DATA AND ANALYTICS IN INSURANCE: P&C VIEW THROUGH 2020 - Martinexsa
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DATA AND ANALYTICS IN INSURANCE:
P&C VIEW THROUGH 2020
Hortonworks has been granted distribution rights to this
SMA research report
An SMA Research Report
Author: Karen Pauli
July 2017
An SMA Research Report © 2017
2016 SMA All Rights Reserved | www.strategymeetsaction.com
TABLE OF CONTENTS Executive Summary 3 Insurer Usage and Plans 5 New Data Sources and Emerging Technology 13 SMA Call to Action 17 About Hortonworks 18 Strategy Meets Action Commentary 18 About the Research and Strategy Meets Action 19 An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 2
EXECUTIVE SUMMARY
SMA conducted a comprehensive research study on
This report is a continuation of research SMA has been engaged in since 2012. While
data and analytics in the property/casualty industry in
prior reports showed steady progress in data and analytics adoption, external forces and
insurer actions are altering that view. Most specifically, the pace of change within the North America in 2017. Survey participants included
industry has materially escalated, yet insurer response relative to data and analytics has personal and commercial lines insurance executives and
not reflected this, and the gaps are emerging. professionals from both business and IT.
SMA survey results indicate that 92% of insurers have data and analytics initiatives in 2017, This SMA research report identifies where there are
the number two focus, only 3 percentage points behind customer experience. No one measurable differences in various P&C segments. In
denies the value of data and analytics. However, evolving past traditional and into advanced some cases the most relevant views are personal and
capabilities has become imperative. Emerging technology such as AI and big data platforms, commercial lines, while in other cases it is more useful
and new data sources such as IoT, geospatial, drones, and wearables represent the next to review the behaviors and plans of large companies
generation of data and analytics. Critical points evidenced in this research are:
(over $1B in premium) and small companies (under
Insurers continue to invest heavily in basic BI and reporting while nominally investing in $1B).
advanced analytics, data and text mining, and cognitive computing. Predictive analytis
Personal lines organizations allocate 8.6% of IT budgets
is the one category of advanced analytics that is swiftly joining the maturity ranks.
to data and analytics and commercial lines allocate
Specific uses of data and analytics are mature, and have historic investment, but 9.3%, with most organizations increasing spending over
uses related to customer experience and claims are reflective of growing gaps. time.
Not surprisingly, personal lines are ahead of commercial lines in data and analytics “Data is now the source of competitive advantage, and
use. Insurers over $1 billion in premium are more advanced than insurers under insurers must commit capital and talent to the emerging
$1billion, which is cause for concern given the competition for the same business. technologies that will transform silent and disconnected
data into new opportunities.” – Karen Pauli, SMA Principal
Regardless of the size of an organization, a shortage of data and analytics talent
and skills sets are now a significant barrier to advancing capabilities.
There is a small percentage of insurers who are utilizing IoT, wearables, and drone
data, but as much as 82% of insurers have no current plans to do so.
In response to changes in the data and analytics landscape, the SMA Data and Analytics
Spectrum has evolved into its next generation. This report provides a framework for
benchmarking, planning, and executing the spectrum, and provides guidance on how
insurers should respond.
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 3
New SMA Analytics Spectrum
Figure 1. New SMA Analytics Spectrum
DESCRIBE DIAGNOSE DISCOVER PREDICT PRESCRIBE
How do we gain information and What are our new How do we capitalize How do we capitalize on
insights from historical data? opportunities? on new opportunities? advanced opportunities?
What What is Why is it What if it What is the best course of
Where is the problem? What is likely to happen?
happened? happening? happening? continues? action to take?
