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ent ents In the age of the customer, the CIO is between a rock and a hard place
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Augmented Everything: How a CIO can make workers smarter, stronger, more aware
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Embracing the Challenges of Gaining Value from Analytics
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Big Data used by health insurers to help counteract threat of market disruption
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Blockchain: Challenges, opportunities, and a car in every driveway
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Letter from the Editor
As spring slowly surrounds us, it makes us feel as though we are starting anew, of pressing forward into the future with a renewed outlook and with a new way to make business process better. In this day and age, when we talk about business process, we recognize that in many ways it falls squarely on the shoulders of the CIO—whose role spans all systems and departments, creating the company’s holistic view of the world. It is for this reason that we dedicated this issue to CIOs, knowing the demanding pressures they face every day, and the huge challenges they have to overcome. And for the most part, it is not surprising that the majority of the challenges they face are focused primarily on data. Understandable, considering the amount of data that customers and companies generate, as well as the business intelligence needed to launch the next business venture. From the daunting tasks of collecting, managing and analyzing Big Data, to the enigma that is Blockchain, this new era of business is becoming more and more complicated with each passing day. And then, of course, there are the analytics generated from all these new paradigms—from what to measure, to how, to what KPIs drive critical business decisions. It is the CIO who must navigate through Big Data analytics to access and configure an architecture that drives business forward and that derives a competitive edge. We hope you enjoy this issue of DirectionIT Magazine. Regardless of your role, the insight these articles bring will hopefully help you build a greater understanding of your role and of the people around you.
Allan Zander Editor-in-Chief
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IN THE AGE OF THE CUSTOMER, THE CIO
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IS BETWEEN A ROCK AND A HARD PLACE By Paul Hogg
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Today, customers drive business and battle lines have been drawn. Organizations are now obsessed with their customers and are competing as never before to win, serve, and retain them. According to Forrester, by 2020 every business will be either a digital predator or a digital prey; in other words, as prey a company will be a casualty of the battle to win customers. This business trend has resulted in unrealistic demands being placed on CIOs. As business and marketing will most likely control more than half of tech spending, the traditional role of the CIO has been marginalized; and with new projects designed to win, serve, and retain customers, tech budgets will need to be rebalanced with 50 percent of the tech budget being spent on these new projects. Forrester applies this shift to “business technology� spending as tech purchases designed to influence the customer experience. As a CIO, you must feel as though you are between a rock and a hard place: by keeping up with rapidly changing customer demands, your budget for ongoing operations and maintenance is reduced by about 50 percent. This is a tough situation for you as a CIO. While technology is becoming increasingly important to meeting business outcomes, the role of business in tech purchases is also becoming increasingly important. According to Forrester, the segment of tech purchases that have been made by business across all stages of the tech-buying lifecycle is only at about 7 percent of total US new tech purchases. Therefore, CIOs will work collaboratively with business to drive tech investments. Even though the role of business in tech purchases is increasing, primarily in tech-buying life cycles where business leads or collaborates in initiating the purchase process, the CIO’s department still plays a major part in the process: choosing the vendor, implementing the solution, managing the vendor relationship, and paying the bills. Thus, CIOs manage the purchasing, delivery, and reporting process that business leaders initiated. The most important part of this process is making sure everyone at the table is speaking the same language. To help you do so we have put together what we consider to be the top four financial metrics for analyzing and prioritizing IT expenditures:
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1. IT Spend Ratio
As a CIO, you’re probably involved in IT portfolio management where you’ve stratified the IT investment portfolio into four investment categories: infrastructure, transactional, informational, and strategic. You’ve aligned these categories to the type of benefit delivered by the investment.
4. Actuals versus Budget
Like most organizations, you probably work on your budget with Finance once a year, while every month you forecast for either a given period or for a project. You need clear definitions of IT services with associated compositions, allocation methods and pricing all incorporating fact-based fair market value. When you conduct monthly forecasts, you compare to actual expenses and determine variances from budget. Then what? Here’s where collaboration between Finance and IT, blended with the proper fixed-to-variable cost ratio, can really make a meaningful impact on the organization. Without an awareness of the “where” and “why” of those variances from budget, then it’s just meaningless numbers on a page, and it will be tough for you to manage an IT budget effectively.
Over the past few years, there has been a move from typeoriented categories to stratifying investments by benefit horizon and strategic nature, with a focus on top-line business growth. You can now manage investments in a way that better reflects the value of the investment to the business. Portfolios now include: run-the-business (RtB), grow-the-business (GtB), and transform-the-business (TtB). You’ll need to keep a balance regardless of how you stratify your investment portfolio. Many CIOs are looking for cost improvements that will reduce their RtB investment ratio as a percent of total IT spend, so that they can then increase their investment ratios in GtB and TtB.
