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www.silvercreeksystems.com
Product Data Mastering for Successful MDM
Silver Creek Systems & Cardinal Health
MDM Summit,August 26, 2009
Silver Creek Systems ©2009 2
Agenda
Automated Data Mastering• The Product Data Problem• Semantic-based Product Data Mastering
Cardinal Health: Product Data Mastering of MDM• Background and Data Challenges• MDM Initiative• Product Data Quality & Governance• Tools and Approach• Status and Business Benefits
www.silvercreeksystems.com
Automated Product Data Mastering
Silver Creek Systems ©2009 4
The Data Problem
What is this?
How many different ways can you receive information?
How many ways do you need it?
10hp motor 115V Yoke mount10hp motor 115V Yoke mount
mtr, ac(115) 10 horsepower 115voltsmtr, ac(115) 10 horsepower 115volts
MOT-10,115V, 48YZ,YOKEMOT-10,115V, 48YZ,YOKE
This 10hp yoke mounted motor is rated for 115V with a 5 year warrantyThis 10hp yoke mounted motor is rated for 115V with a 5 year warranty
10 Caballos, Motor, 115 Voltios10 Caballos, Motor, 115 Voltios
TEAO HP = 10.0 1725RPM 115V 48YZ YOKE MTRTEAO HP = 10.0 1725RPM 115V 48YZ YOKE MTR
Motor, TEAO, 1725 RPM, 48YZ, 15 Voltios, Montaje de Yugo, hp = 10Motor, TEAO, 1725 RPM, 48YZ, 15 Voltios, Montaje de Yugo, hp = 10
Item MotorClassification 26101600Power 10 horsepowerVoltage 115Mounting Yoke
Item MotorClassification 26101600Power 10 horsepowerVoltage 115Mounting Yoke
Silver Creek Systems ©2009 5
Typical Product Data ProblemsThe greatest threat to your PIM/MDM initiative
Product ID Manufa-cturer
Description Product Type Power Voltage Mount
ABC123 AA Inc. 10hp motor 115V Yoke mount Motor, AC/DC 10hp 115V Yoke
abc-123 A.A. mtr, ac(115) 10 horsepower 115volts AC/DC Motor 10 115 AC.
ABC/123/Q AA/Craft 10 Caballos, Motor, 115 Voltios Mot-AC 10 H-pow 115
QA-ST5 Craft TEAO HP = 10.0 1725RPM 115V 48YZ YOKE MTR 26101604
Z99 Z99 MOT-10,115V,48YZ,YOKE Z99
Motor powered pulley, 3/4" Motor
Product ID Manufa-cturer
Description Product Type Power Voltage Mount
ABC123 AA Inc. 10hp motor 115V Yoke mount Motor, AC/DC 10hp 115V Yoke
abc-123 A.A. mtr, ac(115) 10 horsepower 115volts AC/DC Motor 10 115 AC.
