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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!
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