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Copyright of Shell Shared Services (Asia) B.V.
Creating an Effective Data Governance Frameworka.k.a., just get on with it!
Tom Kunz, Data Manager, Downstream, Finance Operations Data, Shell Shared Services (Asia) B.V.
July 2016 All Rights Reserved Shell Shared Services (Asia) B.V.
Chief Data and Analytics Officer ForumSingaporeJuly 27-28, 2016
Copyright of Shell Shared Services (Asia) B.V.
DEFINITIONS AND CAUTIONARY NOTEThe companies in which Royal Dutch Shell plc directly and indirectly owns investments are separate entities. In this presentation “Shell”, “Shell group” and “Royal Dutch Shell” are sometimes used for convenience where references are made to Royal Dutch Shell plc and its subsidiaries in general. Likewise, the words “we”, “us” and “our” are also used to refer to subsidiaries in general or to those who work for them. These expressions are also used where no useful purpose is served by identifying the particular company or companies. ‘‘Subsidiaries’’, “Shell subsidiaries” and “Shell companies” as used in this presentation refer to companies over which Royal Dutch Shell plc either directly or indirectly has control. Companies over which Shell has joint control are generally referred to “joint ventures” and companies over which Shell has significant influence but neither control nor joint control are referred to as “associates”. In this presentation, joint ventures and associates may also be referred to as “equity-accounted investments”. The term “Shell interest” is used for convenience to indicate the direct and/or indirect ownership interest held by Shell in a venture, partnership or company, after exclusion of all third-party interest. This presentation contains forward-looking statements concerning the financial condition, results of operations and businesses of Royal Dutch Shell. All statements other than statements of historical fact are, or may be deemed to be, forward-looking statements. Forward-looking statements are statements of future expectations that are based on management’s current expectations and assumptions and involve known and unknown risks and uncertainties that could cause actual results, performance or events to differ materially from those expressed or implied in these statements. Forward-looking statements include, among other things, statements concerning the potential exposure of Royal Dutch Shell to market risks and statements expressing management’s expectations, beliefs, estimates, forecasts, projections and assumptions. These forward-looking statements are identified by their use of terms and phrases such as ‘‘anticipate’’, ‘‘believe’’, ‘‘could’’, ‘‘estimate’’, ‘‘expect’’, ‘‘goals’’, ‘‘intend’’, ‘‘may’’, ‘‘objectives’’, ‘‘outlook’’, ‘‘plan’’, ‘‘probably’’, ‘‘project’’, ‘‘risks’’, “schedule”, ‘‘seek’’, ‘‘should’’, ‘‘target’’, ‘‘will’’ and similar terms and phrases. There are a number of factors that could affect the future operations of Royal Dutch Shell and could cause those results to differ materially from those expressed in the forward-looking statements included in this
presentation, including (without limitation): (a) price fluctuations in crude oil and natural gas; (b) changes in demand for Shell’s products; (c) currency fluctuations; (d) drilling and production results; (e) reserves estimates; (f) loss of market share and industry competition; (g) environmental and physical risks; (h) risks associated with the identification of suitable potential acquisition properties and targets, and successful negotiation and completion of such transactions; (i) the risk of doing business in developing countries and countries subject to international sanctions; (j) legislative, fiscal and regulatory developments including regulatory measures addressing climate change; (k) economic and financial market conditions in various countries and regions; (l) political risks, including the risks of expropriation and renegotiation of the terms of contracts with governmental entities, delays or advancements in the approval of projects and delays in the reimbursement for shared costs; and (m) changes in trading conditions. All forward-looking statements contained in this presentation are expressly qualified in their entirety by the cautionary statements contained or referred to in this section. Readers should not place undue reliance on forward-looking statements. Additional risk factors that may affect future results are contained in Royal Dutch Shell’s 20-F for the year ended December 31, 2015 (available at www.shell.com/investor and www.sec.gov ). These risk factors also expressly qualify all forward looking statements contained in this presentation and should be considered by the reader. Each forward-looking statement speaks only as of the date of this presentation, [27-28, July 2016]. Neither Royal Dutch Shell plc nor any of its subsidiaries undertake any obligation to publicly update or revise any forward-looking statement as a result of new information, future events or other information. In light of these risks, results could differ materially from those stated, implied or inferred from the forward-looking statements contained in this presentation. We may have used certain terms, such as resources, in this presentation that United States Securities and Exchange Commission (SEC) strictly prohibits us from including in our filings with the SEC. U.S. Investors are urged to consider closely the disclosure in our Form 20-F, File No 1-32575, available on the SEC website www.sec.gov. You can also obtain these forms from the SEC by calling 1-800-SEC-0330.
