Download pptx - #HASummit14 Session #10: Is Big Data a Big Deal… or Not? Pre-Session Poll Question Is big data a big deal for healthcare? a)Most definitely. Big data will

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Session #10:

Is Big Data a Big Deal… or Not?

Pre-Session Poll Question

Is big data a big deal for healthcare?

a) Most definitely. Big data will revolutionize the way we look at and practice healthcare.

b) Yes, but it’s overhyped and at least 3 years away.c) I haven’t decided yet.d) Not really; humans are too complicated to fully understand

with data.e) Definitely not. Like EHRs, it’s just the next big thing with no

positive impact.

Dale SandersEVP, Product Development | Health Catalyst

Richard ProctorGM Healthcare | Hortonworks

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You are in the technology-enabled services business…you leverage technology to

deliver the highest-value professional health management services.

Your business now runs at the speed of software

The Adoption Of

EHRs

Meaningful Use

Patient Engagement

Care Management

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The “Empowered” Patient

Relationship Focused(Values stable doctor’srelationships)

Detests/suspicious of Gadgets

Serial Processor(Email, basic model phone)

Loyalty to Brand

Health as a service

Access to Care

Patient (willing to wait)

Believes experts (not comfortable seeking second opinions)

Goes for Best of Breed (Network of Networks)

Super Connected

Parallel Processor-Integrated (State-of-the-Art)

Loyalty to Value

Take care of Health

Health as a right

Impatient

Asks for Data (researches multiple self enabled searches,

demands second opinions)

My Parents My Children

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Healthcare providers are the only entity that are currently capable of changing the system

Providers Patients Employers

Payers

Change Comes From The Margins

“You signal your determination to belong to the center by unreserved conformation to its standards. For all these elaborate protocols are also meant to ensure that not everybody gets in and that enough are left out; this way the center makes itself perpetually desirable. Since most do what they can to keep their place within the status quo, far-reaching innovation is usually discouraged, and conformism rules.” – Professor Costica Bradatan, Texas Tech University

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Digitization of Personalized, Precision HealthIt’s just beginning…

The Ecosystem of Human Health Data

Healthcare Encounter

Data

7x24 Biometric

Data

Outcomes Data

Consumer Data

Genomic and

Familial Data

Social Data

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Late Binding in Data Engineering

Real Life Data Is Also Alive

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Relational vs. Hadoop Analytic Technology

"Technology Life Cycle". Licensed under CC BY-SA 3.0 via Wikimedia Commons – https://commons.wikimedia.org/wiki/File:Tecnology_Life_Cycle.png

0 Time

Bus

ines

s G

ain

Vital Life

R&D

The Technology Lifecycle Path

A DL

M Maturity

HadoopRelational databases for analytics

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Big Data vs. Throughput

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Big Data vs. Throughput

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Volume, Velocity, and Variety of Data

What is so compelling about Hadoop in comparison to relational database technology?

• Key value data vs. relational data: XML, text

• Declarative vs. procedural programming

• Complex analytic flows with multiple inputs and outputs

• YARN, HIVE

• Optimization for relational data base engines is a black art; not so with Hadoop

• Blazing fast performance allows for complex algorithmic/machine learning/modeling, including real time analytics

• Granular security

• Scale out, not up. A 4x server is way more expensive than adding 4 PCs to a node

• Licensing savings

• Structured vs. Unstructured data… don’t wait until unstructured data is knocking on your door. Prepare now.

• 90% of all information created by humans originated in the last 2 years• We are producing 3 exabytes of data per day… 1 million terabytes

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The 5 Key Pillars of HDP

HDP 2.1Hortonworks Data Platform

Provision, Manage, & Monitor

Scheduling

Data Workflow, Lifecycle, & Governance

YARN: Data Operating System

Script SearchSQL NoSQL Stream OthersIn-Memory

GOVERNANCE &

INTEGRATIONOPERATIONS

AuthenticationAuthorizationAccounting

Data Protection

SECURITY

BATCH, INTERACTIVE, & REAL-TIME DATA ACCESS

Deployment Choice

Linux Windows On-Premise Cloud

HDFS (Hadoop Distributed File System)

DATA MANAGEMENT

Hadoop EcosystemThis slide was outdated the moment I borrowed it.

https://hadoopecosystemtable.github.io/ for the latest

Thank you for the graphic, Aryan Nava

• It’s the community of Hadoop that makes the difference.

