system to another is often a difficult, resource-draining part of that process. Wesley White’s article 10 Data Migration Best Practices For Any Organization summarizes the extent of the challenge:
Migrating data to a new information management system from multiple sources is a complex and often headache-inducing undertaking. Data migration is often necessary to keep up with technological advancements and industry standards, but it requires great effort. Data from various storage areas—both onsite and in the cloud—must be evaluated, analyzed, cleaned up and organized before it can be combined and reconciled.
The right technology can help to reduce the tremendous burden that data migration places on new clients. It can also transform the onboarding process and showcase the unique insights, savings, and benefits your organization delivers. As White notes, “It doesn’t have to be as hard as you may think to get past these challenges and successfully migrate your data.”
Assisting with the pre-migration phase
Research from the independent research firm Bloor paints an ominous picture of data migration projects. Of these projects, 37% exceed budgets, 67% take longer than expected, and 84% fail to meet expectations. In Why do so many data migration projects end in disaster?, Colin Rickard, a data management director with Experian, is asked to explain the high failure rate. “Often there has just not been enough analysis done at the start, so you end up with a lot of data problems at the end,” he responds.
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The number of core systems an insurance risk pool uses can have a major impact on the level of service that members receive, as well as the pool’s ability to make the best use of staff resources. Constantly jumping between multiple systems and trying to coax Word and Excel into accomplishing tasks they were never designed to handle is a recipe for performance issues. This can limit a pool’s growth and the types of services it can provide.
Activities most impacted
While the inefficiency of using a patchwork of applications to handle core business functions cuts across a wide variety of routine tasks, several activities performed by risk pool staff are particularly susceptible.
Calculating loss ratios
Assembling the information necessary to calculate loss ratios often involves building multiple spreadsheets and transferring data from several sources via copy/paste. This highly inefficient process is prone to errors. According to the ECRI Institute report, Copy/Paste: Prevalence, Problems, and Best Practices, the familiarity of the copy/paste technique explains why it is used so often. “However,” the report warns, “with several windows open, information can easily be copied into the wrong location. Secondly, copy/paste accelerates propagation of inaccurate information. The ubiquitous use of copy/paste means that, once created, an error can rapidly spread.”
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It seems as if everywhere you turn, someone’s referencing AI. “Artificial intelligence is essential for business,” states a news article. “AI is the only way forward,” insists a colleague. And the scariest of all: “If you don’t implement AI quickly, you’ll be left in your competitors’ dust.” The constant refrain of AI, AI, AI can leave you feeling like your organization has lost the race before it has even entered it.
The truth is, AI is changing the world at a remarkable pace. And, eventually, nearly every industry and business will benefit from it. “Whether you work in retail, banking, transport or the public sector, AI will be an integral part of the way you do business in the future as it has huge potential to improve decision-making, increase efficiency and power new ways of working,” states the article How to Get Your Business AI-Ready.
But that doesn’t mean AI is the best solution for your organization right now. Implementing AI takes a massive commitment in the form of time, resources, and money. It requires a critical mass of data and properly trained staff. By prematurely jumping into a high-profile AI program, you risk ignoring the valuable tools already available and stalling other strategic projects underway. Instead, a more practical approach—one that uses software scaled to your operations—will move the most important metrics now, while developing an analytics culture that will make AI more feasible down the road.
AI has become a buzzword. What exactly does it mean?
In its prolific use, the meaning of artificial intelligence has become skewed. Some have come to view it as a magical solution capable of instantly transforming business all on its own. Others equate it with automation. Neither of these is true, however. AI requires much preparation and strategy (more on that later), and where automation follows pre-programmed rules, AI involves machine learning. AI is designed to mimic human thinking by making predictions and adjusting its processes based on new data insights. In this way, AI is quite different from many previous technological revolutions, during which technology took over specific, static roles within processes.
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Organizations often miss a crucial step in their drive to acquire and implement technology as a means to remain competitive. The ability of commercial insurance brokers to leverage data and analytics to bring in new business, write policies, and provide added value to their clients is about more than selecting the best risk management information system (RMIS). To get the most out of the investment in technology and become a digital leader, a brokerage should first assess if essential foundational elements are present.