ABC/123/Q AA/Craft 10 Caballos, Motor, 115 Voltios Mot-AC 10 H-pow 115
QA-ST5 Craft TEAO HP = 10.0 1725RPM 115V 48YZ YOKE MTR 26101604
Z99 Z99 MOT-10,115V,48YZ,YOKE Z99
Motor powered pulley, 3/4" Motor
Inconsistent names (representing same business)
(Often) Rich information, but mostly non-standard
Attributes non-standard, missing or invalid
Mis-classified item (not a motor)
Companies struggle with the basics of PIM80% companies are not confident in the
quality of their product data73% find it “difficult” or “impractical” to
standardize product data‘PIM Business & Technology Trends - Survey’, Sept 2007
Inconsistent formats (extra characters
often added)
Inconsistent classifications & misclassifications
Widespread duplication (often hard
to spot)
Silver Creek Systems ©2009 6
Typical Product Data Challenges
Current methods [for product data quality] often don’t work 66% companies use “manual effort” or “custom code”
• 75% say it is too unreliable• 64% say it is too slow• 56% say it is too expensive• >50% say ‘all of the above’
‘PIM Business & Technology Trends - Survey’, Sept 2007
Too complex for custom code
Too ‘messy’ for traditional tools
Too much volume for manual effort
“Poor” data• Little structure• Few standards• Incomplete information
High volumes/Turnover• 10k to 10M items• 5 to 500% turnover (annual)
Multi-domain, Multi-attribute• 5 to 5 thousand “domains” per system• 2 to 30 required attributes per domain/item• Many requirements, validations, rules per attribute
Silver Creek Systems ©2009 7
Product Data MasteringProduct Data Mastering
Product Data Mastering - Semantic Architecture at the Core
Semantic recognition for extraction, standardization & transformation based on meaning and context
Get your data right and keep it right
Combination of Data Quality, Data
Integration & Data Governance to recognize, to recognize,
cleanse, match, govern, cleanse, match, govern, validate, correct and validate, correct and
repurpose product data from repurpose product data from any source to any targetany source to any target
Quality Governance – Data Steward Dashboard
StandardizeStd. Classification(s)Std. AttributesStd. DescriptionsTranslate (inbound)
MatchDe-duplicateMerge
RepurposeTranslate (outbound)(Re-)classify(Re-)standardize(Re-)format
Exception Management – Validation & Remediation
EnrichClassify Extract/append info
Semantic Recognition – Contextual understanding & learning
Silver Creek Systems ©2009 8
Next-Generation Approach
Semantic – Looks for meaning, not patterns
Syntactic – looks for repeated patterns
Basic technology:
Hand-coded – written & maintained by IT
Procedural – IT writes code
Auto-learning – system teaches itself
Code-free – Business user points & clicks
Rules are:
Learning from exceptions:
Traditional Systems DataLens System
Silver Creek Systems ©2009 9
• “One of the most difficult type of data to master is undoubtedly product data – the items, assemblies, parts and SKUs that are core to many businesses.
• Product data is inherently variable, and its lack of structure is generally too much for traditional, pattern-based data qualityapproaches.
• Product and item data requires a semantic-based approach that can quickly adapt and ‘learn’ the nuances of each new product category. With this as a foundation, standardization, validation, matching and repurposing are possible. Without it, the task canbe overwhelming and is likely to include lots of manual effort, lots of custom code – and a whole lot of frustration.”
Andrew White, Research VP, MDM and Supply-chain
Product Data Mastering
Silver Creek Systems ©2009 10
Customer Base: Industry Leading Companies
Leading Healthcare GPO• Eliminate $5MM per year of manual effort• Improve information quality, timeliness and customer
service levels
Office Supplies Company• Reduce manual effort while scaling operations • Increase revenues through better customer experience
and merchandising of high margin items
Industry Leading Food Distributor• Saved at-risk MDM project • Automated a manual process to scale operations
Global Manufacturer• Enforce corporate-wide data standards to accelerate systems
migration• Eliminate large amounts of manual effort and workarounds
Global Retailer• Increase online conversions and search usability • Go live with 1000 product categories in 10 weeks• Empower merchandisers with 40x faster rule changes
Global Healthcare Company • Automating product data quality – as an enterprise-wide
service• Key component of integrated MDM strategy
Product Data Mastering for MDM
Richard HoytMDM Architect
12
Introduction to Cardinal Health
• Founded in 1971 as Cardinal Foods, Inc.• Cardinal Health is a global healthcare company
providing products and services that help improve quality, safety and efficiency all along the chain of care
• $91 billion in annual revenue in FY2008
13
Broad Healthcare Offerings
• Distribute 1/3 of all medicine prescribed in the U.S.
• Manage 275 hospital pharmacies• Dispense 5 million doses every day
through Alaris® and Pyxis® products• Manufacture or distribute products
used in 50% of all surgeries• Products used by 90% of hospitals• Manufacture or package 100 billion
doses of medicine every year
14
Becoming One Company…
eon
CPSI
From a distribution company… To a portfolio of market leaders…
Our MissionTo make Healthcare safer and more productive
To a global healthcare leader
Our RequirementModernize our systems and synchronize our data
15
Product Data Quality – an Industry-Wide Problem
• Average cost to research and correct a single reconciliation exception ranges from $15-$50 per error.
