Fluent Essays

See attached case study on Verisk Analytics. All questions should either be answered in paragraph form or bullet points depending on the ask. Question 5 and 6 are discussion questions based on the case study. This will be turned in through turn it in so all text should be original. wk_2____case_study.docx week_2__lesson_2_verisk_analytics_case_study.pdf Unformatted Attachment Preview QUESTION 1 1. • • • • Why did Verisk Analytics, a company working originally in the property and casualty insurance industry move so aggressively into healthcare insurance? What strengths did Verisk bring to the healthcare insurance market initially and how did it rapidly expand upon that base for competitive advantage? (add as many bullets as needed) QUESTION 2 1. What competitive competencies did Verisk Health bring to the market place and how did their capabilities in data analytics contribute to these competitive competencies? Competitive Competency Data Management/Analytics Capabilities (add more lines as needed) QUESTION 3 1. One of the hot topics in the business literature today is so-called “big data.” It may be argued that the Verisk Analytics/Verisk Health is a study in big data management and business analytics. Do you agree with this statement? Defend your position: • • Yes/No:____________ Your reasoning: QUESTION 4 1. Does the use of patient data at Verisk raise any legal and/or ethical issues and if so, how are these to be addressed? Legal Issues Ethical Issues Mitigations Question 5 - Discussion 1. How did Verisk’s data management and analytics focus/capabilities enable and even drive the creation of Verisk Health? Question 6 - Discussion 2. How did/could Verisk leverage its use of its health care data to create competitive advantage for itself in the healthcare services and risk management marketplace? VERISK ANALYTICS ACHIEVES GLOBAL-LOCAL BALANCE WITH VERISK HEALTH Ina M. Sebastian and Barbara H. Wixom OCTOBER 2017 | CISR WP NO. 424 | 13 PAGES CASE STUDY an in-depth description of a firm’s approach to an IT management issue (intended for MBA and executive education) INFORMATION BUSINESS DATA MONETIZATION CAPABILITIES BUSINESS ANALYTICS This case describes how Verisk Health, the former healthcare services business of Verisk Analytics, developed two critical types of capabilities to successfully monetize data analytics in the challenging healthcare marketplace: foundational capabilities, and capabilities for driving customer action. It further illustrates how Verisk Analytics—an information business with a diversification operating model—developed organizational practices that targeted a “global-local balance,” enabling the company to respond to local needs while leveraging enterprise-wide capabilities. © 2017 Massachusetts Institute of Technology. All rights reserved. Sebastian and Wixom | CISR Working Paper No. 424 | 2 CONTENTS Instructor Note....................................................................................................................................... 3 Verisk Analytics, Inc......................................................................................................................... 5 Verisk Health............................................................................................................................................. 5 Foundational Capabilities: Transforming Data Into Insight................. 6 Capabilities for Driving Customer Action: Understanding Customers to Help Create Value............................................. 7 Mastering the Domain................................................................................................................. 8 Collaborating With Customers. ......................................................................................... 8 Delivering Consulting Services........................................................................................... 9 Creating Consumable Offerings. .................................................................................. 10 Succeeding in the Turbulent Healthcare Marketplace with Global-Local Balance...................................................................................................... 10 Conclusion................................................................................................................................................ 11 Sebastian and Wixom | CISR Working Paper No. 424 | 3 VERISK ANALYTICS ACHIEVES GLOBAL-LOCAL BALANCE WITH VERISK HEALTH Instructor Note: MIT CISR’s Verisk Health case study was created in 2015,1 before the divestiture of the healthcare services business by Verisk Analytics in mid-2016. The details of the case reflect the state of the Verisk Analytics business as of January 31, 2015, while the titles of quoted interviewees reflect their role with the company in October 2017. The Verisk Health case study is one of four published MIT CISR cases that feature companies with “information business” business models.2 The case series intends to showcase a variety of data monetization approaches and evolutions. The Verisk Health case illustrates an information business with a diversification operating model.3 The case showcases organizational practices that 1 The attributions of quoted interviewees indicate their role when this case study was created in 2015. 