Healthy IT Finance collaboration and the right business insights will help you unlock these variances from budget, and turn them into proactive changes. Ensure your stakeholders are informed of the effect of these variances on business units and on the business as a whole. Proactively recommend that if shortfalls exist you can neutralize variances by reducing expenditures, and if a budget surplus exists you can invest it back into the business to focus on high-return strategic initiatives.
2. Return on Capital
One of the reasons why many SaaS companies have prospered is their ability to revolutionize this key metric. The ability to fund projects with OpEx (operating expenditure) flowing through an income statement, rather than CapEx (capital expenditure) burdening the balance sheet, has been a dramatic shift that has helped optimize return on capital. Nevertheless, key financial metrics still need to deliver fantastic ROI (return on investment), rapid payback (the number of months to recoup your investment), and the most valuable metric for ranking, IRR (internal rate of return). A key reminder: some of these metrics can be complex, and accounting practices that govern the capitalization of costs differ from organization to organization. Whether you’re an IT executive or a CIO, you need to clearly understand how the rules apply to your organization. It’s essential that IT Finance collaborate on this topic.
3. Fixed to Variable Cost Ratio
When you consider that nearly two-thirds of most IT budgets are fixed costs, then it’s evident that a variable cost structure is an option that needs further investigation. As a variable cost structure delivers both agility and flexibility, a lower fixed-tovariable cost ratio could be better for your business. When you maintain a higher proportion of costs as variable, it’s frequently more cost effective to scale up or down based on changes in demand.
IT financial metrics are essential to the business
The core value they add to your business is invaluable. IT financial metrics: •
Demonstrate value to the business in a way business
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Enable IT leaders, IT partners and business consumers to
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leaders understand
make decisions that enhance business value and optimize change-readiness
Help to ensure a better business alignment
To ensure IT financial metrics add core value to your business, you require the skill sets, processes and systems to generate efficiency and transparency, as well as the people and mindset to take the actions necessary to be the digital hunter, rather than the digital prey.
It’s key to monitor the fixed-to-variable cost ratio. The ratio needs to balance with business requirements—and it’s not always advantageous for it to be lower. In the case of an organization with economies of scale and low volatility, a higher proportion of fixed costs might be beneficial. However, if you operate in an environment of regulatory change, technology disruption, industry consolidation, uncertainty, rapid growth or decline, or any other external factor beyond your control, then a lower ratio of fixed-to-variable cost will allow you to be more market-agile. In today’s climate, we view the ability to react immediately to changes in business environment as a distinct competitive advantage.
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AUGMENTED EVERYTHING: HOW A CIO CAN MAKE WORKERS SMARTER, STRONGER, MORE AWARE By Wayne Sadin
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Much has been written recently about the emergence of Artificial Intelligence and robotics technology, and about how these technologies will destroy jobs and wreck society. Someday, maybe. But today and for the next few years—the planning horizon of CIOs rather than academics—the reality is far different and far brighter: jobs will be enriched and the workers doing them will do them in new ways, at least for firms whose CIOs understand what’s happening. Artificial Intelligence (AI) has been studied and discussed since the days of Marvin Minsky and Seymour A. Papert, professors who pioneered the field over 60 years ago. Despite 60 years of research and billions of dollars invested, today’s AI is far from intelligent. Yes, AIs can win at Chess, Jeopardy, and Go: narrow problem domains with very specific rules. With the right class of problem—where data searching and correlation are paramount— AI can seem amazing. But very few classes of jobs will be lost to AIs near term. Think of the letters “AI” as standing for “Augmented Intelligence,” which is the use of computers with vast data stores and terrific pattern-matching skills to advise and assist humans who have to juggle complex problems. Have you heard the expression, “When you hear hoofbeats think horses, not zebras?” It means “seek obvious answers,” right? In many fields, limiting oneself to the obvious answer means missing an answer that can save a bundle of money…or doom a hospital patient to death. The expression originated in the 1940s from Dr. Theodore Woodward, professor emeritus of medicine at the University of Maryland School of Medicine, who gave this advice to interns he was training. Back then a new doctor—or even a seasoned clinician—couldn’t possibly remember the huge number of symptoms and lab tests needed for the differential diagnosis of every disease. And even access to comprehensive medical libraries—paper books with card catalogs for access— was difficult, not to mention the research skills needed to ferret out lurking “zebras” when a patient presented with “funny symptoms.”
