Seattle Children’s Hospital

Preparing for the Future with PureData for

Analytics

4/10/2013

Who am I and Why am I Here?

Wendy Soethe

Manager, EDW & BI

Knowledge Management

Information Services

Seattle Children’s Hospital

Seattle Children’s Hospital

Hospital Statistics – FY 2012

• Location: Seattle, WA

• Includes Seattle Children’s Hospital,

Research Institute and Foundation

• Licensed beds: 254

• Total Employees: 5,195

• Active Medical Staff: 1,189

• Hospital Admissions: 14,498

• Clinic Visits: 290,671

• ED Visits: 32,810

Our Mission

We believe all children have

unique needs and should

grow up without illness or

injury. With the support of

the community and through

our spirit of inquiry,

we will prevent, treat and

eliminate pediatric disease.

Seattle Children’s Hospital

Integrated Data Journey

• 2007 and prior • Decision support: manual processes to extract data, Invision into TSI

• 2008 • Rolled out PowerInsight with BOE for Cerner reporting needs

• Epic/Clarity go-live; Crystal reports for Revenue Cycle, ADT, Coding data

• Signed deal with MS Amalga v1.5 for integrated data (“Alpha” Partners)

• 2009 • Initiated Microsoft BI program to augment Amalga

• Continued Amalga development led by MS – moved to v2.0

• 2011 • Replaced Amalga with more traditional SQL data warehouse environment as an

interim solution

• Rolled out Tableau to promote self service and support power users

• Focused on key initiatives to drive EDW work (i.e., CSW)

• Conducted DWA Assessment with Brightlight Consulting

• 2012-2013

• Conducted POC which became part of pilot implementing IBM PureData System for Analytics powered by Netezza technology

• Integrating more data monthly

Current Data Warehouse Profile

• 8 team members

• 1 Data Architect, 1 DBA, 4 EDW/BI Developers, 1

DA/Developer

• Recently moved off SQL Server to a PureData for

Analytics/Netezza DWA based EDW

• Currently 10 source systems and 10 CSV files

• End users access via BI solutions built in the Microsoft

stack, Tableau and BOE

Interim EDW Architecture

Interim EDW Architecture

Integration

KM EDW and Data Marts Target

Acquisition BI Portals and Tools

BO InfoView Prod Portal

(Clinical and Revenue Cycle

Reporting)

ERP Lawson Portal(GL and Payroll

Reporting)

Tableau Portal(Organizational Dashboards and

Reporting)

Knowledge Exchange

SharePoint Portal (Inpatient Access,

SC, HEAT Dashboards &

Reports)

Epic

Lawson

Active Dir

SoftMed

Center Point

MSOW

Source Replication DB

Cerner

EPSi

Clarity

UMRA

Center Point (Backup)

CIS_PRDLOGIC

KM EDW Stage

PortalsTools

Tableau

Excel

SSRS

BO

Crystal

SQL

Excel Departmental

SoftMed (Backup)

KM EDW Views and OLAP Cubes

Distribution

ExistingIn Progress, Currently

ApprovedLong Term Vision

Genomic, M2M, EMR, HL7, HIE,

Clinical/Regulatory 3rd Party,

DATSTAT, TSI, PHIS, CHARS,

ClinDoc, Other

EDW Assessment with Brightlight

EDW Assessment Approach

• Analyzed the Children’s unique business intelligence needs through

on-site interviews with key business and technical team members

(total 26 individuals)

• Reviewed the BI environment and key documentation

• Mapped assessment findings against Brightlight Consulting’s

extensive business intelligence knowledge base and experiences and

against EDW environments at various other companies

• Developed reports of preliminary findings and recommendations that

were presented and reviewed with key KM representatives

• Prepare a final report of findings and recommendations for utilizing

business intelligence at Seattle Children’s (SCH)

Key Challenges

• Rapidly increasing demand for integrated data

• Excessive time to provision new storage to meet demands (3 to 6

months)

