Please see the attached word document with the four questions that need to be answered. The questions pertain to the attached case study. Please use the readings attached as resources.
case_study.pdf

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questions_for_case_study.docx

resource___the_social_enterprise.ppt

resource___decision_support_systems___big_data_slide_set.pptx

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VERISK ANALYTICS ACHIEVES
GLOBAL-LOCAL BALANCE
WITH VERISK HEALTH
Ina M. Sebasian and Barbara H. Wixom
OCTOBER 2017 | CISR WP NO. 424 | 13 PAGES
CASE STUDY
an in-depth descripion of a irm’s approach to an IT management issue
(intended for MBA and execuive educaion)
INFORMATION BUSINESS
DATA MONETIZATION
CAPABILITIES
BUSINESS ANALYTICS
This case describes how Verisk Health, the former healthcare services
business of Verisk Analyics, developed two criical types of capabiliies
to successfully moneize data analyics in the challenging healthcare marketplace: foundaional capabiliies, and capabiliies for driving customer
acion. It further illustrates how Verisk Analyics—an informaion business with a diversiicaion operaing model—developed organizaional
pracices that targeted a “global-local balance,” enabling the company
to respond to local needs while leveraging enterprise-wide capabiliies.
© 2017 Massachusets Insitute of Technology. All rights reserved.
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CONTENTS
Instructor Note …………………………………………………………………………………………………………………….. 3
Verisk Analyics, Inc. ……………………………………………………………………………………………………….. 5
Verisk Health………………………………………………………………………………………………………………………….. 5
Foundaional Capabiliies: Transforming Data Into Insight……………. 6
Capabiliies for Driving Customer Acion:
Understanding Customers to Help Create Value …………………………………….. 7
Mastering the Domain …………………………………………………………………………………………………. 8
Collaboraing With Customers ……………………………………………………………………………… 8
Delivering Consuling Services ……………………………………………………………………………… 9
Creaing Consumable Oferings ……………………………………………………………………….. 10
Succeeding in the Turbulent Healthcare Marketplace
with Global-Local Balance ……………………………………………………………………………………….. 10
Conclusion……………………………………………………………………………………………………………………………… 11
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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 divesiture of the healthcare services business by
Verisk Analyics in mid-2016. The details of the case relect
the state of the Verisk Analyics business as of January 31,
2015, while the itles of quoted interviewees relect 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 “informaion business”
business models.2 The case series intends to showcase a
variety of data moneizaion approaches and evoluions.
The Verisk Health case illustrates an informaion business with a diversiicaion operaing model.3 The case showcases organizaional pracices that
1 The atribuions 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: Compeing as the ‘Switzerland’ of Health Spend Analyics,” MIT Sloan CISR Working Paper No. 400, February 2015;
and B.H. Wixom, J.W. Ross, and C.M. Beath, “comScore, Inc.: Making Analyics Count,” MIT
Sloan CISR Working Paper No. 392, November 2013.
3 MIT CISR deines an operaing model as a simple statement of the integraion and standardizaion requirements for the irm’s core processes. The four opions for an operaing
model are diversiicaion, replicaion, coordinaion, and uniicaion. A diversiicaion operaing model relects low integraion and process standardizaion requirements across independent business units with diferent customers and experise. 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 diversiicaion models may develop shared
services to pursue economies of scale. More informaion on MIT CISR’s operaing model
can be found in J.W. Ross, P. Weill, and D.C. Robertson, Enterprise Architecture as Strategy:
Creaing a Foundaion for Business Execuion, Harvard Business Review Press, 2006; or at
“Enterprise Architecture,” MIT Sloan Center for Informaion Systems Research, htp://cisr.mit.
edu/research/research-overview/classic-topics/enterprise-architecture/.
This case study was prepared by Ina M. Sebasian and Barbara H. Wixom of the MIT Sloan
Center for Informaion Systems Research (CISR). The case was writen for the purposes of
class discussion, rather than to illustrate either efecive or inefecive handling of a managerial situaion. The authors would like to acknowledge and thank the execuives at Verisk
Analyics for their paricipaion in the case study.
© 2017 MIT Sloan Center for Informaion Systems Research. All rights reserved to the authors.