Copyright of Shell Shared Services (Asia) B.V.
Agenda
1. Who is Shell?2. What’s a person to do?3. Making the business case 4. Finally, let’s talk Governance5. Summary
Copyright of Shell Shared Services (Asia) B.V. 4
Big company challenges and strengths
Who is Shell?
1
Copyright of Shell Shared Services (Asia) B.V. 5
Shell’s Businesses
Copyright of Shell Shared Services (Asia) B.V.
Who is Shell? + 70 countries with
operations
$28.9
billion of new capital investment
93,000 average number of people employed
22.6 million
tonnes of LNG sold
3.0 million barrels of gas and oil produced every
day
43,000 Shell service
stations worldwide
$265 billion
of revenue
12 secondsAverage time
between plane re-fuelings by Shell
1.1billionexpended on
R&D
2015 Peformance
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Consolidating Finance Processes: Service Centersincreasing efficiencies and optimizing benefits of global scale
Expenditure: manage finance processes relating to Accounts Payable and Payroll/Accounting including Travel and Entertainment expenseRecord to Report: manage processes for Financial Close, Local Statutory Reporting, Direct Tax, Indirect Tax, Cash Management, Liquidity and Foreign Exchange managementRevenue: responsible for operating finance processes for Intra-Group Billing and the Offer to Cash processManagement Information (MI): manage processes for Internal Performance Reporting and annual Budgeting Data: (or Master Reference Data): manage E2E Data processes, standards and quality assurance and drive development of company wide Data strategy
Hackett Benchmark end 2006Finance cost as a % of revenue
0,0%
0,5%
1,0%
Shell Median FirstQuartile
Other cost Technology cost Outsourcing Labor cost
Copyright of Shell Shared Services (Asia) B.V. 8
Finance OperationsOver half of Shell’s Finance staff are now in Finance Operations:
Revenue Expenditure
Hydrocarbon
Reporting & Analysis
Copyright of Shell Shared Services (Asia) B.V.
The Problem with Data
9
Fr a gm ent a tio nHere a touch…
There a touch…
Everywhere a touch, touch…
Copyright of Shell Shared Services (Asia) B.V.
Upstream & Technology
Downstream
Contracts & Procurement
Finance, HR
Businesses
Data Manager
Data Manager
Data Manager
Accounts
Data Process Owners
Assets & ProjectsOrganisation & PeopleReal Estate ContractsConvenience Retail ProductsB2B CustomersCard CustomersRetail Site CustomersFacilities and EquipmentMaterials and ServicesVendorsProcurement ContractsProductsEtc…
CompetenciesData Teams
Process Manager
s
“Certification”
Data Process Management
Business Knowledg
e
Personal Competen
cies
Data Manager
FunctionalExcellence
Assurance, Design, Improvement,
Program
Sr. Team LeaderTeam
Managers
Run & Maintain Analysts
Improvement & QA analysts
Sr. Process Improvement
Specialist
A Process-based Master Data Organisation
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Why does this have to be so hard?
What’s a Person to do?