Dashboards
Ad hoc Advanced Data & Text Predictive Predictive Preventive Cognitive
Reporting & Analysis Scenarios
Queries Analysis Mining Analytics Models Analytics Computing
Scorecards
Geospatial Platforms
AI and Machine Learning
Big Data Platforms
Source: Strategy Meets Action 2017
The SMA Analytics Spectrum was developed to assist insurers in their efforts in developing strategies and plans for BI and analytics. The Spectrum
has been available for several years. However, as the role of data and analytics has grown in importance, and the available tools and technology have
increasingly become more sophisticated, the Spectrum has evolved as well. The categories of Describe and Diagnose are somewhat mature – this is
the domain of traditional BI. The Predict category, historically the task of actuaries and underwriters, has been the recipient of significant spending to
drive more advanced outcomes in 2016-2017. It is the areas of Discover and Prescribe that represent gaps that insurers must address. This research
will provide insights on where and how insurers should focus to take data and analytics initiatives to the next level.
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 4
INSURER USAGE AND PLANS
Adoption of BI and Analytics Solutions – Advanced Usage
Figure 2. Adoption of BI and Analytics Solutions – Advanced Usage
Percent of P&C Insurers Citing Source: SMA Research, Data and Analytics in Insurance, n=87
The insurance industry continues to build capabilities with tools under the Describe and Diagnose areas – foundational business intelligence and
dashboards and scorecards. A significant percentage of the industry describes themselves as advanced users, with the reporting category reaching 71%
in 2017. It is important that the industry continue to do this to support day-to-day operations.
Predictive analytics – under the Predict category – saw a significant adoption and capabilities increase in 2016-2017. This is important because the
industry does need to understand the potential or probable outcomes emanating from advanced analysis and new data.
Despite positive developments in BI and predictive analytics, insurers are not focused on building advanced capabilities. 80% of responders indicate
they have no plans or are just starting to plan for cognitive computing, and 37% have no plans for data and text mining. These trends must be reversed
to shift the balance from a heavy reporting focus to innovation.
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 5
Investment in BI and Analytics – By Personal and Commercial
Figure 3. Investment in BI and Analytics
Percent of P&C Insurers Citing Source: SMA Research, Data and Analytics in Insurance, n=87
Personal lines organizations are a bit more mature in investing in BI and analytics than commercial lines, particularly in the Diagnose category. However,
given the pervasive nature of data standards and core system modernization, personal lines should be shifting investment to the Discover category
particularly in data and text mining to maximize insights from data.
Commercial lines insurers recognize that the data they receive is highly unstructured, and frequently is only captured in core systems for rating
purposes. Because of this, some insurers have found value in investing in data and text mining (44%). Due to the rapidly evolving nature of risk, all
commercial lines insurers will need to make this a priority.
Correlating the advanced usage indicators from the prior slide with the investment indicators above, the lack of capabilities in the Discover and the
Prescribe categories is a self-fulfilling prophecy, that is, you can’t reach advanced capabilities without investing in tools. Insurers, both on the personal
lines side and commercial lines side, must invest in advanced tools or be relegated to only having visibility into the past.
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 6
Use of Data and Analytics: Customer-Oriented
Figure 4. Customers, Marketing, and Distribution – In Use/Implementing
Percent of P&C Insurers Citing Source: SMA Research, Data and Analytics in Insurance, n=87
Not surprisingly, personal lines is ahead of commercial lines in terms of using data and analytics for customers, marketing, and distribution. However,
given the urgency around customer expectations and changes in distribution, commercial lines insurers need to focus resources in equal measure to
personal lines.
In terms of the specific data and analytics use cases within insurers, certain uses are dominant and others are under-supported. As shown above, new
business and agent/producer performance are primary in the customer/marketing/distribution group. Investment in these areas are historically higher
than other categories.
The significant point of concern are the gaps – which have been historical gaps – and must be addressed. The most significant gap illustrated above is
the single view of the customer and customer lifetime value. SMA’s 2017 Strategic Initiatives survey showed that the number one initiative is customer
experience. Without a single view of the customer and an understanding of a customer’s lifetime value, it is difficult to execute successfully on customer
experience. Social media analytics is also a gap. Given the wealth of customer information lying within social media, tackling the lack of insight requires
a change in analytics usage.