• It’s not the technology that matters.

• It’s how you leverage the technology.

• Hadoop is like an operating system for data management.

• It grew up in the era of data and leapfrogged Oracle, Microsoft, and IBM.

https://hadoopecosystemtable.github.io/

https://hadoopecosystemtable.github.io/

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Gartner Survey 2015Hadoop Investment Plans

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Obstacles to Hadoop adoption

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Use Cases

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Commodity Compute and Storage

SAN

MPP

Engineered System

NAS

HADOOP

Cloud Storage

0 20000 40000 60000 80000 100000 120000 140000 160000 180000

TRUECarStorage costs/Compute costs from

$19/GB to $0.23/GB

UCIrvineStorage costs and licensing reduction

of latent systems

$500,000

ZirMed5 times the amount of usable

storage and processing power for about 30% the cost of traditional

enterprise technologies

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EDW Optimization

Current Reality Augmented w/ Hadoop

• Free up EDW resources from low-value tasks

• Keep 100% of source data and historical data for ongoing exploration

• Mine data for value after loading it because ofschema-on-read

Analytics

20%Operations

50%

ETL Process

30%

Operations

50%Analytics

50%

• EDW at low capacity: Some usage from low value workloads

• Older data archived, unavailable for ongoing exploration

• Source data often discarded

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Business Analytics

Custom Applications

Packaged Applications

Mercy HealthInitial bulk load via sqoop from Oracle/Clarity EDW to HDP.They then capture deltas every3-5 minutes from cache into HDP.

Near Real-Time Analytics with Epic

EpicOracle Clarity

Near Real-time Sqoop

Applications

Data Access

Data ManagementG

ov

ern

an

ce

& I

mm

igra

tio

n

Se

cu

rity

Op

era

tio

ns

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Big DataBig Data is not just for the

IT department anymore

Benefits• Proactively predict events rather

than reactively

• Real-time alerts

• Capture and transmit patient vitals at much higher frequencies

• Improve patient satisfaction

• Improve operational efficiency

• Improved response times

• Reduce adverse drug response times

Monitor Patient Vitals in Real-Time with Sensor Data

ProblemManaging the volumes of system sensor data

In a typical hospital setting, nurses do rounds and manually monitor patient vital signs. They may visit each bed every few hours to measure and record vital signs, but the patient’s condition may decline between the time of scheduled visits.

This means that caregivers often respond to problems reactively, in situations where arriving earlier may have made a huge difference in the patient’s well being.

SolutionHadoop empowers healthcare by converting high volumes of sensor data into a manageable set of data

New wireless sensors can capture and transmit patient vitals at much higher frequencies, and these measurements can stream into a Hadoop cluster.

Caregivers can use these signals for real-time alerts to respond more promptly to unexpected changes.

Over time, this data can go into algorithms that proactively predict the likelihood of an emergency even before that could be detected with a bedside visit.

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ProblemSlow delivery of medical products can waste supplies, increase costs, and harm medical outcomesMedical supplies and pharmaceuticals are time sensitive and climate-controlled

Epidemics require agile changes to delivery schedules

Complex delivery logistics are complex and subject to risks outside of the company’s control (e.g. product availability, weather, and traffic)

SolutionMonitoring with sensor data protects the supply chain and reduces waste today, and improves logistics in the futureData from SAP and EDI in HDP gives unprecedented supply chain visibility

ETL offload increased data retention from 1 to 7 years of data, with daily updates