The next-level broker
A next-level brokerage is a firm that has undergone the process of digital transformation, a term that CIO contributor Mark Edmead defines in Digital Transformation: Why its important to your organization as “the acceleration of business activities, processes, competencies and models to fully leverage the changes and opportunities of digital technologies and their impact in a strategic and prioritized way.”
An anonymously attributed response to the Commercial Property/Casualty Market Index (Q4/2018) survey question, “What opportunities for commercial insurance brokers do you see?” can be also be read as a more specific description of the next-level broker. He or she is able to “maximize use of technologies and analytics to grow business and do so with reduced expenses.” Furthermore, the next-level broker has the “[i]ncreased ability to target growth in select industries via use of data and analytics.” Finally, he or she is able to “[i]dentify new ways via technology and through the use of data and analytics, to solicit, write, and service business.”
Analytics functionality is an essential component in the digital transformation into a next-level brokerage. However, the act of putting a RMIS in place (or modernizing an existing system) doesn’t mean that all expectations around analytics will automatically be met. The right mix of people and data must also be present.
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Data silos not only create obstacles to effective operations, but can also directly affect your bottom line. Listed below are five common issues associated with siloed data and ways to avoid them.
1. Creates a dependency on inefficient external reporting applications
Multiple platform architecture complicates the reporting process. While third-party reporting tools can be used to analyze data across multiple systems and produce unified reports, there are costs incurred. Forrester reports that nearly half of all data professionals spend at least as much time prepping data as they do analyzing it. This inefficiency worsens in cases where reporting reveals a need to modify how data is captured or organized, forcing analysts and IT resources to trace data all the way back to its original source and then make changes.
In some cases, third-party reporting tools can also create a gulf between those who master the reporting technology and those seeking answers from the reports. In a recent interview, Christopher Ittner, chair of the accounting department at The Wharton School, discussed how this division affects the business process:
“What we are finding is that in a lot of companies, there are great data scientists and great business people but what is missing is business people who know enough data analytics to say, ‘Here is the problem I would like you to help me with.’ And then they can take the outcome from the data scientists and see how they can best leverage it. That is where we must get to in the next couple of years if we want to take advantage of the digital technologies.”
Providing users with direct access to reporting that requires no prep work solves both issues. End users can become their own data analysts and answer the business questions that apply to their work. Without the requirement to master the technical process of assembling, scrubbing, and joining data from multiple systems, reporting becomes more efficient, effective, and scalable.
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Whether you’re an organization whose business is handling claims for others or one that administers its own claims, claimants are your customers. Viewing claimants through this lens will help focus your efforts on strengthening relationships and delivering better support. You also have the opportunity to go a step further and establish your reputation as truly customer-first. How? Through a straightforward branding exercise.
Before dismissing branding as something far removed from the claims world and better left to marketing and advertising executives, consider that every customer interaction further establishes an organization’s brand. Your reputation for customer service — however good or bad — is out there. You can continue with the status quo, or you can take control and push the narrative.
“Think about it,” says the Insurance Thought Leadership article 3.5 Ways to Deliver Happiness in Claims. “The claimant is going through your process during a time of grief, hardship and huge loss. Your process should not add to the stress. Your process should be easy. It should work to deliver a little happiness for them during this time. You want your beneficiaries to tell stories to their friends, family or other loved ones about how seamless your process was.”
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Data Breach Today offers predictions in What’s Ahead for Health Data Privacy, Security in 2019? While the article focuses primarily on health data, a few key trends apply more broadly and are likely to resonate with all types of organizations.
Prediction: Disruption from regulatory changes is likely
Rebecca Herold, author of 19 books on information security and CEO of The Privacy Professor consultancy, begins the list of predictions by examining the potential for agency updates to HIPAA. “Based on continued pressure from local, state and federal government agencies, law enforcement, researchers and others to ease the sharing of patient and mental health data by removing the need to obtain patient consent, I expect to see OCR issue proposed HIPAA updates,” she notes.
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