but the narrowest sub-specialist would miss? That is, if you could figure out which sub-specialist you needed and had access when you needed it. And what if that assistant whispered suggestions and opinions into the doctor’s ear? Think of the benefit to patients with rare ailments, or those who might benefit from some notvery-well-known new treatment. And think of the benefit to society if young doctors quickly gained the diagnostic and treatment accuracy heretofore obtained though 25 years of experience. So, when will we see this assistant? How about two years ago? IBM’s Watson does more than win at Jeopardy. Using research data from online libraries, clinical results from teaching hospitals (MD Anderson Cancer Center in Houston, for example), and actual patient results obtained from the families of IBM employees and others, Watson is getting better and better at advising clinicians. It’s not just IBM: Stanford University and the International Skin Imaging Collaboration: Melanoma Project are helping doctors identify subtle melanoma symptoms that even expert eyes may miss. Let’s be clear: most of us are far from ready to leave our diagnosis and treatment to an AI. But who can object to a trained doctor using automated help to identify the best possible diagnosis and treatment? Areas such as weather forecasting, securities and commodities trading, insurance underwriting, pricing (in 2003 my team built an expert system that identified/priced a set of optimal mortgage loans for borrowers, out of millions of choices, in under 20 seconds), wherever you’re dealing with complex data and decision rules an Augmented Intelligence can help a skilled analyst ferret out subtle patterns and obscure choices that make the difference between a “good” outcome and a “brilliant” outcome.
Fast forward 70 years to 2012, or so. Medical knowledge expanded by a factor of ten, or maybe a hundred, but students had access to billions of pages of medical research via laptops or mobile devices. Access to data was easy, but if anything the problem of finding the elusive zebra was even harder than for the 1940 physicians, because there was just so much to sort, select, digest, weigh. What if the doctor had an assistant who had read—and could unerringly recall—every diagnostic fact; who had read the case histories of millions of patients; who had memorized every single fact contained in the patient’s records; and who could sift through the vast troves of data to identify not only “horses” (things the doctor would think of), but also “zebras,” things that everyone
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ROBOTICS
When you hear “robot” do you think, “Bicentennial Man” or “Westworld”? If you do, you’ve been sold a bill of goods by Hollywood. Robots today are arms mounted to conveyor belts or machine tools that perform a limited set of repetitive, perhaps dirty or dangerous, tasks in factories. An emerging class of robots are gaining locomotion (movement), or discrimination (identification of objects), but are still far from operating autonomously. Far more often robotic technology can be used to make workers stronger, faster, more accurate, and grant more endurance. One class of robotic augmentation is the “exoskeleton.” At BMW’s Spartanburg Assembly Plant workers are testing the “ekso vest,” a robotic backpack that supports their upper bodies while doing repetitive overhead work. And at Audi workers wear the “chairless chair” backpack that allows them to just sit down wherever they want—and a chair just appears beneath them. Remember the movie “Aliens” in which Ripley dons the “Power Loader” exoskeleton during the climactic fight scene? While you can’t quite buy one today, several companies have announced development projects for such devices. Another class of robotic augmentation is the “carry-all.” These are robots that carry objects to unburden their human “masters.” Perhaps it’s a drug-delivery cart that brings the proper supplies to the nurse for administration, or a semi-autonomous cart that follows one around (like “Gita,” just announced by Piaggio [parent of Vespa]). These robots save steps—and time—for skilled workers, or free workers up from the burden of carrying heavy loads.
AUGMENTED REALITY
Just as AI can make workers smarter and robotics can make workers faster and stronger, Augmented Reality (AR) can make workers more aware of their surroundings. Until last year, few people paid any attention to AR. Maybe they’d heard of “Google Glass,” but how many had even seen one? Then came “Pokémon Go,” which exposed 500 million people to the wonders of AR. As silly as that application may seem, it demonstrates the utility of superimposing information atop a person’s visual field.