• EDW architecture inefficient and old

• Existing infrastructure is not engineered for high performance

analytics (advanced analytical computations and fast query

performance on large complex data volumes)

• No reliable server failover for Prod as well as for Test and Dev (if

Prod server goes down, Test is used as a temp solution until

Prod is up again)

• Data movement across 20+ servers (Dev, Test, Prod)

• All KM servers provisioned for Amalga – approaching 4 years old

Key Challenges, Continued

• Knowledge Management cannot keep up with data demands of

strategic initiatives

• KM EDW/BI team spends more time tuning inefficient EDW

architecture and less time taking on new data integration projects

• KM team spends more time satisfying one-off requests than

focusing on larger strategic initiatives

• KM Analysts spend most of the time performing manual data

integration tasks. Such activities do not result in creating a

repeatable process, and manually integrated data cannot be

automatically refreshed and re-used.

What Was Attractive About a DWA

• Purpose Built

• Database, Server, and Storage tightly configured

• MPP - Optimized for analytical processing

• High Performance

• SQL 10–1000x faster

• Very fast loads (750+ gb / hour)

• Simplicity

• Faster deployment

• Fewer resources to manage

Solution Options (the Short List)

Option Benefits Risks/Concerns

Maintain Status Quo

• No new training of staff. • Time lag for server/storage provisioning; • Long project cycles; • Limited bandwidth for new projects; • Not scalable with FTE count; • ETL and query performance; • Ability to integrate data sets

PureData for Analytics Data Warehouse Appliance

• Accelerate Time to Market and BI Throughput;

• Increase number of strategic projects;

• Decrease FTE cost to maintain infrastructure;

• Eliminate storage bottleneck; • Add capacity for future growth; • Decrease number of EDW servers; • Introduce failover/recovery; • Introduce flexible analytical sand-box

environment

• New approach, requires training and consulting;

• New technology

Strategic Questions Require Access to Integrated Data

Estimated BI Throughput and Time to Value

Existing vs. PureData for Analytics

Projects 2012-2016 Existing PureData for Analytics

Var. %

EDW Hardened Projects 11 22 194%

EDW Sandbox Projects 0 50 100%

Total EDW Projects 11 72 640%

More Projects in PureData for Analytics by 2016

because:

• 3 to 6 months storage related bottlenecks are

completely eliminated

• 1,800 fewer FTE days are required to maintain and

tune the system, and therefore most of this time can be

re-invested back into new project development

• An environment engineered specifically for EDW

enables more efficient and agile development.

Therefore, it will cost $78K or 37% less to produce one

medium-sized EDW Project from the FTE cost

perspective

• Sandbox environments enable support of ad hoc

projects (at least 10 projects per year)

