tom kunz
TRANSCRIPT
Copyright of Shell Oil Company 1March 2011
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Data Quality APAC Congress 2011
In Pursuit of Data Quality:
When the Business Demands
Results
Tom Kunz
Data Manager, Downstream, Shell
Finance Operations - Data
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Today’s Agenda
1. Who is Shell?
2. Can a professional data organization exist in a big company?
3. Can a practical data governance structure really be created?
4. How can metadata accelerate data quality improvement?
5. Why would I want to use six sigma and lean techniques to solve
data quality issues?
6. Does knowing the cost of poor data quality really help?
7. What are the key takeaways?
In pursuit of Data Quality: When the Business demands results
Business OverviewSome Data About Shell
Who is Shell?
1.0
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Business Overview
UPGRADERPLANT
ON ANDOFFSHOREOIL AND GAS
REFINERY
GAS TOLIQUIDSPLANT
BIOFUELSPLANT
CHEMICALPLANT
LNGLIQUEFACTIONPLANT
LNGREGASIFICATIONTERMINAL
WINDTURBINES
POWERSTATION
CHEMICAL PRODUCTSUSED FOR:
• Plastics• Coatings• Detergents
REFINED OIL PRODUCTS
• (Bio) Fuels• Lubricants• Bitumen• Liquefied
petroleum gas
GAS AND ELECTRICITY• Industrial use• Domestic use
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FACTS AND FIGURES – SHELL PERFORMANCE IN 2009
Source: 2009 Annual Report
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2010 data available March 15th
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The problemThe solutionThe new opportunities
Can a professional data organization exist in a big company?
2.0
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The Problem: Does Data Quality Matter?
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Missing aircraft info could pose security threatNEW YORK (AP) — The Federal Aviation Administration's aircraft registry is missing key information on who owns one-third of the 357,000 private and commercial planes in the U.S. — a gap the agency fears could be exploited by terrorists and drug traffickers.
While he served abroad, his credit was under siege
Federal Reserve plays major role in fate of 2006 market
Homeland Security contributed bad data to military intelligence database
Greenspan is probably one of the most-intuitive economists because he concluded the Fed had bad data.
Mr. Baur said that those operating the database had misinterpreted their mandate and that what was intended as an antiterrorist database became, in some respects, a catch-all for leads on possible disruptions and threats against military installations in the United States, including protests against the military presence in Iraq.
Report: Low oil spill estimates rested on "unexplained assumptions"
Bad data? Infection Prevention groups reject federal Healthcare Associated Infections report. 'An outdated and incomplete picture of HAIs' Faced with a critical federal report on the lack of progress against healthcare associated infections, the nation's leading infectionprevention groups find themselves in the thankless position of having to challenge the methodology of the report without appearing to be in denial about HAIs.
A 2005 survey by the U.S. Public Interest Research Group found 79% of credit reports contained errors, and 25% contained enough mistakes to prevent the individual from obtaining credit. Once the credit system accepts bad data, it can be next to impossible to clear.
The reports authors say they cannot tell if the low estimates actually slowed the response to the oil spill, but say they likely undermined public confidence in BP and the federal response team, regardless.
The Problem
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Fr a gm ent a tio n
Here a touch…
There a touch…
Everywhere a touch, touch…
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9P0
The Solution: Manage Data as a Process in Finance
Data risk management
Data quality assurance
Meta-data management
Data lifecycle management
Audit and reporting
Controls & compliance
Assess, quantify and maximize the business value of enterprise data assets across the value chain (including suppliers, partners, customers)
Capture, use, maintain, archive and delete data
Define, measure, improve, and certify the quality (accuracy, validity, completeness, timeliness) of data
Identify, assess, avoid, accept, mitigate, or transfer out risks
Identify and establish control requirements for data and ensure compliance (including privacy, security, regulatory aspects)
Measure and monitor data quality, risks, and efficacy of governanceCapture, use, maintain semantic definitions for business terms and data models
Create Value
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The Solution: A process-based data management organization
Upstream
Downstream
Projects & Technology
Finance, HR, Corporate, legal
Businesses
Data Manager
Data Manager
Data Manager
Business Facing
Process ownersAccounts
Aligned by Data Process
Assets & Projects
Organisation & People
Real Estate Contracts
Convenience Retail Products
B2B Customers
Card Customers
Retail Site Customers
Facilities and Equipment
Materials and Services
Vendors
Procurement Contracts
Lubes Products
Etc…
Data Competency Framework
Data Teams
Data ManagerProcess Manager
Process Manager
Process Manager
Process Manager
“Certification”
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New Opportunities
A
Third-quartile data
Top-quartile data
Migrate: De-fragment and migrate data activities into a single team of dedicated data professionals
Operate & measure:Operate and measure end-to-end data process performance: KPIs, controls, quality standards.
Improve:Continuously improve data quality by addressing processes, tools, capabilities, quality standards
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3
B
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The Impossible DreamThe Long and Winding RoadI’m A Believer
Can a practical data governance structure really be created?
3.0
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The Impossible Dream
Where everything just works…..