2
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Data Domain Responsibility Summary Process 1 Process 2 Process 3 Process 4 Process 5 Process 6 Process 7 Process 8
Business GM The Owner
Business ProcessSets and Drives the Agenda
Global Data Value Owner
Drive Data Quality Improvement
Data Definition Owner
The Expert on data and its uses
Data Process Design Owner
Expert on how the the process fits together
Data Process Manager
Manages and improves the E2E process
Data ArchitectDefines IT Data Sources and Uses
Key: ChampionsPositiveNeutralCasual
Don't Know
Governance Challenges
GM Champion (Process Owner) + a Business Champion (Global Data Value Owner + PM Champion (Process Manager) = Great Progress
A Business Champion + PM Champion = Good Progress
Anything else = Half hearted (at best)
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Are you a believer or are you not?
Making the Business Case
3
Copyright of Shell Shared Services (Asia) B.V.
For Data, Only Two Moments Really Matter
The Moment of Use
The Moment of Creation
Path From Creator ToCustomer
DATA CREATOR DATA CUSTOMER
The whole point of data quality management is to connect the
two!
Note that they DO NOT occur in IT
Used with PermissionThomas C. Redman, Ph.D.© Data Quality Solutions
2000-2016
Copyright of Shell Shared Services (Asia) B.V.
Friday Afternoon Measurement Protocol
Assemble “last 100 records”
Assemble 2-3 experts
Mark “obviously-erred” data
in red
Summarizeand
interpret results
Used with PermissionThomas C. Redman, Ph.D.© Data Quality Solutions
2000-2016
Copyright of Shell Shared Services (Asia) B.V.
Count the “Perfects”
After Fig 18.2, Redman, Data Quality: The Field Guide
Attribute 1: Attribute 2: Size
Attribute 3: Amount etc
record perfect?
(y/n)Record A Jane Doe Null $472.13 nRecord B John Smith Medium $126.93 y
C Stuart Madnick XXXL Null n
D Thoams Jones n
Record 100 James Olsen
One Locked Place $76.24 n
Count perfect 67
Data Quality = 67%! The interpretation is a full third of recent customer orders had a
serious DQ issue. A worry indeed!
Used with PermissionThomas C. Redman, Ph.D.© Data Quality Solutions
2000-2016
Copyright of Shell Shared Services (Asia) B.V. 17
Business Scenario Review
Identify and review E2E processes, business scenarios, interfaces, and key data requirements
Confirm that critical data required to deliver the most important transactions for the business scenario are subject to quality assurance processes
Identify gaps that require corrective actions Gather specific examples of pain points and evidences of
critical to success process or data that has no data quality standards
Determine preventive or detective measures to address gaps
18Copyright of Shell Shared Services (Asia) B.V.
Workflow 1
System 1
System 2
System 3
System 4 Workflow
2System 5 System
6
Combo 2 Combo 3 Combo 4
Combo 5
Combo 6 Combo 7 Combo
8
Pain Points BUSINESS SCENARIO REVIEW – DATA MAP
Copyright of Shell Shared Services (Asia) B.V.
Copyright of Shell Shared Services (Asia) B.V.
Complementary Functional Plant Maintenance Processes
Potential Pain Points in Process Data Flows – Pump Example
Data Risk ExamplesMaster Data incomplete – awaiting as-built specs and drawingIncomplete / Inaccurate Purchase Specification data (Purchase Order issues)Incomplete Bill Of MaterialsIncomplete documents (Pump Spec sheets, Pump Curves, ...)Incomplete/ invalid maintenance plansWork Order left open
1
2
3
4
5
6
Procurement Buys the Equipment
Hardware Flow
Equipment delivered to
sitePSSR Equipment
StartUp
Technical Asset Plant Maintenance ProcessesEquipment Specificati
ons
Data Flow Master Data
entry into ERP
Master Data entry into Connected
Applications
Maintenance Plans
Material Master
tracking
Purchase Order creation
Work Order
1Data Flow
Equipment
Testing
AReliability Study
Bill of materials
B
C
Close Work Order
Procure Asset and Acquire Initial Asset Data & Information
2
3
4 5
6
MI Measurement Points
Copyright of Shell Shared Services (Asia) B.V.