• 30% of item data in product catalogues is in error – Each of those errors cost $60 - $80 to reconcile
• 60% of ALL invoices generated have errors– Each invoice error costs $40 - $400 to reconcile– 43% of all invoice errors result in deductions
• Item master, charge master & case management systems are out of sync
• Any QRA violation can lead to fines - $50,000 per incident
• The cost of low data quality is significant!
1 Source: Rand Ballard, “Strategic Supply Cost Management, Physician Preference Without Deference,” Healthcare Financial Management, April 2005, pp 78-84
A study by the Healthcare Distribution Management Association (HDMA) estimates
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Cardinal Health – Business Situation & Opportunity
Business Opportunities– Customers experience improvement
with product search and cross reference when ordering online
– Increased sales opportunities
Proposal– Overhaul product data mastering
(cleansing, matching etc.) practices to reduce cost, improve scalability and quality etc.
– Improve enterprise-wide data leverage via MDM for Product Data
Product Data Problems– Expensive to create and maintain
• Largely manual processes• Unable to keep up with data volumes
– Poorly leveraged across the business• Lack of clear ownership and accountability• Disparate systems and processes
Business Benefit– Improved customer satisfaction
• Improve ability to find and purchase desired items
• Identifying alternatives reduces stock-outs– Increase revenues
• Better product lookup• Ability to recommend preferred substitute
items.– Reduced costs
• Reduced manual effort• Improved scalability • Improved quality
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Data Mastering for MDM
Business Unit View Enterprise Common View Enterprise Functional View
All Business Units
Match&
Cleanse
BU View
Enterprise Applications
Enterprise Finance
Business Unit 1
EnterpriseDataHub
Unique View
Business Unit 2
Business Unit 3
BU View
BU View
Sales & Customer Service
Enterprise Operations En
terp
rise
Dat
a W
areh
ouse
Common View
Business UnitLegacy Systems
(as needed)
Common View
Enterprise Data Managment
Dat
a E
ntry
Business Unit 4 BU View
• Analyze• Parse• Clean• Standardize
• Process metrics• Process improvement• Data review • Exception management
• Match• Merge• Enrich
Cleanse & Match
Data Stewardship
• Persistent View • Data Management• Data Distribution• Master Data Services
18
Legacy ‘Cleanse & Match’ Operations
• HSCS-Pharmaceutical Distribution– Product cleansing and management
operations + ongoing maintenance
• HSCS-Medical Distribution– Product cleansing and management
operations + ongoing maintenance – Partial automated processes and
inefficiency to handle volumes.
• SupplyLine– Internal service group– Strong category management
capabilities– Homegrown solution, largely manual
There have been numerous parallel and largely redundant efforts
Proposal– Eliminate redundancy – Build single,
enterprise-wide ‘service’ for product data clean-up
– Automate – use best available technology to reduce costs, increase scalability
– Enterprise leverage – use MDM to make reliable data available enterprise-wide
19
Automation of Data Quality - Evolution
• Manual– Only basic field-level validations– Expensive and hard to scale
• 1st Generation – Basic pattern recognition – Intensive IT coding & maintenance– Constant tuning through trial and error– High level of exceptions & false-positives– Questionable results
• 2nd Generation – Advanced pattern recognition– Potential for context-sensitive recognition, may require good
amount of IT effort to leverage it– Can take a long time to deploy– No provision of business users– Requires ongoing maintenance by IT
• 3rd Generation – Semantic recognition – Leverages context and business-generated rules– Designed for non-technical business user (avoids– Relatively rapid deployment – High quality results with few errors
Each evolution in data quality technology has reduced TCO by reducing labor costs – both IT and business operations
Manual 1stGeneration
2ndGeneration
3rdGeneration
Labor
Purchase Costs
TCO
Quality Levels
20
Selected Vendor – Silver Creek Systems
• Better use of resources – Business tool powered by IT, not
an IT tool used by the business
– Requires fewer advanced business resources in order to be effective
– Requires fewer IT resources to support
• Lowest total cost of ownership
• Quicker time to value
Silver Creek provides a solution that empowers the business to control the quality of the data with little dependency on the IT group
and does so at a more than competitive TCO
Why Silver Creek Systems?• Advanced semantic technology
• Auto Learning leverages existing corporate knowledge
• Built for business users
21
Cardinal Overall MDM Landscape
• Cardinal Health’s current MDM environment consists of multiple technologies working together
• Silver Creek provides a unique methodology to approach data quality for the product domain
• Silver Creek is used as the standard for product cleansing and matching and is incorporated into the remainder of the environment
Many aspects of MDM are similar between Customer to Product data. Data Quality has the least amount of
similarities!
