Semantic Technology for the Data Warehousing Practitioner -- Shattering Traditional DW/BI Best Practices to Drive Intelligent Analytics Current DW/BI best practices optimize technologies that were conceived two to three decades ago. To successfully leverage semantic technology, DW/BI professionals will change (even reverse) many of these practices. Many organizations use data warehousing and business intelligence to monitor their operations and guide tactical and strategic decision making. Data warehouses continue to have challenges in: - keeping the data organized in sync with the organization's analytics needs, - delivering data to decision makers in a timely manner, - managing constantly-evolving data quality requirements, - integrating new data sets into the data warehouse, - reusing expert knowledge that is embedded in end-user analytics, and - organizing internal data assets, data from cloud applications, and data from business partners into a common access method. Many of today's DW/BI practices were developed to optimize technologies that were conceived in the 1970's, 80's, and 90's. This presentation examines key features of semantic technology and how DW/BI practices are likely to change to successfully deliver intelligent DW/BI projects.

1. Semantic Technology for
the Data Warehousing
Practitioner
Shattering Traditional DW/BI Best Practices to
Drive Intelligent Analytics
June 2-5, 2013
Speaker
Thomas Kelly
Practice Director, Life Sciences
Enterprise Information Management
Cognizant Technology Solutions, Inc.
©2013, Cognizant

2. Agenda
1
Why the Data Warehouse is Important
2
Observations on Project Execution
3
Changing DW/BI Practices through Semantic Technology
4
A New Generation of Data Warehousing and Business Intelligence
2 | ©2013, Cognizant

3. Data Warehousing Keeps the Business Running, While Delivering Information &
Insights
Objectives
• Performance
• Predictable Results; Consistent
Reports
• Maintain History
Traditional Enablers
• Most data was well-understood
• New data sources emerged only
occasionally
• Relational and well-structured
data (sources)
3 | ©2013, Cognizant

4. Many Successful DW Projects have had Challenges
•
•
Get the … right, or the … breaks
•
Speed of business change
•
More data is available, from new sources
•
We need data faster – closer to time of creation
•
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Lengthy time-to-business value
Developing expertise

5. Semantic Technology is about Standards, Products, and Techniques
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6. Semantic Technology Features that enable Agile Data Warehousing
Extensible Ontologies
Entity Resolution
Expert Knowledge
Data Virtualization
Provenance
Data Federation
Linked Data
6 | ©2013, Cognizant

7. Fresh, Never Frozen Requirements
Requirements
described during the
“Requirements”
phase
Requirements that
are unearthed in
later phases
Now that we’ve discussed my requirements,
how soon can I get my new reports?
7 | ©2013, Cognizant
• Semantic Technology provides
highly flexible and extensible
features
• Better support for agile
development (ongoing
requirements definition and
prioritization, managed through
timeboxing)
• Extend the data model without
breaking dependent data loading
and analytics functions

8. Evolutionary Data Modeling
ISBN
ISBN
Title
Language
Title
Author
Author
Publisher
Publisher
ISBN
ISBN
ISBN
Language
bookURI
ISBN
title
8 | ©2013, Cognizant
Title
ISBN
Language
Language
Publisher
Author
“A Game of Thrones (A Song of Ice and Fire, Book 1)” @en
“Le trône de fer : L'intégrale, tome 1 “ @fr
“漫画系列•冰与火之歌漫画:权力的游戏(第1卷) [平装]“ @ch
“Игра Престолов” @ru
“Juego de Tronos” @es

9. Multi-Dimensional Data Quality Management
And
Here
Traditional Data Warehouse
Data Quality
Happens Here
Data
Source A
Data
Source B
Data
Warehouse
Data Quality
Happens Here
Data
Source C
Data
Store B
9 | ©2013, Cognizant
Data
Store C
And
Here
DepartmentLevel View
DivisionLevel View
Data
Store A
Semantic Data Warehouse
My
View
And
Here
And
Here
Enterprise
Data
Warehouse
DivisionLevel View
DivisionLevel View
And
Here
Your
View
And
Here

10. Minimizing Data Movement
Traditional Data Warehouse
Data Warehouse
Data Mart
Data
Source
Landing
Zone
Staging
Area
Integrated
Store
Analytics
Layer
Data Mart
Semantic Data Warehouse
Data
Source
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Data Warehouse

11. Build Links, Not Storage Farms
Traditional Data Warehouse
Data
Source
Data
Source
Data
Source
Data
Source
Data Warehouse
Data
Source
“The Preferred
Repository”
Data
Source
Semantic Data Warehouse
Data
Source
Data
Source
Data
Source
11 | ©2013, Cognizant
Data
Source
Data
Source
Data
Source
One Stop
Access Point