In my own industry, healthcare, it’s vital to be able to categorize people for many reasons: are they staff, patient, or visitor; are they where they’re supposed to be; are there any special circumstances about which we should know? Imagine that as you walk down the halls wearing your glasses, everyone you see has a colored outline around them: blue for staff, green for patient, yellow for contractor, purple for visitor. This identification might come via their RFID badges communicating with building sensors, or later via facial recognition camera (à la “Windows Hello”) built right into the glasses. Using “geofencing” it becomes easy to identify an unauthorized person (a patient in a staff area, or a Senior Living “Memory Care” resident wandering outside their section). Combine AR with AI and you can imagine some very fine discrimination: a patient who has not taken their meds, or a Senior Living resident who just lost a spouse and may need extra compassion. It’s not just people, but things: workers at firms that inspect, upgrade or repair systems in complex environments such as factories or oil platforms spend time finding components in need of inspection. AR can allow the workers to quickly look around a room and have critical components visually highlighted, along with a readout of recent and historical operating parameters and issues. Again, by adding AI to the AR system the worker can be alerted to “zebras”: infrequent situations that warrant special attention based on collective company or industry experience. When the inspection, upgrade or repair commences, instructions can be superimposed right in the worker’s visual field, eliminating the need for reference to instruction manuals. This creates a situation akin to that of the new physician: wearing an AR display that identifies components, suggests needed tests and procedures, and provides “tips” can enable a less experienced technician to function at the level of a seasoned pro—because the seasoned pro is “right there” giving suggestions. Who can say what might happen in the long run? Will AI and robotics someday supplant workers rather than augment them? What we can say is that in the world of today, CIOs have access to tools that make their workers far more productive than ever before. Over the next three to five years, for sure, Augmented workers will create competitive advantage for firms that embrace the technologies.
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EMBRACING THE CHALLENGES OF GAINING VALUE FROM ANALYTICS By Craig S. Mullins
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Data volume and higher transaction velocities associated with modern applications are driving change in organizations
across all industries. This has occurred for many reasons. Customer and end-user expectations for interacting with computerized systems have changed—and technology changes to accommodate these requirements. Furthermore, larger
and larger amounts of data are generated and made available both internally and externally to businesses. Therefore, the desire and capability to store large amounts of data continues to expand.
One clear goal of most organizations is to harness this data—regardless of source or size—and to glean actionable insight. This is known as analytics. Advanced analytical capabilities can be used to drive a wide range of applications, from
operational applications such as fraud detection to strategic analysis such as predicting patient outcomes. Regardless of the applications, advanced analytics provide intelligence in the form of predictions, descriptions, scores, and profiles that help organizations better understand behaviors and trends.
Moreover, the desire to move up the time-to-value for analytics projects will result in a move to more real-time event processing. Many use cases can benefit from early detection and response, meaning that identification needs to be as close to real time as possible. By analyzing reams of data and uncovering patterns, intelligent algorithms can make
reasonably solid predictions about what will occur in the future. This requires being adept enough to uncover the patterns before changes occur. This does not always have to happen in real time.
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Issues in Deploying Advanced Analytics
When implementing an analytics project, it is not uncommon to encounter problems. One of the first issues that needs addressing when adopting analytics in the cognitive era, is to have organization leaders who will embrace making decisions based on data—instead of gut feelings based on the illusion of having data. Things change so fast these days. It is impossible for humans to keep up with them. Cognitive computing applications that rely on analytics can ingest and understand vast amounts of data and keep up with the myriad of changes occurring daily—if not hourly. Armed with advice that is based on a thorough analysis of up-todate data, executives can make informed decisions rather than the guesses they are making today. However, most managers make decisions based on their experience and intuition without necessarily having all the facts. When analytics-based decision making is deployed, management can feel less involved and might balk. Without buyin at the executive level, analytics projects can be costly without delivering an ROI because the output, which would deliver the ROI, is ignored. Another potential difficulty involves managing and utilizing large volumes of data. Businesses today are gathering and storing more data than ever before. New data is created during customer transactions and to support product development, marketing, and inventory. Many times, additional data is purchased to augment existing business data. This explosion in the amount of data being stored is one of the driving forces behind analytics. The more data processed and analyzed, the better advanced analysis will be at finding useful patterns and predicting future behavior. Even so, as data complexity and volumes grow, so does the cost of building analytic models. Before real modeling can happen, organizations with large data volumes face the major challenge of getting their data into a form from which they can extract real business information. One of the most time-consuming steps of analytic development is preparing the data. In many cases, data is extracted and a subset of this data is used to create the analytic data set where these subsets are joined together, merged,
aggregated, and transformed. In general, more data is better for advanced analytics. There are two aspects to “more data”: 1. Data can increase in depth with more customers, transactions, etc.