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enabled localized innovaion and helped drive scale economies across the enterprise within this operaing model.
We encourage classroom discussion regarding the ways in which informaion businesses should make decisions
about verical capabiliies. We also encourage that students debate the divesiture of Verisk Health, considering
the pros and cons of operaing a diversiied informaion business and unique challenges of operaing in turbulent markets such as healthcare.
When teaching with this case, we suggest that instructors explore the following quesions with students:
1. What was the Verisk Analyics business model?
2. How did Verisk Analyics create compeiive advantage within its healthcare division?
3. How did Verisk Analyics senior management balance the need to build enterprise capabiliies across the
enterprise while remaining nimble and responsive to localized needs within the healthcare division?
4. In 2016, Verisk Analyics divested its healthcare division. What is your assessment of that strategic choice?
Provide raionale for your assessment.
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VERISK ANALYTICS, INC.
In 1971, property and casualty (P&C) insurers across the United States collecively iniiated and funded the Insurance Services Oice, Inc. (ISO) to facilitate compliance with state regulatory processes. The insurers had large
state regulatory burden requirements, where they had to put forward relaively granular informaion about their
products and pricing algorithms for each line of insurance. The P&C insurers formed ISO as an associaion that
would gather their data and report it to regulators. The industry collaboraion reduced the costs while increasing
the quality of the insurers’ reporing processes.
As the ISO database grew in size over ime, insurers used the data to help manage insurance products, underwriing, and raing. The organizaion evolved for close to thirty years with the sole mission of understanding and
meeing the needs of the P&C insurers. As a result, ISO developed a deep appreciaion for the value of pooled
data sets and informaion products that matered to and were used by its members.
In 1997, ISO became a private, for-proit company,4 and in 2009 it went public as Verisk Analyics, Inc. (Verisk;
NASDAQ:VRSK). In 2015, Verisk was providing risk assessment services and decision analyics for professionals
in many ields, including property and casualty insurance, inancial 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 acquisiion of DxCG, a company that possessed a strong
presence in the area of healthcare payments. DxCG founders created analyical models that were foundaional
for key payment processes at the US Centers for Medicare & Medicaid (CMS). At the ime of acquisiion, DxCG
owned more than one hundred ity analyical models, some of which predicted potenially 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 addiional Verisk acquisiions over the next decade (see exhibit 2 for a imeline of Verisk acquisiions related to Verisk Health).
The acquisiions helped Verisk Health assemble resources and capabiliies speciic to healthcare risk analyics
much more quickly than building them from scratch. Each acquisiion also brought a pre-exising customer base,
customer understanding, and domain experise.
We assumed that we would get a good and necessary level of acceleraion if we could ind a player
that was already in the space and serving customers, because we believe so strongly in the verical
focus and in the need to be very inimate 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, populaion health, and payments (see exhibit 3 for a breakdown and valuaion of the healthcare
addressable market in 2015). A set of foundaional capabiliies helped Verisk Health convert data assets from all
of these areas into insights to beneit 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 privaize ISO in 1997 to avoid the risk.
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FOUNDATIONAL CAPABILITIES: TRANSFORMING DATA INTO INSIGHT
We don’t believe that you can be a great data analyics company without having the data experise. It
is one of the most disincive 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 oferings. Verisk Health drew client data from claims, clinical electronic medical records, and billing, as well as from health risk
assessments and pharmacy and workers’ compensaion records. Out of necessity, Verisk Health developed highly
efecive data ingesion capabiliies. The ability to painlessly collect data was essenial, because clients hosted
data in a variety of diferent 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 organizaions. 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 acquisiion of Bloodhound Technologies, Inc.5 (Bloodhound). Bloodhound had developed a real-ime plaform that could pull in claims data in
any format and run claims processes in mere hundreds of milliseconds. Bloodhound built the plaform to run
analyics 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 populaion health.
At imes, clients did not have easy access to key data—either it did not exist in a digiized format or it was not
formated for analyics, such as with narraive data in electronic health records. For these cases, Verisk Health
employed approximately one thousand people who digiized 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 paients 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 of 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.” Ulimately, the result is capturing the appropriate disease burden of the populaion 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 pracices for business
coninuity 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 opion. 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 regulaions. Then there’s an adverising component. You’ll see a lot of posters in our diferent oices talking about privacy and security, reminding people that these things mater.