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• Business understands master data
• Business takes ownership for data quality
• Process designers are valued
• Continuous improvement is a mindset
• Results are more important than politics
• E2E process is understood
• Data gatherers know what to do
• Data processes are managed
• Feedback is welcomed
The Long and Winding Road
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Business Sponsored
Go where the need is
Keep the scope narrow Slippery SlopesCompromise
Go slow at times
…and then start again
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I’m a Believer
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Data Value Owner
Data Gatherer
Process Manager
Data Operation
s
• Business understands master data and its processes
• Business takes ownership for data quality
• Process designers are valued
• Continuous improvement is a mindset
• Results are more important than politics
• E2E process is understood
• Data gatherers know what to do
• Data processes are managed
• Feedback is welcomed
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What it isHow we used itWhat we learned
How can metadata accelerate data quality improvement?
4.0
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Metadata: What it is
Data about Data
• Describes the contents of the information
• Provides documentation or information about a specific piece of information
• Include elements and attributes such as a name, size or type
• Can represent the location or ownership of the file
• Any other information that needs to be noted about the data
• Can be information about frequency or volume of updates
Metadata: How we use it
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Fields with a significant number of updates in a given period
Identification of fields not used in the design, but actually have data in them
Fields critical to the success of a particular process but not covered by a current data quality standard
Metadata: What we are learning…
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Frequency and number of updates to each field in the customer
master
Fields with data in them, but not used in the design
Fields included in
the data quality
compliance standards
Discover fields that
are candidates for mass upload tools
Reduce effort by no
longer populating unused fields
Identify which fields are not in data quality
standards that should be
Data about Data:
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Danger: Low Hanging Fruit!Structuring for successDelivering the goods
Why would I want to use six sigma and lean techniques to solve data quality issues?
5.0
Danger: Low Hanging Fruit!
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What happens when you
pick it and it just grows
back?
Structuring for Success
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Operations Improvemen
t Logs
Business Pain Points
Prioritization of Improvement Projects
Project Charter
Project Charter
Project CharterOperations Business
Black Belt CoachingDevelop Greenbelts Develop Greenbelts
Delivering the Goods
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Reducing Costs
Increasing speed
Improving quality
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Everybody has a modelWhat works for usWhen it just doesn’t matter…much
Does knowing the cost of poor data quality really help?
6.0
Everybody has a model
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What works for us – FMEA (Failure Mode Effect Analysis)
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SAP Field Name & Description
Potential Failure EffectsSEV
Potential CausesOcc
Current ControlsDET
RPN
Human input error when filling out form
3 Outside MRD; approvers check requests for consistencyMRD Analyst Valid request check
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Human input error by MRD analyst 3 100% check for manual input, 30% for Mass upload
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Describe the failure mode in this column: what if the value of this property is missing, incomplete, accurate, duplicate, material, consistent
Refer to comments in headings for further guidance
Data input inaccurate and wrong category applied resulting in wrong ownership of data
Business not able to find records: additional time required to search, additional time to correct MRD records
3Equipment category
Effect SEVERITY of Effect Ranking PROBABILITY of Failure
Failure Prob Ranking Detection Likelihood of DETECTION by Design Control
Ranking
Hazardous without warning
Very high severity ranking when a potential failure mode affects safe system operation without warning
10 Very High: Failure is almost inevitable
>1 in 2 10 Absolute Uncertainty Design control cannot detect potential cause/mechanism and subsequent failure mode
10
Hazardous with warning
Very high severity ranking when a potential failure mode affects safe system operation with warning
9 1 in 3 9 Very Remote Very remote chance the design control will detect potential cause/mechanism and subsequent failure mode
9
Very High System inoperable with destructive failure without compromising safety
8 High: Repeated failures 1 in 8 8 Remote Remote chance the design control will detect potential cause/mechanism and subsequent failure mode
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High System inoperable with equipment damage
7 1 in 20 7 Very Low Very low chance the design control will detect potential cause/mechanism and subsequent failure mode
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Moderate System inoperable with minor damage 6 Moderate: Occasional failures
1 in 80 6 Low Low chance the design control will detect potential cause/mechanism and subsequent failure mode
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Low System inoperable without damage 5 1 in 400 5 Moderate Moderate chance the design control will detect potential cause/mechanism and subsequent failure mode
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Very Low System operable with significant degradation of performance
4 1 in 2,000 4 Moderately High Moderately High chance the design control will detect potential cause/mechanism and subsequent failure mode
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Minor System operable with some degradation of performance
3 Low: Relatively few failures
1 in 15,000 3 High High chance the design control will detect potential cause/mechanism and subsequent failure mode
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Very Minor System operable with minimal interference
2 1 in 150,000 2 Very High Very high chance the design control will detect potential cause/mechanism and subsequent failure mode
2
None No effect 1 Remote: Failure is unlikely
<1 in 1,500,000 1 Almost Certain Design control will detect potential cause/mechanism and subsequent failure mode
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Cost of providing the data
PLUS
Cost of compliance to the standard
VS.
Cost of non-compliance to the standard(Requires RISK BASED analysis
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When COPDQ just doesn’t matter…. much
Business is energized
Resources are available
Hot spots are known
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What are the key takeaways?7.0In Pursuit of Data Quality: When the business demands results
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Takeaways
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In pursuit of Data Quality: When the Business demands results
Q & A
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