big data: opportunities for the dental benefits industry › 2014 › 02 › ... · 2014-09-08 ·...

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Big Data: Opportunities for the Dental Benefits IndustryJoel Reichert - VP, Data Strategy

Herschel Reich - VP, Payer ConsultingSeptember 16, 2014

Joel Reichert - VP, Data StrategyHerschel Reich - VP, Payer Consulting

Big Data: Opportunities for the Dental Benefits Industry

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Confidential property of Optum. Do not distribute or reproduce without express permission from Optum. 3

Agenda

• Big Data 101

• Use Cases For Big Data Analytics in Insurance & Healthcare

• Opportunities for the Dental Benefits Market

• Challenges for Implementing Big Data


From Big Data to Big Dental

• The Old Paradigm:– Big Dreams

– Big Deficits

– Big Disappointments

• The New Paradigm– Big Data

– Big Drilling

– Big Dental

• Big Disclaimer

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How I Learned about Big Data!

• I watched Captain America: The Winter Solider 3 times on an eleven hour flight

• In Captain America's latest big screen adventure, his arch-enemy isn't a diabolical super villain or space alien. It's big data. "The 21st century is a digital book," the captain is told. "Your bank records, medical histories, voting patterns, emails, phone calls, your damn SAT scores! [Our] algorithm evaluates people's past to predict their future.“

• While most of the people in this room won’t go as far as Hydra in eliminating the competition, we can appreciate it’s business context.

• I never did see Moneyball – but the concept of using analytics to win in sports is only getting more extreme!


What influenced the development of ‘Big Data’ solutions?

• Data– Growing data volumes and sources

– Structured (claims, eligibility, etc.) & Un-Structured (social media, images, etc.)

• Platform– A new scalable solution to manage the data and processing (Hadoop & GFS)

• Storage – New ways to store data in various formats (Text, NoSQL, Columnar, etc.)

• Processing– New ways of processing the data (Batch, Stream, In-Memory, etc.)

• Analytics– New Insights

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Big Data Quiz

Which of the following is not one of the 4 V’s of Big Data?

1. Volume

2. Veracity

3. Variety

4. Victory

5. Velocity


What is ‘Big Data’?

“Big data” is high-volume, -velocity and -variety information assets that

demand cost-effective, innovative forms of information processing

for enhanced insight and decision making..

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History of Big Data

The world just wanted a better open-source search engine. Doug Cutting and Mike Cafarella set out to build it. They called their project Nutch.

2002

Sanjay Ghemawat, Howard Gobioff, Shun-Tak Leung release paper on GFS (Google File System)

Sanjay Ghemawat and Jeffery Dean Google release paper on MapReduce

2003

2004

Cutting and Cafarella built up the underlying file systems and processing framework that would become Hadoop (in Java, notably, whereas Google’s MapReduce used C++) and ported Nutch on top of it2005

2006 Cutting goes to Yahoo, to build open source technologies based on GFS and MapReduce. Launches Hadoop as an open-source Apache Software Foundation project.

Christophe Bisciglia, a Google engineer, begins teaching a computer science course at the University of Washington focusing on web-scale data. Google’s MapReduce code was proprietary, so adopts Hadoop as a proxy.

With Google’s blessing, Bisciglia incorporated a company called Cloudera in March 2008 and closed its first funding round in April 2009.

Slow march of horizontal scalability, growing Hadoop’s capabilities from the single digits of nodes into the thousands, every factor of 2 or 1.5 required re-engineering.

2007

2008

2009

Yahoo spins off Hortonworks as a separate company in June of 2011.

2011


What is Big Data?

The characteristics of Big Data (aka Hadoop)Scalable

Fault-tolerant

Open Source

Distributed system for

• Data Storage

• Processing

Commodity Hardware

• The components of HadoopDistributed File System (HDFS) – the storage manager

MapReduce – the programming model

YARN – the resource manager

Common libraries and utilities

http://en.wikipedia.org/wiki/Hadoop

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The ‘Old’ Data Management

We are leaving the old age of data storage and processing with finite and absolute use cases.