2 Other published cases in this series include B.H. Wixom and P.P. Tallon, “AdJuggler: Using Data Science to Serve the Right Ad at the Right Time,” MIT Sloan CISR Working Paper No. 404, November 2015; B.H. Wixom and C.A. Miller, “Healthcare IQ: Competing as the ‘Switzerland’ of Health Spend Analytics,” MIT Sloan CISR Working Paper No. 400, February 2015; and B.H. Wixom, J.W. Ross, and C.M. Beath, “comScore, Inc.: Making Analytics Count,” MIT Sloan CISR Working Paper No. 392, November 2013. 3 MIT CISR defines an operating model as a simple statement of the integration and standardization requirements for the firm’s core processes. The four options for an operating model are diversification, replication, coordination, and unification. A diversification operating model reflects low integration and process standardization requirements across independent business units with different customers and expertise. The organizing logic is based on synergies from related, but not integrated, business units whose individual success drive the growth of the company. Companies with diversification models may develop shared services to pursue economies of scale. More information on MIT CISR’s operating model can be found in J.W. Ross, P. Weill, and D.C. Robertson, Enterprise Architecture as Strategy: Creating a Foundation for Business Execution, Harvard Business Review Press, 2006; or at “Enterprise Architecture,” MIT Sloan Center for Information Systems Research, http://cisr.mit. edu/research/research-overview/classic-topics/enterprise-architecture/. This case study was prepared by Ina M. Sebastian and Barbara H. Wixom of the MIT Sloan Center for Information Systems Research (CISR). The case was written for the purposes of class discussion, rather than to illustrate either effective or ineffective handling of a managerial situation. The authors would like to acknowledge and thank the executives at Verisk Analytics for their participation in the case study. © 2017 MIT Sloan Center for Information Systems Research. All rights reserved to the authors. Sebastian and Wixom | CISR Working Paper No. 424 | 4 enabled localized innovation and helped drive scale economies across the enterprise within this operating model. We encourage classroom discussion regarding the ways in which information businesses should make decisions about vertical capabilities. We also encourage that students debate the divestiture of Verisk Health, considering the pros and cons of operating a diversified information business and unique challenges of operating in turbulent markets such as healthcare. When teaching with this case, we suggest that instructors explore the following questions with students: 1. What was the Verisk Analytics business model? 2. How did Verisk Analytics create competitive advantage within its healthcare division? 3. How did Verisk Analytics senior management balance the need to build enterprise capabilities across the enterprise while remaining nimble and responsive to localized needs within the healthcare division? 4. In 2016, Verisk Analytics divested its healthcare division. What is your assessment of that strategic choice? Provide rationale for your assessment. Sebastian and Wixom | CISR Working Paper No. 424 | 5 VERISK ANALYTICS, INC. In 1971, property and casualty (P&C) insurers across the United States collectively initiated and funded the Insurance Services Office, Inc. (ISO) to facilitate compliance with state regulatory processes. The insurers had large state regulatory burden requirements, where they had to put forward relatively granular information about their products and pricing algorithms for each line of insurance. The P&C insurers formed ISO as an association that would gather their data and report it to regulators. The industry collaboration reduced the costs while increasing the quality of the insurers’ reporting processes. As the ISO database grew in size over time, insurers used the data to help manage insurance products, underwriting, and rating. The organization evolved for close to thirty years with the sole mission of understanding and meeting the needs of the P&C insurers. As a result, ISO developed a deep appreciation for the value of pooled data sets and information products that mattered to and were used by its members. In 1997, ISO became a private, for-profit company,4 and in 2009 it went public as Verisk Analytics, Inc. (Verisk; NASDAQ:VRSK). In 2015, Verisk was providing risk assessment services and decision analytics for professionals in many fields, including property and casualty insurance, financial services, government, human resources, and healthcare. Verisk had also become an S&P 500 company, had a market cap of $12 billion, and generated $2 billion in annual