2. Data can grow in width when subject areas are added to enhance the analytic model
At any rate, as the amount of data expands, the analytical modeling process can elongate. Clearly, performance can be an issue. Real-time analytics are another interesting issue to consider. The adjective “real-time” refers to a level of responsiveness that is immediate or nearly immediate. Market forces, customer requirements, technology changes, and governmental regulations collectively conspire to ensure that out-of-date data is unacceptable. As a result, today’s leading organizations are constantly working to improve operations with access to and analysis of real-time data. Nimble organizations need to assess and respond to events in real time based on up-to-date and accurate information, rules, and analyses. Real-time analytics is the use of, or the capacity to use, all available enterprise data and resources when they are needed. If at the moment information is created in operational systems it is sensed and acted upon by an analytical process, real-time analytics have transpired. As good as real-time analytics sounds, it is not without its challenges to implement. One such challenge is reducing the latency between data creation and when it is recognized by analytics processes. Time-to-market issues can be another potential pitfall of an advanced analytics project. A large part of any analytical process is the work involved with gathering, cleansing, and manipulating data required as input to the final model or analysis. As much as 60 percent to 80 percent of personnel effort during a project goes toward these steps. This up-front work is essential to the overall success of any advanced analytics project.
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Technology Considerations
From a technology perspective, managing the boatload of data and the performance of operations against that data can be an issue. Larger organizations typically rely on a mainframe computing environment to process their workload. But even in these cases the mainframe is not the only computing platform in use. And the desire to offload analytics to other platforms is often strong. However, for most mainframe users most of the data resides on the mainframe. If analytics is performed on another platform, moving large amounts of data to and from the mainframe can become a bottleneck. Good practices and good software are needed to ensure that efficient and effective data movement is in place. But before investing in a lot of data movement off the mainframe, consider evaluating the cost of keeping the data where it is and moving the processes to it (the analytics) versus the cost of moving the data to the process. Usually, the former will be more cost effective. Taking advantage of more in-memory processes can also be an effective approach for managing analytical tasks. Technologies like Spark, which make greater use of memory to store and process data, are gaining in popularity. Of course, there are other in-memory technologies worth pursuing as well. Another technology that is becoming more popular for analytics is streaming data software. Streaming involves the ingestion of data—structured or unstructured—from arbitrary sources and the processing of it without necessarily persisting it. This is contrary to our common methodology of storing all data on disk. A stream computing application gets quite complex. Continuous applications composed of individual operators are interconnected and operate on multiple data streams. For example, in healthcare multiple streams exist such as blood pressure, heart rate, and temperature, from multiple patients with multiple diagnoses.
The Bottom Line
Many new and intriguing possibilities exist for analytics that require an investment in learning and new technology. Even so, the return on investment is potentially quite large through insight into the business, which results in better service to customers. After all, that is the raison d’être for the business.
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Big Data used by health insurers to help counteract threat of market disruption By Andrew Armstrong
Today, the health insurance industry is increasingly volatile and facing constant uncertainty with the proposed drastic changes by the current administration to the Patient Protection and Affordable Care Act (PPACA), or Affordable Care Act (ACA) as it is more commonly known. Signed into law in 2010 by President Barack Obama, with its first year of full reform in 2014, the ACA is currently facing the threat of changes, as yet unknown. Since House Republican leaders abandoned their bill to replace the ACA with a new health-insurance system, their efforts are now focused on making changes to the law through waivers and rule changes. What is going to happen now? Will it be repealed outright? Will it be replaced by an “American Healthcare Act,” or another similar act? As of this writing, the answer to these questions is as unknowable as the answer to the question: “Is the universe finite or infinite?” Either way, it will be complex and costly to change a system—designed specifically for hospitals and primary physicians to transform their practices financially, technologically, and clinically—to a radically different system. Currently, no concrete clarification on what these changes might look like is forthcoming. The business environment in the face of such volatility and uncertainty cannot predict or create future business models with any degree of certainty—without big data and analytics. Today, in the healthcare insurance industry, market turbulence is a fact of life. In such an uncertain environment, how do companies make sound business decisions that are usually based on history and experience? They are in uncharted territory with no parallels in history to guide them.
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Embrace Big Data and Analytics
A new Healthcare IT News survey found that an estimated 59 percent of healthcare insurance providers have a big data solution in place, while those who do not are planning on implementing one this year. It is definitely time to leverage big data to help provide clarity and informed decision making. (Analytics 2017: The Year Ahead in Healthcare Information Technology) With big data and analytics, business development functions can create multiple scenarios and play out many potential outcomes. It will help companies predict how their customers will react to various legislative changes, as well as different business models. It will give them the adaptability they need to keep pace with changing market conditions and to maintain competitiveness. In his article, How Big Data is Changing Insurance Forever, Bernard Marr, a globally recognized big data and analytics expert, looks at some of the more recent developments in the insurance industry— made available through the increasing ability to record, store, and analyze data. These developments demonstrate how big data is transforming the world of insurance. Enhancing services A prime example of the use to which big data can be put to enhance services is provided by US Insurer Progressive. The organization has developed the Business Innovation Garage. The garage has “mechanics,” in reality technologists, who produce and road test innovations. One such innovation renders 3D images of damaged vehicles using computer graphics. Scanned in from cameras, these images are used to create 3D models, enabling structured data to be recorded on the condition of a vehicle and any damage it might have sustained. Utilizing wearable technology Another revolutionary concept underway stems from wearable technology such as the Apple Watch and Fitbit activity trackers. The data from these wearable technologies delivers an evolving assessment of activity levels and lifestyle. According to research by Accenture, a third of insurers are offering services based on the use of these devices. One such insurer is the John Hancock insurance company. It is offering “An innovative life insurance solution that rewards healthy living!” Customers receive benefits such as personalized health goals, expert nutrition information, and wellness tips. They even get a free Fitbit device to get them started. Customers can reduce their premiums on a sliding scale while, at the same time, improve their health and lifestyle.