– Storage limited to files and relational data stores

– Processing options were limited to scripting, ETL tools and in database processing


Age of ‘New’ Data Management

Data storage and processing are no longer finite and absolute.

The new goal is matching storage and processing, which continue to expand.

ASCII FileProtocolBuffers, Thrift and Avro serializationSequence FileRecordColumnar FileORC (Optimized RC) File

Key-ValueColumn FamilyDocument

Property GraphRDF/Triple Stores

Row OrientedColumn Oriented

Spectrum of storage options:

Spectrum of processing options:

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Proliferation of ‘New’ Data Management Tools

Courtesy of Knowledgent (http://knowledgent.com)


The ‘New’ Data Management

The aspiration is to match storage, processing and analytics to create new value.

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Non-Techie Spin on the “New” World

• Data, Storage, Processing and Analytics are all increasing rapidly and not necessarily sync

• Storage has evolved to handle non-structured data in non-relational databases

• In many cases, Big Data Analytics are being used today even on non-Big Data assets

• Actuaries need to evolve or will be replaced by AI beings with better personalities!


Big Data Industry Use Cases

Courtesy of Knowledgent (http://knowledgent.com)

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Use Cases in Healthcare

Payer

Member

Provider

Health ProfilesHelping insurance payers

understand the risk profile of their members administrative

data (claims, labs, etc.)

Value Based ReimbursementHelping insurance payers understand and measure provider quality and efficiency.

Clinical RiskHelping providers understand the

risk profile of their members using medical records.


Examples of Big Data Applications for the Dental Space

• Provider Profiling & Fraud Detection in Real Time

• Customer Service Integrated Cross-Platform / Cross-Product Views

• Optimized work assignment for underwriters and claim adjudicators

• Risk Evaluations based upon Claims, HRA and EHR Data

• Predictive and Prioritized Network Recruiting

• Guided Customized Product Enrollments

• Outcomes Based Reimbursement

• Claim Review performed by AI

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Big Data Adoption

Gartner Survey Reveals That 64 Percent of Organizations Have Invested or Plan to Invest in Big Data in 2013

– Big data investments in 2013 continue to rise, with 64 percent of organizations investing or planning to invest in big data technology. However, less than eight percent of survey respondents have actually deployed.

– Of the 64 percent of organizations investing or planning to invest in big data technology in 2013, 30 percent have already invested in big data technology, 19 percent plan to invest within the next year, and an additional 15 percent plan to invest within two years.

– Industries leading big data investments in 2013 are media and communications, banking, and services. Thirty-nine percent of media and communications organizations said that they have already invested in big data, followed by 34 percent of banking organizations and 32 percent of services firms.

– Planned investments during the next two years are highest for transportation (50 percent), healthcare (41 percent) and insurance (40 percent). However, every vertical industry again shows big data investment and planned investment.

http://www.gartner.com/newsroom/id/2593815


Big Data & The Hype

Big Data is approaching the ‘peak of inflated expectations’.

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The Pending ‘Trough’?

“In a study of more than 100 data scientists .. 71 percent of data scientists believe their jobs have grown more difficult as a result of a multiplying variety of data sources, not just data volume.”1

“Only 48 percent of respondents said they had used Hadoop or Spark for their work and 76 percent felt Hadoop is too slow, takes too much effort to program or has other limitations” 1

“Hadoop has been unrealistically hyped as a universal, disruptive big data solution, noting that it is not a viable solution for some use cases that require complex analytics.” 1

“Twenty-two percent of the data scientists surveyed said Hadoop and Spark were not well-suited to their analytics. [It was] also found that 35 percent of data scientists who tried Hadoop or Spark have stopped using it.” 1

1. http://www.computerworld.com/article/2489767/big-data/data-scientists-frustrated-by-data-variety--find-hadoop-limiting.html


Big Data Challenges

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Non-Techie Translation on the Big Data Challenges

• If you haven’t put this to good use yet, you’re in good company

• The tools are still evolving

• If you want to succeed, you need a good consultant!

Thank you.

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http://nadpconverge.org/evaluations

big data: opportunities for the dental benefits industry › 2014 › 02 › ... · 2014-09-08 ·...

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