revenue. VERISK HEALTH Verisk entered the healthcare space in 2004 with its acquisition of DxCG, a company that possessed a strong presence in the area of healthcare payments. DxCG founders created analytical models that were foundational for key payment processes at the US Centers for Medicare & Medicaid (CMS). At the time of acquisition, DxCG owned more than one hundred fifty analytical models, some of which predicted potentially avoidable high-cost events in the commercial, Medicare, and Medicaid spaces. With DxCG serving as its cornerstone, Verisk Health’s business (see exhibit 1) grew through seven additional Verisk acquisitions over the next decade (see exhibit 2 for a timeline of Verisk acquisitions related to Verisk Health). The acquisitions helped Verisk Health assemble resources and capabilities specific to healthcare risk analytics much more quickly than building them from scratch. Each acquisition also brought a pre-existing customer base, customer understanding, and domain expertise. We assumed that we would get a good and necessary level of acceleration if we could find a player that was already in the space and serving customers, because we believe so strongly in the vertical focus and in the need to be very intimate with our customers. Certainly, we can try to build these things on our own, but we are in a hurry. SCOTT G. STEPHENSON, CHAIRMAN, PRESIDENT, AND CHIEF EXECUTIVE OFFICER, VERISK ANALYTICS, INC. By 2015, Verisk Health had become Verisk’s second-largest business unit, with three primary focus areas: revenue and quality, population health, and payments (see exhibit 3 for a breakdown and valuation of the healthcare addressable market in 2015). A set of foundational capabilities helped Verisk Health convert data assets from all of these areas into insights to benefit its clients—providers, payers, and employers. 4 According to Verisk leaders, as insurers became more reliant upon shared ISO data, regulators became concerned about the risk of price collusion. The leaders decided to privatize ISO in 1997 to avoid the risk. Sebastian and Wixom | CISR Working Paper No. 424 | 6 FOUNDATIONAL CAPABILITIES: TRANSFORMING DATA INTO INSIGHT We don’t believe that you can be a great data analytics company without having the data expertise. It is one of the most distinctive elements of our character and of our success. VINCE MCCARTHY, SENIOR VICE PRESIDENT, CORPORATE DEVELOPMENT AND STRATEGY, VERISK ANALYTICS, INC. Clients (e.g., insurers, employers) provided the majority of the data at the heart of Verisk Health’s offerings. Verisk Health drew client data from claims, clinical electronic medical records, and billing, as well as from health risk assessments and pharmacy and workers’ compensation records. Out of necessity, Verisk Health developed highly effective data ingestion capabilities. The ability to painlessly collect data was essential, because clients hosted data in a variety of different source systems, each with unique standards, data structures, and quality levels. We want to make it easy to do business with us. That means being able to accept data in whatever format our clients can give it to us. Being a CIO, I understand the burden on many IT organizations. So if we insist that a client translate their data into our format, then we’re dead in the water because we’re placed into the IT queue. PERRY ROTELLA, SENIOR VICE PRESIDENT AND CHIEF INFORMATION OFFICER AND GROUP EXECUTIVE, SUPPLY CHAIN RISK ANALYTICS (FORMER), VERISK ANALYTICS, INC. Verisk Health’s ability to ingest data advanced to new levels with Verisk’s 2011 acquisition of Bloodhound Technologies, Inc.5 (Bloodhound). Bloodhound had developed a real-time platform that could pull in claims data in any format and run claims processes in mere hundreds of milliseconds. Bloodhound built the platform to run analytics related to payment processing and healthcare fraud; however, Verisk Health was able to leverage the technology to support other businesses that needed to analyze large numbers of claims quickly, such as population health. At times, clients did not have easy access to key data—either it did not exist in a digitized format or it was not formatted for analytics, such as with narrative data in electronic health records. For these cases, Verisk Health employed approximately one thousand people who digitized paper documents and manually retrieved and coded data based on electronic records. In the example of Medicare risk adjustment, we take in data and create a list of patients who are most likely to have disease states beyond what has been captured. We then send people out to retrieve their medical records. We hand that off to our clinical coders who say, “This is what was coded on the chart, but when I look at these notes, the following things indicate disease states that are not captured.” Ultimately, the result is capturing the appropriate disease burden of the population to ensure appropriate reimbursement. JORDAN BAZINSKY, CHIEF OPERATING OFFICER (FORMER), VERISK HEALTH As Verisk Health