Identifying Critical Fields: FMEA (Failure Modes and Effect Analysis)
20
Field Name
Describe the Failure
Mode
Potential Failure Effects
Sev.
Potential Causes
Occ.
Current Controls
Det. Risk Profile Number
Equipment
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 data
3 Human input error when filling out the request form
3 Approvers check requests for consistency. Data analyst valid request check
8 72
Human error by data analyst when inputting to system
3 Statistical Process Control checks
5 45
• A step-by-step approach for identifying all possible failures in a design, a manufacturing or assembly process, or a product or service.
• “Failure modes” means the ways, or modes, in which something might fail. Failures are any errors or defects, especially ones that affect the customer, and can be potential or actual.
• “Effects analysis” refers to studying the consequences of those failures.• Failures are prioritized according to how serious their consequences are, how frequently they occur
and how easily they can be detected. • Failure modes and effects analysis documents current knowledge and actions about the risks of
failures, for use in continuous improvement.
Copyright of Shell Shared Services (Asia) B.V.
Calculating the Cost of Poor Data Quality
C• Critical objects/fields/records selection : Determine scope of work and identify critical fields.
E• Engage with the Business to determine End to End usage: Determine objects/fields/records complexity, how the data is used and an E2E perspective.
C• Characteristic of the object/field/record: Determine failure modes, effects of poor quality, pain points and their costs.
O• Occurrence of failure modes: Determine failure mode occurrence likelihood from control incidents, data quality standards or using data profiling
V• Validate with Business partners: Get agreement with business partners that they support the calculation and the outcome of the exercise
Copyright of Shell Shared Services (Asia) B.V.
The Cost of Poor Data Quality in Offer to Cash
2012
BUSINESS SCENARIO
2013
CRITICAL FIELDS
REVIEW
Understanding of
OTC business models
and Pain Points
Definition of fields,
purpose, usage,
users of data,
failure modes and
criticality
CONSISTENT LANGUAGE WITH THE BUSINESS …. FOCUSED ON $$
STRONGER LINK BETWEEN DEFECTS AND BUSINESS PROCESS FAILURES. COPDQ for >
150 CMD FIELDS
MORE POWERFUL DATA QUALITY REPORTS… VISIBILITY ON COST OF GETTING DATA
WRONG
CRITICALITYCMD FIELDS Co
ntra
ct Se
t-Up
Proc
ess 2
Proc
ess 3
Proc
ess 4
Load
ing &
Deli
very
Proc
ess 6
Proc
ess 7
Proc
ess 8
Proc
ess 9
Proc
ess 1
0
Proc
ess 1
1
Team
1
Sales
Sup
port
Team
3
Team
4
Team
5
Team
6
Cred
it
Team
7
Team
8
Team
9
Team
10
TOTA
L RE
WO
RK C
OST
PE
R DE
FECT
(USD
)
TOTA
L W
ORK
ING
CAPI
TAL
DELA
Y (U
SD)
OTH
ER P
ROCE
SS
FAIL
URE
CO
ST (U
SD)
TOTA
L DE
TECT
IVE
COST
(USD
)
TOTA
L U
NIT
CO
ST
(USD
)
SEVERE Field 1 x x x x $X $X $X $X $X $X $X $X $X $XXSEVERE Field 2 x x x x x x x $X $X $X $X $X $X $X $X $X $X $X $XXHIGH Field 3 x x x $X $X $X $X $X $X $X $XXHIGH Field 4 x x x x $X $X $X $X $X $X $X $X $X $XX
MEDIUM Field 5 x x $X $X $X $X $X $X $X $XXMEDIUM Field 6 x $X $X $X $X $X $X $X $XX
LOW Field 7 x x $X $X $X $X $X $X $X $XXLOW Field 8 x $X $X $X $X $X $X $XX
$XX$XNON-CRITICAL FIELDS (AVE)
CRITICAL FIELDS (MEDIAN)
COPDQ APPROACH - ABCIMPACTED PROCESS IMPACTED TEAMS (REWORK COST)
A. Tax Reporting
B. Loading & Delivery C. Billing D. Pricing
E. Debt Collection & Cash Alloc
F. MI Reporting G. Manage Order
H. Financial Reporting
I. Credit Assessment