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 7
Use of Data and Analytics: Risk-Oriented
Figure 5. Actuarial, Underwriting, Product – In Use/Implementing
Percent of P&C Insurers Citing Source: SMA Research, Data and Analytics in Insurance, n=87
Given the historical use of data and analytics by actuaries across reserving, models, and product development, the relatively high levels of usage of
data and analytics are not surprising. Additionally, there is not the significant gap between personal lines and commercial lines that are exhibited in
other areas.
While underwriting operations does not reflect as high a usage percentage as other scenarios for both personal lines and commercial lines, portfolio
analysis and product development are two areas that commercial lines organizations do need to concentrate on. Commercial lines are hyper-competitive,
and commercial lines insurers will benefit from bringing more science into outcomes. Focusing on individual risk pricing is, of course, critical. However,
as commercial lines insurers grow into new risk coverage areas, understanding how portfolios and products are performing is very important.
New sources of data in the connected world will bring even more potential in the future to this area, especially for CAT modelling, underwriting
operations, and risk analysis.
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 8
Use of Data and Analytics: Service-Oriented
Figure 6. Policy, Billing, Claims – In Use/Implementing
Percent of P&C Insurers Citing Source: SMA Research, Data and Analytics in Insurance, n=87
Given the high usage of data and analytics by personal lines organizations around operational reporting, profitability analysis, policy in-force analysis,
and operational metrics, there is opportunity for reallocation of resources to claims outcomes. In particular, litigation propensity and fraud prevention/
detection are highly under-invested. The significant point is that improved outcomes in both these areas have a direct bottom-line impact. While
commercial lines would also benefit from increased usage in litigation propensity and fraud, frequency in personal lines would be positively impacted,
and there are more commercially available solutions in both areas for personal lines which can be leveraged for speed to business value.
A correlated survey question in the policy, billing, and claims areas relates to analytics and core technology. Almost 30% of survey responders indicated
that stand alone analytics vendors completely satisfied their needs. Comparatively, about 20%, depending on the core system indicated, responded
that embedded core analytics completely satisfied needs. While insurers get speed to business value with embedded core analytics, it is important that
insurers consider all analytics opportunities to assure a broad strategy of analytics adoption relative to their business initiatives.
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 9
Key Drivers for Success in BI/Data Initiatives – By Size of Insurer
Figure 7. Key Drivers for Success in BI/Data Initiatives
Percent of P&C Insurers Citing Source: SMA Research, Data and Analytics in Insurance, n=87
Insurers under $1billion indicate “data readiness” as the number one driver of success. Because many insurers under $1billion tie core system suite
adoption and initial phases of BI and analytics together, the data readiness success factor is hyper-critical.
Regardless of the size of the organization, two other success factors rise to the surface – new solutions/tools, and talent and human resources
investments. Given the vast quantities of emerging data, only new platforms and tools can successfully deal with it. Traditional analytics skills can handle
foundational BI. But insurers, particularly in the large, complex organizations with multi-layered analytics needs, recognize that new skills are pivotal.
An interesting trend surfaced this year related to organizational structure/design: 31% of insurers under $1billion indicate that organizational structure
is a success factor. Aligning with this is our finding that smaller insurers have developed either enterprise data/analytics organizations (45%) or
teams centralized in IT (55%). These insurers have not developed teams in business units – finding greater impact with centralized organizations.
Comparatively, 38% of large insurers have centralized teams.
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 10Primary Barriers to Successfully Executing Data Initiatives – By Size of Insurer
Figure 8. Primary Barriers to Successfully Executing Data Initiatives
Percent of P&C Insurers Citing Source: SMA Research, Data and Analytics in Insurance, n=87
Given that insurers of all sizes believe that new solutions/tools and talent/human resources investments are key success factors, as shown on the prior
chart, it is a significant problem that the number one barrier for insurers under $1billion is lack of IT resources, and 50% of insurers over $1billion (tied
for #2) see the lack of data-related skills as a barrier. Additionally, inflexible legacy technology is a top barrier for all insurers.