amassed growing volumes of data, the business concurrently grew its practices for business continuity and disaster recovery; security; and standards and protocols for internal and third-party data use. We have to be vigilant stewards of our clients’ data. There is no other option. Obviously, there is a lot of training: HIPAA training, privacy policy training, IT security training … we do that regularly because we need to stay up to date on the regulations. Then there’s an advertising component. You’ll see a lot of posters in our different offices talking about privacy and security, reminding people that these things matter. We have a hotline that people can call if anything comes up that they think is problematic or question- 5 “Verisk Analytics, Inc., to Acquire Bloodhound Technologies, Inc.,” Verisk Analytics, Inc. press release, April 27, 2011, on the Verisk Analytics, Inc. website, http://www.verisk.com/archived/2011/verisk-analytics-inc-to-acquire-bloodhound-technologies-inc.html. Sebastian and Wixom | CISR Working Paper No. 424 | 7 able. We have intense data security monitoring and a solid compliance team who eat, sleep, and breathe protection of our data. We are very crisp in our client contracts about what their data is and is not being used for, and we hold ourselves to the absolute highest standard of how that data is utilized. JORDAN BAZINSKY, CHIEF OPERATING OFFICER (FORMER), VERISK HEALTH Verisk Health also had to manage client perceptions regarding its objectivity as an information business. Competitive pressures in the healthcare space made some clients nervous about sharing data because they feared such openness could reveal pricing strategies and other competitive insights. Verisk Health was not owned by a payer or other key stakeholder in the marketplace, so it was positioned to be a neutral curator of cross-company data. We serve as a neutral Switzerland for the observation and retention of customer data. This neutrality builds fundamental trust. VINCE MCCARTHY, SENIOR VICE PRESIDENT, CORPORATE DEVELOPMENT AND STRATEGY, VERISK ANALYTICS, INC. With regulatory constraints and customer-sharing concerns top of mind, whenever possible Verisk Health leadership pursued opportunities to pool client data, integrate data across subject areas, and augment data with new data elements. These activities were believed to create competitive opportunities. We always strive to build a moat around our businesses, where we can take client data sets and enrich them to add insight, turning them into a Verisk proprietary asset. PERRY ROTELLA, SENIOR VICE PRESIDENT AND CHIEF INFORMATION OFFICER AND GROUP EXECUTIVE, SUPPLY CHAIN RISK ANALYTICS (FORMER), VERISK ANALYTICS, INC. For example, new approaches for identifying fraud emerged from pooling data across clients. Fraud’s a $250 billion problem, and it’s estimated to grow to $500 billion. As we pool data across multiple health plans for the purpose of identifying fraudulent practices, we find the simplest but [most] effective scams, such as a doctor who charges multiple health plans for eight hours a day. NADINE HAYS, PRESIDENT (FORMER), VERISK HEALTH Verisk Health leadership believed that its unique data sets became valuable when combined with its analytics capabilities. The company possessed a large inventory of high-performing analytical models that it developed organically or owned via acquisition. Verisk Health worked to embed the models into standard industry approaches—similar to the continuing way that the DxCG predictive models were used pervasively in CMS payment processes. The DxCG models were considered by many to be “the gold standard” for risk adjustment; many providers chose the DxCG models because of this perception. A lot of the risk models will get you to roughly the same place, but when you’re a provider entering into a risk agreement, you don’t want to be using one approach when the payer has a different approach. Then you’re measuring your performance one way, and they’re measuring your performance a different way. VINCE MCCARTHY CAPABILITIES FOR DRIVING CUSTOMER ACTION: UNDERSTANDING CUSTOMERS TO HELP CREATE VALUE Hays believed that Verisk Health’s data sets and analytical models were foundational to the company’s competitive advantage; however, this advantage was deepened and sustained through capabilities that allowed the organization to understand its clients and influence their effective use of analytics insights within core business Sebastian and Wixom | CISR Working Paper No. 424 | 8 processes and decisions. When clients acted upon analytics insights, they created positive bottom-line impact— sometimes at significant levels, such as in the area of revenue solutions. Revenue Solutions is about ensuring that our clients are being paid appropriately for the illness burden that they are managing. One client alone had hundreds of millions in additional revenue last year simply through that activity. In claims editing, we identified more than $1.26 billion of ... Purchase answer to see full attachment