PureData for Analytics

Existing

Additional Benefits

• ROI for advanced BI and analytical capability provided

• Additional Storage

• PureData for Analytics 96 TB Storage vs. Existing 30

TB Storage

• Support Sandbox environments and other growth

• Phase out development VMware instances

• Advanced monitoring capability provided in Brightlight

Managed Services

SCH Data Volume Growth Analysis for the Next 5 Years

• Due to increasing needs for information and analytics at

SCH, the data warehoused data volume may increase

by 440% in the next 5 years

• In a traditional data warehouse environment, the EDW

would reach 4.6 Tb to host data as well as indexes for

performance optimization

• In an environment engineered specifically for DW,

storage requirement could be potentially lower by 25%

and the data volume could reach 3.7 Tb

Findings / DWA Impact

• EDW has an estimated yearly data volume growth of

135% on an average within the next 5 years

• The growth rate could be higher if the EDW

organizational and technical constraints were lifted to

enable higher BI projects throughput

• Additional unplanned data sources and environments

could potentially emerge due to changed business

priorities, including unstructured data

• Expanded service to more patients in existing and newly

built facilities could result in an increased data flow

Assessment Conclusions

• DWA can offer high capacity solutions to accommodate

large data volumes for initial historical data loads and

future growth with minimum efforts to manage storage

• DWA can eliminate additional storage requirements

needed in a traditional DW environment, e.g. support for

indexes, temp space, aggregate tables, and cubes

• DWA simplifies the environment

DWA Project with IBM and Brightlight

Guiding Principles

• Create a data warehouse that is identified as stable and dependable

by the business

• Reduce and simplify the data movement from one platform to

another platform

• Consolidate data within the enterprise

• Maintain or improve the security of the business data

• Improve the flexibility and resilience of the data load processes and

the data services

• Lower the long-term Total Cost of Ownership

• Turn “business data” into “business information” faster

• Create data warehouse services that provide for flexible information

consumption

• Integrate with the existing self-service environment

DWA Project Scope

• Create a detailed plan for a Phase One implementation

of a new BI/DW solution centered on a Data Warehouse

Appliance (DWA), including plans to execute an initial

POC to meet acceptance criteria clause

• Setup, configuration, and establishment of a new

Linux/UNIX based Data Acquisition layer (Dev, Test and

Prod)

• Installation, setup and configuration of the Brightlight

Data Integration Framework (nzDIF) for Development,

Test, Pre-Production and Production

• Integrate SCH security and access requirements for the

Landing Zone and DWA

DWA Project Scope, Continued

• Land source data sets, that were part of the existing

EDW solution, into the new Data Acquisition layer

• Migrate the existing EDW ETL processes into the

Brightlight Data Integration Framework

• Clone the reporting environments off of the existing EDW

into new environments that will point at the DWA

• Execute the DWA criteria acceptance plan

• Grow team skill sets through knowledge transfer, best

practices, and DWA subject matter expertise from

Brightlight Consulting

DWA Project Schedule

Summary of POC Results

Gaps/Challenges

• Data Model

• Improvements to the Source Extraction Layer

• Data Governance

• Organizational Engagement/Governance

• Blob/Row Limits in PureData for Analytics 64k

Anticipated Project Impacts

• Established BI Architecture that will serve the

organization for at least 5 years

• Decrease time to delivery for KM team

• Add Sandbox functionality to allow more participation in

building and testing BI solutions within the organization

• Provide integrated data that the organization has never

had before, to answer more complex questions, more

efficiently

• Begin to address unstructured data – large amount of

clinical data is unstructured

Current EDW Architecture

Integration

KM EDW and Data Marts Target

Acquisition BI Portals and Tools

BO InfoView Prod Portal

(Clinical and Revenue Cycle

Reporting)

ERP Lawson Portal(GL and Payroll

Reporting)

Tableau Portal(Organizational Dashboards and

Reporting)

Knowledge Exchange

SharePoint Portal (Inpatient Access,

SC, HEAT Dashboards &

Reports)

Epic

Lawson

Active Dir

SoftMed

Center Point

MSOW

Source Replication DB

Cerner

EPSi

Clarity

UMRA

Center Point (Backup)

CIS_PRDLOGIC

KM EDW Stage

PortalsTools

Tableau

Excel

SSRS

BO

Crystal

SQL

Excel Departmental

SoftMed (Backup)

KM EDW Views and OLAP Cubes

Distribution

ExistingIn Progress, Currently

ApprovedLong Term Vision

Genomic, M2M, EMR, HL7, HIE,

Clinical/Regulatory 3rd Party,

DATSTAT, TSI, PHIS, CHARS,,

Other

CUMG

Conclusion

PureData for Analytics:

Setting SCH up for Success with Big Data

• As an internal and external demand for an integrated

and high-quality data is growing exponentially, enabling

self-service BI and advanced analytics have become the

key EDW goals at SCH

• SCH requires a robust platform that enables insight into

performance across multiple business processes and

research

• EDW has major opportunities to provide deep insight into

the SCH business centered around patient care,

enhance the quality of research, and promote a metrics

based decision-making

Q & A

Contact Information

Wendy Soethe

Manager, EDW & BI

Knowledge Management

Information Services

Seattle Children’s Hospital wendy.soethe@seattlechildrens.org

Thanks!