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PERCEPTIONS AND MISCONCEPTIONS BIG DATA IN INSURANCE. APRIL 2013. Craig Beattie and Nicolas Michellod Senior Insurance Analysts. General observations. A recording of this presentation will be available for download to subscribers of Celent’s Insurance services at www.celent.com - PowerPoint PPT Presentation
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© CELENT
PERCEPTIONS AND MISCONCEPTIONS BIG DATA IN INSURANCE
APRIL 2013
Craig Beattie and Nicolas MichellodSenior Insurance Analysts
QUALIFICATIONS, ASSUMPTIONS AND
LIMITING CONDITIONS
This report is for the exclusive use of the CELENT client named herein. This report is not intended for general circulation or publication, nor is it to be reproduced, quoted or distributed for any purpose without the prior written permission of CELENT. There are no third party beneficiaries with respect to this report, and CELENT does not accept any liability to any third party. Information furnished by others, upon which all or portions of this report are based, is believed to be reliable but has not been independently verified, unless otherwise expressly indicated. Public information and industry and statistical data are from sources we deem to be reliable; however, we make no representation as to the accuracy or completeness of such information. The findings contained in this report may contain predictions based on current data and historical trends. Any such predictions are subject to inherent risks and uncertainties. CELENT accepts no responsibility for actual results or future events.The opinions expressed in this report are valid only for the purpose stated herein and as of the date of this report. No obligation is assumed to revise this report to reflect changes, events or conditions, which occur subsequent to the date hereof.
All decisions in connection with the implementation or use of advice or recommendations contained in this report are the sole responsibility of the client. This report does not represent investment advice nor does it provide an opinion regarding the fairness of any transaction to any and all parties.
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General observations
• A recording of this presentation will be available for download to subscribers of Celent’s Insurance services at www.celent.com
• In addition to this presentation, Celent suggest the following reading– Perceptions and Misconceptions of Big Data in Insurance, April 2013– How Big Is Big Data?: Big Data Usage and Attitudes Among North American Financial
Services Firms, April 2013– Big Data: A Guide to Where You Should Be, Even If You Don’t Know Where You Are,
February 2013– Big Insurance Data: Drawing Lessons from Amazon, Google, and Facebook,
December 2011
• You can obtain more information about subscribing from Chris Williams: [email protected]
• For questions about the content please contact: Craig Beattie [email protected] or Nicolas Michellod [email protected]
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Webinar contents
• Survey participants
• Data challenges in Insurance
• Current Big Data adoption
• Investment in data-related technologies
• Applying the Celent Big Data Adoption Maturity Model to Insurers
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Survey participantsSurvey launched in February 2013
Source: Celent survey
• 42% of insurers active in the property & casualty line of business
• 24% in the life business
• The remaining 34% work for composite insurance firms (offering both life and property casualty products)
Title and functions of in proportion of respondents (n=276)
Survey participants by size and geography of insurance companies (n=276)
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Data challenges in InsuranceRanking the “V” challenges
Definition ChallengeVolume The amount of
information gathered is often in terabytes, sometimes in petabytes, and will soon be in exabytes
Digital information to process is exceeding the current data mastery investment in most financial firms
Velocity The speed at which data is collected, analyzed, and presented to users
High-speed data flows and response expectations of end users
Variety Data can take many forms and be gathered from many devices and from internal and external systems and sources, including social media
Growth in the number of data types available, especially with unstructured data
Value Information provided by data about insurance business elements (customers, risks, etc.)
Difficulty to turn data into reliable information
Veracity Plethora of data insurance companies have
Stakeholders do not believe in the data (too many versions of the truth)
• Velocity and Variety of data are the most important challenges
• Volume, Value and Veracity of data are less problematic Source: Celent
In your organization, please rank the following challenges in relation with data by level of difficulty? (In % of respondents; n=225)
The “V” challenges
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Current Big Data adoptionThe Celent Big Data Adoption Maturity Model
ACTIVITIES
PEOPLE
TOOLS
ATTITUDE TO DATA
BENEFIT OF DATA
POSITION OF COMPETITORS
CUSTOMER EXPECTATIONS
Focused on business priorities and only watching Big Data projects
Investing in a few pilot Big Data projects
Implemented tools in production but focused on pilot programs rather than broad Big Data solutions
Building tools and using open source code. Approach is based on established methods
Developing new algorithms, dealing with the most complex data sets
Traditional data skill setsCurious staff, computer scientists, and data analysts
Like experimenters plus experienced, business-oriented professionals and technology partners
Adding a mix of advanced degree mathematicians, computer scientists, and statisticians
Advanced degrees are common here, along with plenty of PhDs
Excel, simple dashboards, standard reports
Free trial software and open source tools, vendor-supported pilot projects
Licensed software and open source tools, vendor-supported installations
Pragmatic approach to building, configuring, customizing, or buying
Proprietary software, open source extensions, etc. improve existing and develop new solutions
Data across the industry is mature, and data models are well established and defined
Analysis of data can provide insights into internal operations and small improvements
New data occasionally allows new approaches and even entrants to the market
New approaches to data lead to new startups, radical changes and great benefit for first mover
Actively looking for new data sources and ways of using data to help drive revenue and profit growth
Data is a commodity across the industry and of little intrinsic value
