1. Data Quality APAC Congress 2011
In Pursuit of Data Quality:
When the Business Demands
Results
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Tom Kunz
Data Manager, Downstream, Shell
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2. Today’s Agenda
In pursuit of Data Quality: When the Business demands results
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?
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4. Business Overview
CHEMICAL
UPGRADER PRODUCTS
PLANT USED FOR:
• Plastics
• Coatings
CHEMICAL • Detergents
PLANT
REFINERY
GAS TO BIOFUELS
LIQUIDS PLANT
PLANT REFINED OIL
PRODUCTS
• (Bio) Fuels
• Lubricants
• Bitumen
• Liquefied
ON AND petroleum gas
OFFSHORE
OIL AND GAS
GAS AND ELECTRICITY
• Industrial use
• Domestic use
POWER
STATION
LNG LNG
LIQUEFACTION REGASIFICATION WIND
PLANT TERMINAL TURBINES
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5. FACTS AND FIGURES – SHELL PERFORMANCE IN
2009
Source: 2009 Annual Report 2010 data available March 15th
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6. 2.0
Can a professional data organization
exist in a big company?
The problem
The solution
The new opportunities
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7. The Problem: Does Data Quality Matter?
Federal Reserve plays major
Missing aircraft info could
role in fate of 2006 market
pose security threat Greenspan is probably one of the most-intuitive
NEW YORK (AP) — The Federal Aviation economists because he concluded the Fed had bad
Administration's aircraft registry is missing key data.
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 Homeland Security contributed
exploited by terrorists and drug traffickers.
bad data to military intelligence
While he served abroad, his database
Mr. Baur said that those operating the database had
misinterpreted their mandate and that what was
credit was under siege intended as an antiterrorist database became, in some
respects, a catch-all for leads on possible disruptions and
A 2005 survey by the U.S. Public Interest Research threats against military installations in the United States,
Group found 79% of credit reports contained including protests against the military presence in Iraq.
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. Bad data? Infection Prevention groups
reject federal Healthcare Associated
Report: Low oil spill estimates Infections report.
'An outdated and incomplete picture of HAIs'
Faced with a critical federal report on the lack of progress against
rested on "unexplained healthcare associated infections, the nation's leading infection
assumptions"
The reports authors say they cannot tell if the low prevention groups find themselves in the thankless position of having
estimates actually slowed the response to the oil to challenge the methodology of the report without appearing to be in
spill, but say they likely undermined public denial about HAIs.
confidence in BPShell Oil Company
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and the federal response team, March 2011 7
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regardless.
8. The Problem
Here a touch…
There a touch…
Fr a gm ent a tio n
Everywhere a touch, touch…
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9. The Solution: Manage Data as a Process in Finance
Create Value Assess, quantify and maximize the business value of enterprise d ata
assets across the value chain (including suppliers, partners, customers)
Data lifecycle management Capture, use, maintain, archive and delete data
Define, measure, improve, and certify the quality (accuracy,
Data quality assurance
validity, completeness, timeliness) of data
Data risk management Identify, assess, avoid, accept, mitigate, or transfer out risks
Controls & compliance
Identify and establish control requirements for data and ensure
compliance (including privacy, security, regulatory aspects)
Audit and reporting Measure and monitor data quality, risks, and efficacy of
governance
Capture, use, maintain semantic definitions for business terms
Meta-data management
and data models
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10. The Solution: A process-based data management organization
Businesses Data Aligned by Data
Business Data Teams
Competency Process
Facing
Framework
Process owners
Data Manager Accounts
Upstream Process
Manager Assets & Projects
Organisation & People
Data Manager Real Estate Contracts
Process
Downstream Manager
Convenience Retail Products
B2B Customers
Data Manager Card Customers
Projects & Process
Technology
Manager
“Certification” Retail Site Customers
Facilities and Equipment
Data Manager Materials and Services
Process
Finance, HR, Manager
Vendors
Corporate, legal
Procurement Contracts
Lubes Products
Etc…
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11. New Opportunities
B
Top-quartile data
3 Improve:
Continuously improve data quality by
addressing processes, tools, capabilities,
quality standards
2 Operate & measure:
Operate and measure end-to-end data process
performance: KPIs, controls, quality standards.
1 Migrate:
A De-fragment and migrate data activities into a
single team of dedicated data professionals
Third-quartile data
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12. 3.0
Can a practical data governance
structure really be created?
The Impossible Dream
The Long and Winding Road
I’m A Believer
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13. The Impossible Dream
Where everything just works…..
• 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
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required. May sit up to two lines in depth.