While all this is good news for those who embrace analytics and the innovations that enable positive predictive modeling, if the ACA is drastically altered through waivers and rule changes, then it threatens the insurance coverage of large numbers of paying customers. Because of this, the focus of many health insurers is on enhancing customer satisfaction to retain as many customers as possible—especially their most valuable customers.
Focus on Customer Retention
The uncertainty resulting from unknown healthcare coverage changes, and from the potential loss of paying customers, is causing widespread unease for both the insurers and the insured. Republican Senator Susan Collins of Maine talked about the unease of some in her party, saying that changing the healthcare law and delaying a replacement could send insurance markets into “a death spiral.” Consequently, for health insurers, customer satisfaction and loyalty have become increasingly critical. According to Accenture, it is no surprise that about one quarter, 26 percent, of health insurance customers describe themselves as “not loyal at all” to their insurer. It is not surprising considering the historical lack of focus on the consumer. Today, it has become even more critical that health insurers focus on their customers: before the ACA’s coverage mandate caused more consumers to look at their health insurance choices, they were taking a more active role in their care experience. As a result, healthcare insurance companies had to deliver a more engaging experience if they wanted to increase their customer base. To do so, they will need to leverage new technologies so they can offer customized health insurance, discounts, and incentives to their customers. Considering we are in the age of the customer, and have been for a while, the focus on customer satisfaction and retention is long overdue. In addition, with the growth in mobile technology—it was predicted that by 2016 there will be over 2 billion smartphone users with over 268 billion mobile downloads by 2017—there has not only been a rapid and dramatic rise in data usage, but also countless ways to engage with customers. Good news for health insurance companies. (Statistic courtesy of IBM Security) So how do health insurers deliver a more consumer-friendly experience while increasing customer satisfaction and loyalty?
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Improve Customer Experience Continually improving the customer experience is crucial. Social media analytics deliver capabilities that enable insurers to gain many new customers. It also yields cross-selling opportunities by connecting profiles, social activities, and insurance services of existing customers. If customer dissatisfaction exists, then it can be addressed through customized marketing campaigns and fine-tuning of customer services. Deliver Personalized Policies Another opportunity for building and strengthening customer loyalty is by adapting insurance policies for each individual customer. For instance, through integrating vehicle telematics, big data and analytics, insurers can personalize vehicle insurance policies with rates and discounts based on real-time driving data placed in context. IBM’s Telematics for Insurance white paper gives an illustration: “For example, perhaps a telematics report shows that a car’s wheels are spinning but the vehicle is not moving. With a traditional Usage-based Insurance (UBI) program powered by data collected from the Internet of Things (IoT), a driver would not see a discount for safe driving based on the vehicle data alone. But by running real-time analytics on the vehicle data in conjunction with weather and geolocation data sources, the insurer can see that at the time the wheels were spinning, conditions were icy and the car was actually in the policyholder’s driveway— showing that the driver was merely trying to get their car moving in the morning.”
Based on this predictive model, the insurer can introduce personalized offerings based on how the customer drives. When natural disasters occur, customers want immediate assistance. With big data, insurance companies can analyze claims data quickly, assigning the right claim adjusters while also setting the right limits for claim payouts.
Define target markets
Big data analytics offer health insurers insight into their customers, enabling them to be more targeted with the risks they want to underwrite, to identify new customers, predict fraud, or identify those claims that have the potential to become increasingly expensive. Demographic Data – As of this writing, the ACA requires uninsured US citizens to purchase health insurance (This requirement may soon be repealed by an Executive Order dated January 20, 2017, allowing federal agencies to change, delay or waive provisions of the law that they deem overly costly for insurers, drug makers, doctors, patients or states.) Currently, if US citizens do not purchase health insurance, they pay a penalty. Demographic data helps to narrow the field by finding the individual who would pay the penalty rather than purchase the health insurance. Consumer Data – Determines the type of coverage an individual is most likely to purchase, further refining the target market for insurers. This data gives insurers clarity and an understanding of their ideal customer for certain tiers of coverage. Data to determine Value Propositions – This applies to both current and new healthcare customers. The data identifies those who are most likely to be loyal. The data also helps to determine high coverage rate customers versus low coverage rate customers.