Due to the pressing need to move from traditional BI/analytics responses to complex analytics and cognitive computing, all insurers must invest in
talent, and/or partner with technology and service providers who can bring business value in shortened time frames.
Insurers have been addressing inflexible legacy technology for years, and efforts continue with varying degrees of urgency. This constraint on insurers’
abilities to adopt advanced analytics and manage exploding volumes of data must be eliminated, and insurers should reconsider lengthy project
timelines.
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 11Tech Spending, Data and Analytics – By Personal/Commercial
Figure 9. Primary Barriers to Successfully Executing Data Initiatives
Percent of P&C Insurers Citing Source: SMA Research, Data and Analytics in Insurance, n=87
On the average, personal lines insurers spend 8.6% of their total IT budget on data and analytics. For commercial lines insurers, it is 9.3%. However,
the more important story is – are the budgets increasing or decreasing?
Personal lines insurers are more mature in their adoption of data and analytics, largely due to automobile lines that were earlier adopters of analytics.
41% of personal lines insurers are increasing their spending year over year by 6-10%. An additional 41% are increasing budgets by 1-5%. This indicates
that personal lines insurers recognize the value of data and analytics and want to continue to optimize their investments and business outcomes.
Commercial lines insurers exhibit a different picture: 26% indicate they will increase spending by +10%, with 18% increasing by 6-10%, and 29% by
1-5%. Those in the 26% category clearly understand that they have to catch up to demands for new insights and opportunities.
The critical issue across both segments is that spending on data and analytics is not “once and done.” Given the urgent needs, almost all insurers should
be assessing budgets for increases in spending above what the averages suggest.
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 12NEW DATA SOURCES AND EMERGING TECHNOLOGY
Expected New Wave of Innovation in Data and Analytics – By Personal and Commercial
Figure 10. Expected New Wave of Innovation in Data And Analytics
Given the hype and the significant reality, it is
not surprising that machine and deep learning,
artificial intelligence, and cognitive models are at
the top of the lists for personal and commercial
lines. However, there are some differences.
Commercial lines of business are fraught with
complexity. Because the risks and exposures are
Percent of P&C Insurers Citing
complex, and many underwriters believe that
“art” makes up the decisioning process, it makes
sense that enlightened insurers (52%) believe
that cognitive models which emulate human
thinking would be number one on the list. The
possibilities for process and service improvement
are extensive.
While not at the top of the list, it is significant
that personal lines responders believe that robotic
process automation (26%) and chatbots (21%) are
the next wave. The use cases across the lifecycle of
a personal lines account are numerous, spanning
from new business submission support through
billing questions to claims FNOL and services
execution.
Source: SMA Research, Data and Analytics in Insurance, n=87
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 13Preference for Obtaining New and Emerging Data Sources
Figure 11. Preference for Obtaining New and Emerging Data Sources
The current data explosion is almost audible.
Options for collecting and managing new and
emerging data are morphing as well.
51% of insurers want to collect and manage
their own data via their own systems. While this
might be a preference, given the noted barriers of
legacy technology and IT/data related skills, until
these barriers are eliminated, the reality is that
data volumes will be constrained and insights/
Percent of P&C Insurers Citing
opportunities will be limited to the traditional.
49% of responders indicated that industry
consortiums and exchanges are a preference. For
smaller insurers in particular, these sources are
an excellent choice for securing data they might
not be able to gather by themselves.
Choosing to work with InsurTechs is an excellent
way to mitigate all of the above noted barriers.
However, 60% of responders did not see this as
a preference.