We have a hotline that people can call if anything comes up that they think is problemaic or quesion-
5 “Verisk Analyics, Inc., to Acquire Bloodhound Technologies, Inc.,” Verisk Analyics, Inc. press release, April 27, 2011, on the Verisk Analytics, Inc. website, htp://www.verisk.com/archived/2011/verisk-analyics-inc-to-acquire-bloodhound-technologies-inc.html.
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able. We have intense data security monitoring and a solid compliance team who eat, sleep, and breathe
protecion 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 uilized.
JORDAN BAZINSKY, CHIEF OPERATING OFFICER (FORMER), VERISK HEALTH
Verisk Health also had to manage client percepions regarding its objecivity as an informaion business. Competiive pressures in the healthcare space made some clients nervous about sharing data because they feared such
openness could reveal pricing strategies and other compeiive insights. Verisk Health was not owned by a payer
or other key stakeholder in the marketplace, so it was posiioned to be a neutral curator of cross-company data.
We serve as a neutral Switzerland for the observaion and retenion 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 opportuniies to pool client data, integrate data across subject areas, and augment data with new
data elements. These aciviies were believed to create compeiive opportuniies.
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 idenifying fraud emerged from pooling data across clients.
Fraud’s a $250 billion problem, and it’s esimated to grow to $500 billion. As we pool data across muliple health plans for the purpose of idenifying fraudulent pracices, we ind the simplest but [most]
efecive scams, such as a doctor who charges muliple 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 analyics
capabiliies. The company possessed a large inventory of high-performing analyical models that it developed
organically or owned via acquisiion. Verisk Health worked to embed the models into standard industry approaches—similar to the coninuing way that the DxCG predicive 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 percepion.
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 diferent approach. Then
you’re measuring your performance one way, and they’re measuring your performance a diferent way.
VINCE MCCARTHY
CAPABILITIES FOR DRIVING CUSTOMER ACTION: UNDERSTANDING CUSTOMERS
TO HELP CREATE VALUE
Hays believed that Verisk Health’s data sets and analyical models were foundaional to the company’s compeiive advantage; however, this advantage was deepened and sustained through capabiliies that allowed the
organizaion to understand its clients and inluence their efecive use of analyics insights within core business
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processes and decisions. When clients acted upon analyics insights, they created posiive botom-line impact—
someimes at signiicant levels, such as in the area of revenue soluions.
Revenue Soluions 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 addiional revenue last year
simply through that acivity. In claims ediing, we ideniied more than $1.26 billion of documented
savings for our clients last year alone.
NADINE HAYS, PRESIDENT (FORMER), VERISK HEALTH
Ensuring that clients acted on analyics insights, however, was not straighforward. For one, Verisk Health had no
direct control over client acion; at best, the company could communicate and advise. Thus, Hays and her team
were intent on establishing customer-centric strategies that would posiively impact the client’s botom line and
fend of compeitors that developed “good enough” predicive models.
We have beter and more unique data than many people. We are the gold standard, but we should be
worried about people who might create “good enough.” When I think about sustaining compeiive
advantage, we need to make sure that we are evolving with the decisions that our customers need to
make, and not just assume that we’re going to throw data over the wall to them and they’re going to
igure it out.
DOUG FLEISHMAN, EXECUTIVE VICE PRESIDENT, SALES (FORMER), VERISK HEALTH
Mastering the Domain
The beter Verisk Health understood the exising and evolving needs of its clients, the more it could tailor data
and analyics to address pressing and compelling problems and decisions. In part, Verisk Health employees understood the needs of clients because they possessed deep domain experise. Verisk Health proacively recruited
and developed domain-savvy data scienists. It hired analysts with past experience in its client organizaions, and
deep knowledge of claims and the complex infrastructures of payer, provider, and employer organizaions.
Some people on my team and on our client services team could write the rulebook, probably from
memory, on Medicare risk adjustment. Those folks can see the quesion behin …
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