22
Why is Product Data different? Variability & subjectivity
Graduations?
Immersable?
Type? Usage?Accuracy?
Autoclaveable?Manufacturer?
Auto-off?Control type?
Certifications?
Item Category = ?“Thermometer”
23
Product Data ‘Rules’ are Category-Specific
Graduations FarenheitImmersion Water resistantChemical type DigitalUsage OralAccuracy 0.2 FAutoclaveable Not autoclaveableManufacturer AllegianceAuto-off 5 minutesCertifications NoneControl_type None
ThermometerDisposableLengthSterilityStyleGuageStyletWingSubclass_nameHandleReprocessedODSizeNeedle_nameNeedle_attachmentDiameter
Needles_EpiduralDiameterEnd_typeMaterialMouth_typeUsageManufacturerClass_gradeVolumeDisposable_reusableAutoclavableHandlesBase_typeGraduationsGraduatedStackable
BeakersFirst_nameLast_nameStreet_addressCityZIP - 5ZIP - 9
Party Information
CDI/Party Data rules are mostly uniform
1. Product Data rules are mostly category-specific
• Required attributes• Allowable values• Vocabulary• Abbreviations
2. Cardinal Health has over 1,600 categories…
• Inferences• Validations• Exception handling…
24
Product Data Hierarchy & Volumes
Categories
Product Lines
Class
Clinical Product Descriptions
Manufacturer SKUs
Distributor SKUs
Unique items
Match to alternates
Cleanup, validate, enrich
Classification
Item definition level Specify standards
Existing hierarchy and cross-reference of internal and external products
25
Phased Silver Creek Rollout Delivers Immediate Value
Phase 1: Initial Deployment • 3 months • 25% of original (manual process)
team assigned to the DataLens System
• Select high priority product categories for greatest impact
• ‘Work out the kinks’ – incorporate learning
– New & improved processes– Ways to work faster and more
effectively• Refocus team from line-by-line effort
to building reusable rules & process
Phase 2: Full rollout• 9 months • Double team size to accelerate
roll-out• Rapidly broaden coverage • Tackle DQ item backlog• Further develop item cross-
referencing • Further integration into other
business processes• Deploy as enterprise-wide data
service
Enabling a fundamental change in process and effectiveness
26
Phase 1 – Initial Results
• Non-technical – Clinicians are able to perform work with minimal concerns about the technical environment
• Easy to integrate – Installation and configuration within the Cardinal environment has been good
• Short time to value – The team has been able to quickly provide value on high-priority products
• Identified substitutes per product have been increased by 5x
• Increased sales opportunities and customer satisfaction
• Foundation for MDM
The experience with the Silver Creek technology has been extremely positive
We are already seeing 1) good results and 2) more opportunities!
27
Bottom-Line…
The combination of Automated Data Mastering and MDM solves a critical need for the business
Automated Data Mastering will– Significantly reduce operating costs– Improve output quality and scalability– Identify more substitute items
(improved customer satisfaction and revenues)
MDM for Product Data will– Ensure all systems are using the
‘right’ trusted data– Leverage the value of the data being
created
– Increased revenues – through better customer service, choice and functionality
– Reduced cost – of running the business
– Business flexibility – the ability to respond quickly to new opportunities
– Increased trust – in the validity and reliability of the information supply chain
Sustainable competitive advantage!
www.silvercreeksystems.com
Product Data Mastering for Successful MDM
Silver Creek Systems & Cardinal Health
MDM Summit,August 26, 2009