Better tracking reduces waste, improves customer confidence, and patient health

Historical data informs long-term strategic investment decisions

Why Hadoop?Data discovery

HealthcareSupplier of pharmaceuticals and medical products to pharmacies and hospitals

Monitoring of Healthcare Supply Chain to Minimize Waste

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HealthcarePublic university teaching hospital

ProblemInability to store and access sufficient data for medical decision support in real time9 million patient records on a legacy system were not searchable nor retrievable

Cohort selection for research projects was slow, despite abundance of data

Clinicians had minimal access to historical data gathered across all patients

Solution

Why Hadoop?Predictive analytics

Unified data lake improves patient health, speeds researchLegacy system retired immediately, saving $500K in annual recurring expense

Records stored with patient identification for clinical use, same data presented anonymously to researchers for cohort selection

Wireless patches transmit vital signs, algorithms notify doctors of high risk patterns

Heart patients weigh themselves from home, algorithms notify doctors about unsafe weight changes and recommend a visit to the clinic

Improve Patient Treatment with Real-time Monitoring of Vital Signs

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Role at MerckManufacturing innovation and analytics at Merck

SolutionSingle view of data is the “holy grail” for yield and quality optimization• How to predict when a machine is going to fail

• How to improve yield and enable feedback control

• How this is being enabled by the Hortonworks Data Platform

Plans for the Future• Analyze streaming machine sensor data in real time

• Proactively minimize yield variability

• Predictive equipment maintenance

Key Challenges • Data silos

• High cost of data retention

• High cost of testing hypothesis in the real world

Yield, Quality, and Process Optimization at Merck

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ManufacturingMajor automotive OEM

ProblemAutomaker sought to transform auto manufacturing by pursuing multiple big data use cases Two mature enterprise warehouses (Microsoft and SAP) required cost-prohibitive ETL processing to capture sensor and machine data with variable structures

Company CIO mandated first Hadoop cluster within 7-month time frame

Goal: archive 1.4 petabytes of manufacturing data for predictive analytics

SolutionHDP Data Lake established as Hadoop architectural standard to support multiple big data use cases, including….Manufacturing optimization: Two-mile auto painting assembly line monitored with more than 100K sensors measuring temperature, humidity, and paint mixtures

Streaming usage data from cars (post-sale) for valuable engineering insight

Predictive analytics for extreme testing send alerts to prevent engine explosions

Data-as-a-service for retailers on returns and sales velocity

Why Hadoop?Predictive analytics

Enterprise Data Lake for Auto Manufacturing

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Lessons Learned

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1. Margin vs. Marginalized: What sort of healthcare legacy do you want to leave behind?

2. Technology Enabled Services: Like it or not, fast or slow, healthcare runs at the speed of software now.

3. No Question: The Hadoop ecosystem is revolutionizing data management and analytics.

4. Adoption Curve: Start learning and adopting now; be ready for full adoption in 2 to 3 years.

5. The Hadoop Community: Incredible velocity and quality of collaboration to commoditize this revolutionary technology. It’s not the technology that matters; it’s how you use it.

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Analytic Insights

AQuestions &

Answers

27

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Choose one thing…

28

Write down one thing will you do differently after hearing this presentation

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Thank you!

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Session Feedback Survey

1. On a scale of 1-5, how satisfied were you overall with this session?

1) Not at all satisfied

2) Somewhat satisfied

3) Moderately satisfied

4) Very satisfied

5) Extremely satisfied

2. What feedback or suggestions do you have?

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Upcoming Speakers

3:45 PM – 4:35 PM

16) Delivering Excellence at Stanford Health Care

Amir Dan Rubin, President and CEO, Stanford Health Care

4:35 PM – 5:00 PM

17) The Future World of Value-Based Healthcare (Documentary featuring Michael Porter)

Caleb Stowell, MD, Vice President, Research and Development, International Consortium for Health Outcomes Measurement (ICHOM, Senior Researcher, Harvard Business School)

Location

Grand Ballroom

Grand Ballroom