Occasional insightsData helps the business to operate better and improve efficiency
Collection, use, and productization of data is key to success
Data and firms’ use of data are crucial to differentiating
The technology is untried and untested, and the business case is uncertain
Firms have been slow to adopt these technologies but they are trying to better understand them
Competitors are leveraging this technology to achieve demonstrable cost savings
Competitors have adopted this technology and are seeking industry awards for its novel use
Competitors are delivering new unique solutions and seeking ways to protect their investment
The same old products, services, and offerings that they are used to
Clients expect good, solid products and offerings and are pushing insurers to meet their demands
Good service at a reasonable cost. Offerings that are at pace with if not ahead of competitors
Market leading, offering value for money but also novel solutions
Innovative use of new data sources to its own benefit and that of its customers
MATURITY
SPECTATOR
EXPERIMENTERPRACTITIONER
INNOVATORSCIENTIST
Source: Celent
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Current Big Data adoptionPeople and tools to look at data (1)
Which of the following best describes the teams currently looking at data in your organization and which sort of tools are they using? (Multiple answers possible) (n=218)
Which of the following best describes the teams currently looking at data in your organization and which sort of tools are they using? (team only view) (Multiple answers possible) (n=218)
Source: Celent survey
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Current Big Data adoptionPeople and tools to look at data (2)
Source: Celent survey
• The use of proprietary tools is almost as popular as Excel as a type of tool utilized
• Use of open source tools and proprietary extensions is higher than one might expect in the insurance industry
Which of the following best describes the teams currently looking at data in your organization and which sort of tools are they using? (tools view) (Multiple answers possible) (n=218)
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Current Big Data adoptionAttitude to and benefits of data
In your opinion, how important will the following sources of data be in the insurance industry in the near future (2–3 years)? (In % of respondents; n=202)
How would you prefer to integrate to open data sources? (n=195)
Source: Celent survey
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Current Big Data adoptionPerceived position of competitors
Source: Celent survey
• More than half of them think the sector has been slow to adopt Big Data technologies and is still trying to better understand these technologies
• An additional 16% of respondents feel the technology is untried, untested and therefore the business case remains uncertain
• Only less than one insurer over five thinks their competitors are already leveraging Big Data to make demonstrable savings
• Only 8% feel that competitors were delivering new solutions and propositions to the market
What best summarizes your view of the adoption of big data technologies in your industry and by your competitors on the market? (In % of respondents; n=200)
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Current Big Data adoptionPerceived customer’s expectations
Source: Celent survey
• Half of insurers considers customers to be relatively traditional and basing their choice on the quality of the insurance product proposed and the perceived quality of the related services and tariffs
• Less than a third of respondents think customers’ expectations have changed
• A bit less than 6% of insurers think they are perceived by customers as a leading industry in their use of data, novel use of new data sources and in their ability to leverage that to their own benefit and that of their customers
In terms of customer expectations as a result of big data initiatives, what statements best describe your opinion? (In % of respondents; multiple answers possible; n=199)
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Investment in data-related technologiesCurrent situation
• Insurers leverage data analysis technologies to improve their business basics
• Big Data-related technologies are still unknown for many insurers
• Growing interest in cloud based analytics
What is your current situation with regard to investment in these technologies? (in % of respondents; n=178)
Source: Celent survey
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Investment in Data-Related technologiesInvestment in Big Data and priorities
If you have invested or plan to invest in Big Data capabilities, how do you want to do this investment? (in % of respondents and with multiple answers possible; n=151)
What would be the top 5 priority analysis to do using Big Data infrastructure (rank by priority)? (Number of respondents; n=171)
Source: Celent survey
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Applying the Celent Big Data Adoption Maturity Model to InsurersCurrent versus desired situation
MATURITY
SPECTATOR
EXPERIMENTERPRACTITIONER
INNOVATORSCIENTIST
ACTIVITIES
PEOPLE
TOOLS
ATTITUDE TO DATA
BENEFIT OF DATA
POSITION OF COMPETITORS
CUSTOMER EXPECTATIONS
= where insurers are today
= where insurers should be
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Applying the Celent Big Data Adoption Maturity Model to InsurersFilling in the gaps
To fill in the gaps
Source: Celent survey
If you are planning or have already invested in Big Data capabilities, could you rank by order of priority what you consider to be the building blocks for success? (In % of respondents; n=151)
• Don’t believe competitors are laggards• The insurance industry is set for a step change in customer
engagement• Treat data as a critical raw material
Priority 1
Priority 2
Priority 3
Priority 4
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General observations
• A recording of this presentation will be available for download to subscribers of Celent’s Insurance services at www.celent.com
• In addition to this presentation, Celent suggest the following reading– Perceptions and Misconceptions of Big Data in Insurance, April 2013– How Big Is Big Data?: Big Data Usage and Attitudes Among North American Financial
Services Firms, April 2013– Big Data: A Guide to Where You Should Be, Even If You Don’t Know Where You Are,
February 2013– Big Insurance Data: Drawing Lessons from Amazon, Google, and Facebook,
December 2011
• You can obtain more information about subscribing from Chris Williams: [email protected]
• For questions about the content please contact: Craig Beattie [email protected] or Nicolas Michellod [email protected]