Adapt and Respond Rapidly
To remain competitive and succeed in a destabilized market, health insurance companies must leverage big data and predictive analytics. When they capture and analyze data from their customers and unstructured data from other sources such as social media, they gain superior insights into their customers’ minds and behavior. Health insurers must also adapt and respond rapidly to changing market trends and corresponding customer demands. They must develop and deliver innovative products and services, anticipate unexpected changes, and be prepared for multiple scenarios and their potential outcomes. They must also have the right talent with the skill sets necessary to develop a Big Data Analytics Strategy. A Big Data Analytics Strategy, together with adaptability and agility, will help health insurers weather continuous market disruption caused by ongoing threats to healthcare coverage in America.
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Blockchain: Challenges, opportunities, and a car in every driveway By Allan Zander
When the automobile replaced the horse and carriage, it was just considered to be a faster horse. What you might ask, does this have to do with blockchain and its challenges and opportunities? Let’s begin by considering the challenges and opportunities presented by the automobile when the first person stepped into the first car. Now think of the car you drive today. It has built-in assistance and security systems that enable it to communicate with you, your car dealer and, if necessary, emergency services. And think how our world today is structured around the automobile: from local civic planning to international trade agreements. What the automobile did for the world of transportation, the internet did for media. When the internet started twenty-five years ago, it was a complex, impenetrable environment populated with innovators and early adopters. Today, according to a Forrester Research study, reliance on the internet for commerce, communication, entertainment, and social lives is growing. What was pursued in the past by a few, is now pursued by the many. It is embedded and integrated into our regular routines and in our language: we “bookmark websites” and we “Google to find products and information.” Now consider blockchain—a software platform for digital assets. Blockchain is said to be the most consequential technology innovation since the internet.
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How Blockchain started
Blockchain is best known as the technology that underpins bitcoin, which was found to be exceptionally robust and highly extensible. Since its introduction, several new and existing businesses are developing new products based on blockchain. In particular, the financial services sector is investing heavily in blockchain startups and is funding research initiatives worldwide.
How Blockchain works
At its core, a blockchain is a distributed database where all participating parties have access to the entire dataset. As the name suggests, the data is stored in blocks that are chained together. One or more transactions make up a block, which when bundled together with the fingerprint of the previous block in the chain are run through a complex mathematical process to create a block. This combination of strong cryptography and linking blocks of transactions in a chain makes for an exceptionally secure place to store records.
How Blockchain creates trust
Blockchain offers the opportunity to remove, or at least limit, the dependency on third parties to verify and record transactions. The trust will reside in the mechanism and in the ability of all participants to verify a transaction. Money moves from the buyer to the seller—and the network operator and taxation authority— immediately, and in a verifiable way that is seen by all participants. Blockchain operates on an enforced kind of consensus: as all participants operate independently either submitting transactions to be processed or creating the blocks in the chain, things can happen concurrently. The network resolves these occurrences with a simple rule of “longest chain wins.” The network and all participants continue and the longest chain becomes authoritative. This enforced consensus is what creates the immutability of the records stored in the chain since each block in the chain is built from the preceding block and, in turn, is used to build the next block. With this process, it becomes exceptionally difficult to alter a record since it would require all blocks after it to be changed as well. This creates a kind of built-in risk management system when used with the longest chain rule. Retailers can set their own levels of confidence. Low-risk transactions like buying a cup of coffee can be considered safe after they are two blocks deep. However, a million-dollar house might need to be several hundred blocks deep to be considered safe. The further back in the chain, public chains at least, the more you can consider the transaction permanent and trustworthy. You can easily envision the benefits for the financial services industry: faster, lower cost, immediate and easily verifiable transactions.
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How Blockchain will affect our world
Like the automobile and the internet, Blockchain offers both challenges and opportunities to completely change the world in which we live. Following are four examples:
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Smart contracts
Other than regular financial transactions, the concept of smart contracts coded into the transaction has the more immediate potential. Imagine, someone agrees to buy 100 widgets from a supplier as long as the exchange rate remains within a given range. The transaction itself would know that and, if the exchange rate fell out of the range, ownership of these widgets would revert to the supplier without any intervention. The opportunities for smart contracts are almost limitless. Imagine if your ERP system could allocate budgets to departments where the allocation and spending rules were built into the “money.” Audits or waiting for approvals would no longer be relevant since the allocation could not be spent incorrectly. Additionally, a deposit you paid on a large purchase would immediately be returned or forfeited pending approval or expiration of an agreement without any intervention. As you can see, fantastic opportunities exist to embed business rules directly into transactions, so that when conditions are met they can execute themselves.