12. Embedding Expert Knowledge
SNOMED Clinical Terms Ontology
Integrating Expertise: Selecting for Hypothyroidism
Case Medications
Levothyroxine, synthroid,
levoxyl unithroid, armour
thyroid, desicated thyroid,
cytomel, triostat,
liothyronine, synthetic
trilodothyronine, liotrix,
thyrolar
Pregnancy Exclusion
ICD-9 Codes
Any pregnancy billing code
or lab test if all Case
Definition codes, labs, or
medications fall within 6
months before pregnancy
to one year after
pregnancy
V22.1, V22.2, 631, 633,
633.0, 633.00, 633.1,
633.10, 633.20, 633.8,
633.80, 633.9, 633.90,
645.1, 645.2, 646.8, etc.
ICD-9 Codes for Hypothyroidism
244, 244.8, 244.9, 245, 245.2, 245.8, 245.9
sno:40930008 ID
sno:40930008 Preferred Name
Case Definition
ICD-9 Codes for Secondary
Causes of Hypothyroidism
244.0, 244.1, 244.2, 244.3
40930008
Hypothyroidism
ICD-9 Codes for Post
Surgical or Post Radiation
Hypothyroidism
All three conditions required:
193*, 242.0, 242.1, 242.2,
1. ICD-9 code for hypothyroidism OR abnormal TSH/FT4
icd9:244
ID
244
242.3, 242.9, 244.0, 244.1,
2. Thyroid replacement medication use
icd9:244 at least Preferred Name medication or lab
Acquired hypothyroidism
244.2, 244.3, 258*
3. Require
2 instances of either
icd9:244.8
ID
244.8
with at least 3 months between the first and last
icd9:244.8 of medication and lab
Preferred Name Other specified acquired CPT Codes for Post
instance
Radiation Hypothyroidism
hypothyroidism
77261, 77262, 77263, 77280,
77285, 77290, 77295, 77299,
ind:4093008
40930008
Case Exclusions ID
77300, 77301, 77305, 77310,
{ SELECT the following information occurssno:40930008
Exclude if DISTINCT ?patientID,any time in
at ?patientName etc.
ind:4093008
Defined By
the record:
ind:4093008
Inclusion ICD
icd9:244
• Secondary causes of hypothyroidism
Exclusion Keywords
icd9:244.8
WHERE surgical or post radiation hypothyroidism
• Post
Multiple endocrine neoplasia,
ind:4093008
Exclusion ICD
icd9:631
•{ Other thyroid diseases
MEN I, MENII, thyroid cancer,
icd9:633
• Thyroid altering medication
thyroid carcinoma
?patient ?indication “HYPOTHYROIDISM”
SPARQL query (abbrieviated)
}
}
Case Exclusions
Exclusion Keywords
Time dependent case exclusions:
Optiray, radiocontrast,
• Recent pregnancy TSH/FT4
iodine, omnipaque,
• Recent contrast exposure
visipaque, hypaque,
ioversol, diatrizoate,
iodixanol, isovue,
iopamidol, conray,
iothalamate, renografin,
sinografin, cystografin,
Source: SNOMED-CT Ontology, IHTSDO
conray, iodipamide
12 | ©2013, Cognizant
Abnormal Lab Values
TSH > 5 OR FT4 < 0.5
Conway et al.; Denny et al.
Thyroid-Altering Medications
Phenytoin, Dilantin, Infatabs,
Dilantin Kapseals, Dilantin-125,
Phenytek, Amiocarone
Pacerone, Cordarone, Lithium,
Eskalith, Lithobid,
Methimazole, Tapazole,
Northyx, Propylthiouracil, PTU
Reprinted with permission from Jyotishman Pathak, Ph.D., Mayo Clinic

13. Researching, Analyzing, Justifying, Socializing, and Pleading for Approval of the
Project Business Case
• Justify an exploratory project to
prove and demonstrate value
• Focus efforts on incremental
improvements that achieve a positive
result
• Justify further work based on success
13 | ©2013, Cognizant

14. Semantic Technology-enabled Data Warehousing: Features at a Glance
Features
Traditional Technology
and Practices
Semantic Technology
and Practices
Requirements Gathering and Analysis
• Capture requirements and freeze
early
• Manage change
• Capture and validate initial
requirements
• Adjust and fine tune
• Prioritize new requirements
Data Modeling
Thorough upfront analysis to avoid
rework later
• Expect change
• Agile, evolutionary
Data Latency
“Yesterday’s data is available today, if
it all loaded ontime”
“The pricing data is updated in realtime”
Data Infrastructure
Bring all of the data in house
• Leverage external data
• Cache locally to address
performance / reliability
Deliver Business Value
Correctly define, design, build, and
document everything before
delivering value
Deliver value early and often
14 | ©2013, Cognizant

15. Questions?
©2013, Cognizant

16. Thank you
©2013, Cognizant

17. Speaker
Thomas (Tom) Kelly
Practice Director, EIM Life Sciences, Cognizant
Thomas is a Practice Leader in Cognizant’s Enterprise
Information Management (EIM) Practice, with over 30
years of experience, focusing on leading Data
Warehousing, Business Intelligence, and Big Data
projects that deliver value to Life Sciences and related
health industries clients.
thomas.kelly@cognizant.com
©2013, Cognizant