Source: SMA Research, Data and Analytics in Insurance, n=87
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 14Top Uses of Data from Emerging Tech (Through 2020)
Figure 12. Top Uses of Data from Emerging Tech (Through 2020)
Across the 3 categories of emerging tech,
underwriting is finding the greatest current use of
these new data sources. Underwriting is followed
by claims, and loss control and pricing are tied.
2018-2020, IOT and drones represent the highest
opportunity areas. Wearables have yet to bubble
up into higher opportunity areas for P&C, though
the use cases in commercial lines are meaningful,
particularly for underwriting, claims, and loss
Percent of P&C Insurers Citing
control.
The overwhelming story coming from this survey
data is the very high percentage of insurers that
have no plans for using emerging tech data. The
success factors, barriers, and spending trends
noted in this research foreshadow these results.
Insurers in the “no plans” category should
recognize that there are insurers - largely over $1
billion - that are already utilizing these new data
sources. The gap they are creating is one that
those without plans may find exceedingly difficult
to close. Insurers in the 2018-2020 planning
process must accelerate adoption to assure they
too are not left behind.
Source: SMA Research, Data and Analytics in Insurance, n=87
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 15New SMA Analytics Spectrum with Data Sources BI/Advanced Tools & Big Data Platform
Figure 13. New SMA Analytics Spectrum with Data Sources BI/Advanced Tools & Big Data Platform
DESCRIBE DIAGNOSE DISCOVER PREDICT PRESCRIBE
Dashboards
Ad hoc Advanced Data & Text Predictive Predictive Preventive Cognitive
Reporting & Analysis Scenarios
Queries Analysis Mining Analytics Models Analytics Computing
Scorecards
Geospatial Platforms
AI and Machine Learning
Big Data Platforms
Embedded Chips, Sensors, Drones, Wearables etc.
External Datasets (Risks, Demographics, Geospatial Data, etc.)
Unstructured Corporate Data
Social Media/Web
Transaction Data (Historical, Current)
Source: Strategy Meets Action, 2017
It is intuitive that BI and analytics sits on top of transaction data, both historical and current. The insurance industry has focused on leveraging
operational data, and adoption and spending have likewise been focused on this. However, given the rapid growth in IoT data, new external data sets,
unstructured corporate data, and social media/web data, business and IT initiatives must take a broader approach.
Insurers need to look past traditional data warehouses and data stores to big data platforms that are fully capable of handling the new, data-driven
world and are architected for AI and machine learning. Geospatial platforms will also have increasing importance in the connected world, with use cases
for insurance expanding way beyond risk analysis.
Due to the rapidly accelerating pace of change in consumer expectations and the global business environment, the time horizon for adopting advanced
analytics and cognitive computing, that can derive new opportunities hiding in emerging data, is shrinking. Insurers of all sizes, without plans across all
lines of business, will potentially face insurmountable challenges in the marketplace.
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 16SMA CALL TO ACTION SMA Call to Action for IT Solution Providers
Being a data-driven organization has taken on an entirely different Be clear – Messaging about what your technology can do relative
meaning. Traditional BI, reporting, and analytics are table stakes. Advanced to data and analytics is critical, but be upfront if data and analytics
capabilities are urgently required to compete. Data and analytics are not execution is not part of your solution.
“once and done” nor a science project – they are fundamental capabilities
Partner for capabilities – Solutions that are tangential to data and
that all insurers must have, both in business and IT. The timeline for
execution has condensed because technology is advancing at a rapid analytics will benefit from partnerships that are directly aligned.
pace, and customers expectations and risk complexity have matched that
Create use cases – Develop use cases that identify exactly what
pace. Finding opportunities lying within new emerging data demands
your solution can accomplish to help insurers visualize.
sophisticated technology. Skills are critical and scarce; partnerships with
experts in data and analytics are imperative to jump-start execution Teach – Help insurers learn. Make your data and analytics expertise
horizons. available so that insurers can grow.