Trusted computing
If smart contracts can be coded into the transaction, why not actual computer code? In fact, the ideas are one and the same. Trusted computing is central to how much of the internet runs today. Consumers can trust websites that take credit cards, recipients get encrypted email and messages unaltered, and applications and updates from a manufacturer can be trusted. Mostly, all this works well. That said, the current model of security for much of the world is based on security certifications issued by centralized third parties. Even so, these factors—criminals becoming increasingly sophisticated, nation states involved in espionage, the sheer scale of the internet and its continued growth—come together to paint a grim picture of security and privacy in the future. Many of the same concepts that apply to smart devices that comprise the Internet of Things (IoT), can be applied to the array of servers, virtual machines, neural networks, and databases that make up the backend of the internet—generously described as the cloud. The cloud, of course, simply means “someone else’s computer.” This is not to say the companies currently dominating this market have not done an excellent job of securing and automating their networks, but we are again back to the idea of scale—at some point centralized management begins to break down. While blockchain is not going solve all security issues overnight, or spontaneously cause the downfall of oppressive regimes, as a technology it does offer us alternative opportunities: we no longer have to build bigger more complex solutions based on centralized trust models. The answer is likely going to fall somewhere in between, but we now have a working model of how to build a secure distributed network that can be trusted by all participants.
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Identity and authentication
Increasingly, online security is becoming a priority for businesses, governments, and individuals. Weekly, we read about data breaches exposing people’s identities, password hashes, credit card numbers, email addresses, and all other types of personal and private information. Even though blockchain is not a magic bullet designed to solve existing challenges, it does help to solve some of the challenges. For example, in the case of stolen credit card numbers, what if there were no credit card numbers—only people’s public key addresses? That would save us all the annoying hassles associated with cancelling and renewing our credit cards.
IoT and micro transactions
One of the most exciting opportunities is how the technology can be applied to the IoT. Gartner forecasts that 20.8 billion connected things will be in use worldwide by 2020. Managing these devices (things) will be tough: as evidenced recently by using IoT webcams to create a DDOS botnet. As these devices continue to proliferate, centralized management will become completely impossible. At its core, managing IoT is a problem of scale, something blockchain is good at solving. Blockchain presents a unique set of capabilities to allow devices to “manage” themselves. Companies could publish instructions, updates, or configuration information on a blockchain, then devices would autonomously read and verify to execute the new instructions. Typically, in business we talk about B2C or B2B: Business to Consumer or Business to Business types of transactions. With the proliferation of autonomous devices, we now need to look at how to handle Machine to Machine transactions, or M2M. Micro transactions have long been the Holy Grail of monetization of the internet. Almost all “free” services on the internet are driven by advertising revenue, along with the associated privacy implications that come with collecting personal information. While micro transactions have long been thought of as a way to enable “pay as you go” types of services, the cost of the transaction typically outweighs the value of the transaction itself. With M2M transactions there is no personal information, or even a person to receive advertisements. However, blockchain, with its much lower (and decentralized) transaction costs, along with its ability to bundle or hold transactions in escrow until a minimum is met, presents many opportunities for a clear path forward to monetize IoT, while also providing much needed security and device management.
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A glimpse into a Blockchain future
We cannot know the impact blockchain will have on industry, governments, and society in the future. But we can imagine it like the internet is today, embedded and integrated into our daily lives. For example, we will fully trust our transactions as they will execute themselves when conditions are met. We will enjoy less costly and more immediate services from our bank. We will live in a safer and more secure world with the threats that computer hackers and predators pose greatly reduced, and the growing threat of wide-scale cyber-attacks on critical infrastructure also reduced because with blockchain, IoT will deliver much needed security and device management. And that car in the driveway, will drive itself. Self-driving cars are a near-term technology with the potential to leverage some of the technology of blockchain. When an individual car is not being used by its owner it can be assigned to a fleet for hire. Networks of cars could negotiate bulk purchase of fuel or electricity in automated markets, drive themselves to the dealership for maintenance, and pay directly using money they had earned by hiring themselves out. Because those self-driving cars alone will generate major shifts and disruption within the automotive industry, it follows that our world tomorrow will be structured around blockchain technology, just as it is structured around the internet today.
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