14. The Long and Winding Road
Business Sponsored …and then
start again
Go where the need is Go slow at times
Keep the scope narrow
Compromise 14
Slippery Slopes 14
15. I’m a Believer
• Business understands master
Data
data and its processes
Value
Owner • Business takes ownership for
data quality
• Process designers are valued
• Continuous improvement is a
mindset
Data Process
Manager • Results are more important
Gatherer
than politics
• E2E process is understood
• Data gatherers know what to
do
Data • Data processes are managed
Operation
• Feedback is welcomed
s
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16. 4.0
How can metadata accelerate data
quality improvement?
What it is
How we used it
What we learned
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17. 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
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18. Metadata: How we use it
Identification of fields
not used in the design,
but actually have data
in them
Fields with a significant
number of updates in a
given period
Fields critical to the
success of a
particular process but
not covered by a
current data quality
standard
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19. Metadata: What we are learning…
Data about Data:
Frequency Fields
Fields with and number included in
data in them, of updates to the data
but not used each field in quality
in the design the customer compliance
master standards
Reduce effort by no Discover fields that Identify which fields are
longer populating are candidates for not in data quality
unused fields mass upload tools standards that should
be
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20. 5.0
Why would I want to use six sigma and
lean techniques to solve data quality
issues?
Danger: Low Hanging Fruit!
Structuring for success
Delivering the goods
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21. Danger: Low Hanging Fruit!
What
happens
when you
pick it and it
just grows
back?
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22. Structuring for Success
Operations
Business
Improvemen
Pain Points
t Logs
Prioritization of
Improvement Projects
Operations
Project Project Project Business
Charter Charter Charter
Develop Greenbelts Black Belt Coaching Develop Greenbelts
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24. 6.0
Does knowing the cost of poor data
quality really help?
Everybody has a model
What works for us
When it just doesn’t matter…much
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26. What works for us – FMEA (Failure Mode Effect Analysis)
Describe the failure mode in this Refer to comments in headings for further guidance
column: what if the value of this
property is missing, incomplete,
SAP Field accurate, duplicate, material, S O D R
Name & consistent Potential Failure Effects E Potential Causes c Current Controls E P
Description V c T N
Equipment Data input inaccurate and wrong Business not able to find records: 3 Human input error when filling out 3 Outside MRD; approvers check 8 72
category category applied resulting in wrong additional time required to search, form requests for consistency
ownership of data additional time to correct MRD MRD Analyst Valid request
records check
Human input error by MRD analyst 3 100% check for manual input, 3 27
30% for Mass upload
Effect SEVERITY of Effect Ranking PROBABILITY of Failure Prob Ranking Detection Likelihood of DETECTION by Ranking
Failure Design Control
Hazardous without Very high severity ranking when a Very High: Failure is >1 in 2 Absolute Uncertainty Design control cannot detect potential
Cost of
10 10 10
warning potential failure mode affects safe almost inevitable cause/mechanism and subsequent providing the
system operation without warning failure mode
data
Hazardous with Very high severity ranking when a 9 1 in 3 9 Very Remote Very remote chance the design control 9
warning potential failure mode affects safe will detect potential cause/mechanism
system operation with warning and subsequent failure mode PLUS
Very High System inoperable with destructive 8 High: Repeated failures 1 in 8 8 Remote Remote chance the design control will 8
failure without compromising safety detect potential cause/mechanism and
subsequent failure mode Cost of
High System inoperable with equipment 7 1 in 20 7 Very Low Very low chance the design control will 7 compliance to
damage detect potential cause/mechanism and
subsequent failure mode
the standard
Moderate System inoperable with minor damage 6 Moderate: Occasional 1 in 80 6 Low Low chance the design control will 6
failures detect potential cause/mechanism and VS.
subsequent failure mode
Low System inoperable without damage 5 1 in 400 5 Moderate Moderate chance the design control will 5
detect potential cause/mechanism and Cost of non-
subsequent failure mode
compliance to
Very Low System operable with significant 4 1 in 2,000 4 Moderately High Moderately High chance the design 4
degradation of performance control will detect potential the standard
cause/mechanism and subsequent (Requires
failurechance the design control will
High mode
Minor System operable with some degradation
of performance
3 Low: Relatively few
failures
1 in 15,000 3 High
detect potential cause/mechanism and
3 RISK BASED
subsequent failure mode analysis
Very Minor System operable with minimal 2 1 in 150,000 2 Very High Very high chance the design control will 2
interference detect potential cause/mechanism and
subsequent failure mode
None No effect 1 Remote: Failure is <1 in 1,500,000 1 Almost Certain Design control will detect potential 1
unlikely cause/mechanism and subsequent
failure mode
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27. When COPDQ just doesn’t matter…. much
Business is energized
Hot spots are known
Resources are available
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28. 7.0
What are the key takeaways?
In Pursuit of Data Quality: When the business demands
results
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29. Takeaways
In pursuit of Data Quality: When the Business demands results
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on Title pg.