SMA Call to Action for Insurers
Accelerate adoption and investment – Business ownership is critical.
Data and analytics must be a top priority supported by robust funding.
“Insurers understand that data and analytics will drive business
Advance skill development and staffing – Hiring plans must include
value. Some insurers are aggressively pursuing execution plans.
analytics skills specifically. Existing staff must be trained to handle
Others are still searching for a path. Fundamentally, however, for
new data and analytics driven needs. Partnering for data and analytics
all insurers, until legacy system barriers are eliminated, results will
capabilities must be an additional tactic to assure appropriate levels
be constrained.” – Karen Pauli, SMA Principal
of capabilities.
“The preponderance of data/analytics activities and investments
Expand plans for new and emerging data – Seek new sources and
are still for structured data. The great potential of harvesting
access, including consortiums. Explore uses, initiate pilots, and then
insights from unstructured data is largely untapped. A wide range
deploy.
of unstructured sources offers new possibilities for text mining and
Explore the big data and geospatial platforms – Experiment and big data usage, including e-mails, underwriter and adjuster notes,
recognize that there is learning in failure. Establish partnerships to images from cameras and drones, social media and many other
gain expertise. sources.” – Mark Breading, SMA Partner
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 17ABOUT HORTONWORKS and combine that with their historical data repositories to drive new insights
that are differentiating, actionable, and timely. Recently introduced to the
Company Overview market as components of HDF are Streaming Analytics Manager and Schema
Registry. This allows developers to easily build streaming analytics apps
Hortonworks®, founded in 2011, is a leading without writing code, increasing developer productivity. Business analysts
innovator in the data industry, creating, can create dashboards and data visualizations for descriptive analytics of
distributing, and supporting enterprise- streaming data. And IT operations teams can manage the entire streaming
ready open data platforms and modern application lifecycle. Schema Registry further enhances streaming application
data applications. The company leverages a business model based on development by providing a shared repository of schemas that can be shared
open source projects such as Apache™ Hadoop®, Apache™ NiFi, and and reused to maximize governance and security.
Apache™ Spark®. Additionally, a network of over 2100 partners worldwide
provides open and connected data platforms designed for enterprise use. Implications for Insurers
Hortonworks solutions and platforms enable customers to build data-
This report has illustrated that data, new solutions/tools, and talent
driven applications that maximize the value of all data, including historical
are key success factors in data and analytics initiatives. Yet, significant
data-at-rest and emerging big data sources such as data-in-motion. The
barriers keep these success factors at bay for many insurers. IoT, drones,
combined expertise, training, and services allow Hortonworks’ customers
wearables, and a host of other emerging data provide the source of new
to unlock transformational value for their organizations across any line of
opportunities. However, without a platform that can handle the massive
business. Hortonworks has a strong footprint in the insurance industry
amounts and variety of data, and deliver integrated streaming analytics
that includes key implementations by leading industry innovators.
capabilities, insurers will find it difficult, if not impossible to compete.
Data and Analytics Offering
Hortonworks’ two foundational offerings are Hortonworks Data Platform
(HDP®) and Hortonworks DataFlow (HDF®). They are, by design, connected STRATEGY MEETS ACTION COMMENTARY
data platforms built to manage and analyze the massive volumes of
Hortonworks was born in and for the big data world. The volume and variety
data from both new and traditional sources – whether in the cloud or
of data is already staggering, and with the pace of change exponentially
on-premises. The HDP, powered by Apache Hadoop, addresses the full
escalating every day, insurers must be able to innovate and find new
needs of data-at-rest (data stored in digital form in a physical location
opportunities, in real time or near real time. Virtually all technology providers
such as a database or data warehouse). HDF is an integrated platform
are seeking ways to deliver value in a big data world, one way or another.
that securely collects, curates, and analyzes real-time data-in-motion.
However, as their heritage, Hortonworks offers a comprehensive, open
The unique combination of HDP and HDF enables companies to collect
source, big data platform with tools designed specifically to drive ease of use
data and capture insights from the furthest reaches of their landscape,
and speed to business value across all lines of business.
Hortonworks, the Hortonworks logo, HDP, HDF, SmartSense, Cloudbreak, and Powering the Future of Data are registered trademarks or trademarks of Hortonworks, Inc. and/or its subsidiaries
in the United States and/or other countries.
Apache, Apache Hadoop, Apache Spark and Apache NiFi are either registered trademarks or trademarks of The Apache Software Foundation in the US and/or other countries. No endorsement
by The Apache Software Foundation is implied by the use of these marks.
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 18ABOUT THE RESEARCH AND STRATEGY MEETS ACTION
Survey Demographics
Figure 14. Size Figure 15. Line of Business
Figure 16. Role
Source: SMA Research, Data and Analytics in Insurance, n=87
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 19SMA Research Methodology Report Usage
The findings and analyses in SMA’s Data and Analytics series and other SMA research The entire content and context of this research report
reports reflect our analysts’ considerations, opinions, and insights, which are based on is subject to copyright protection, with all rights
their experience and research. SMA analysts use a basic research model: reserved. Reproduction or distribution of the report,
Data gathering: A combination of primary and secondary research data is collected in whole or in part, without written permission is not
through surveys, interviews, demos, publicly available materials, and onsite advisory work. allowed.
SMA analysis: The market trends, data, and the information gathered in the research The material and observations contained in this
are analyzed, vetted, and validated. publication have been developed from sources
believed to be reliable. SMA shall have no liability
The report: Findings and insights are documented. Source information for all data
from third parties or opinions is attributed. When formal survey results are cited, as for omissions or errors and no obligation to revise
much information as possible about survey methodology and participants is provided, or update any data or conclusions should new
within the limits of confidentiality. All other material appearing in this report is created information become available or future events occur.
by the analysts and is derived from the sources listed above and SMA’s experience. The opinions expressed in this report are subject to
Figures and charts based on this analysis are labeled either “Source: SMA Research, change without notice.
Data and Analytics in Insurance, n=87” or “Source: Strategy Meets Action 2017.”
© 2017 Smallwood Maike & Associates, Inc. USA.
May not be reproduced by any means without
express written permission. All rights reserved.
An SMA Research Report © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 20About SMA
Karen Pauli, Principal, has comprehensive knowledge SMA is an independent, privately owned, strategic advisory
about how technology can drive improved results, firm that provides business and technology insights, research,
innovation, and transformation within insurance operations. and actionable advice to the insurance industry. SMA blends
Her areas of focus include claims, underwriting, business unbiased research findings with expertise and experience
intelligence and analytics, distribution, and customer to deliver game-changing intelligence. Analysis of industry
management. She has real-world experience with digital trends, best practices, technology investment patterns and
transformation projects, which has given her unique insight levels, and solution availability and fit are segmented by key
into the changing customer and distributor experience in industry interest areas. SMA’s research reports are written
the digital age. By aligning business goals and perspectives entirely by SMA Partners who have extensive experience at
with technology roadmaps, Karen helps insurers to support a variety of top global financial services firms, technology
evolving business models and create competitive advantage. vendors, and consultancies. Clients of SMA include insurers,
solution providers, brokers/agencies, and consulting firms.
Karen can be reached at 1.774.462.7820 or kpauli@strategymeetsaction.com.
Founded in 2007 and Boston-based, SMA offers services
Follow Karen @kpauliSMA on Twitter.
for the Property and Casualty and Life and Annuity industry
segments. SMA’s services are actionable, business-driven,
and research-based – where strategy meets action – enabling
companies to achieve business success. The SMA suite of
advisory offerings includes retainers, research, consulting,
events, and innovation.
Additional information on SMA’s research and services can be
found at www.strategymeetsaction.com.
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