Country 1 $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XXX - $XXX $XX $XXXCountry 2 $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XX $XXXCountry 3 $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XX $XXXCountry 4 $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XX $XXXCountry 5 $XXX $XXX $XXX $XXX $XXX $XXX $XXX - $XXX $XXX $XX $XXXCountry 31 $XXX $XXX $XXX - $XXX $XXX - - $XXX $XXX $XX $XXXCountry 32 $XXX - $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XX $XXXCountry 33 $XXX - $XXX $XXX - $XXX $XXX $XXX $XXX $XXX $XX $XXXCountry 34 $XXX - - $XXX $XXX - $XXX $XXX $XXX $XXX $XX $XXXCountry 35 - $XXX $XXX $XXX $XXX $XXX $XXX - - $XXX $XX $XXXCountry 36 $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XX $XXXCountry 37 $XXX - - - - - - - - $XXX $XX $XXXGrand Total $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XXX $XX $XXX
COST OF ERRORS ON CRITICAL FIELDS (US$) SUBTOTAL CRITICAL ERRORS
(US$)
NON-CRITICAL ERRORS
(US$)
GLOBAL COMMERCIAL
Grand Total (US$)
Copyright of Shell Shared Services (Asia) B.V.
Metadata
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Metadata: What we have learned….
24
Frequency and
number of updates to each field
in the customer master
Fields with data in
them, but not used in the design
Critical Fields not
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
Copyright of Shell Shared Services (Asia) B.V. 25
Maybe there really is something we can do
Finally, let’s talk Governance
4
Copyright of Shell Shared Services (Asia) B.V. 26
Words of the Day!
Footer Date Month 2016
Word cloud from the Data Governance Financial Services Conference – Sep 8-9 2016
Copyright of Shell Shared Services (Asia) B.V. 27
Shell’s Data Journey
Migrate
Data Quality Standa
rds
Reporting
Governance
Workflows
Targets &
Cleansing
COPDQ
Visualization
Analytics
• Roles• Process Owners• Process Design• Data Definitions
• Business Rules• Critical Fields• Metadata
• Segmented• Drill down• Automated
CreatorsGlobal Data Value OwnersLocal Data Value OwnersData Councils
• Automated• Preventative Checks• Tracking
• Agreed, auditable costs• Integrated reporting• Communication
• Record level• Agreed goals• River vs Lake
• Data accuracy• Aging of defects• Profiling for value
• Predictive• Prescriptive• Profiling
Training & EducationData Awareness**Data Knowledge**Data Skill**Data Mastery**Project Management**Supervisory**Technical**Resiliency**Critical Thinking**Lean Sigma
Lean Sigma Continuous ImprovementE2E Efficiency**Tracking**Automation**Value Stream Mapping**Statistical Process Control**Time Studies**Pain Point
Mapping**Benchmarking
Copyright of Shell Shared Services (Asia) B.V. 28
Governance ChallengesData Domain Responsibility
Summary Process 1 Process 2 Process 3 Process 4 Process 5 Process 6 Process 7 Process 8
Business GM The Owner
Business ProcessSets and Drives the Agenda
Global Data Value Owner
Drive Data Quality Improvement
Data Definition Owner
The Expert on data and its uses
Data Process Design Owner
Expert on how the the process fits together
Data Process Manager
Manages and improves the E2E process
Data ArchitectDefines IT Data Sources and Uses
Key: ChampionsPositiveNeutralCasual
Don't Know
GM Champion (Process Owner) + a Business Champion (Global Data Value Owner + PM Champion (Process Manager) = Great Progress
A Business Champion + PM Champion = Good Progress
Anything else = Half hearted (at best)
Copyright of Shell Shared Services (Asia) B.V.
Core Data Governance Roles1. Data Executive (DE)/Process
Owner: accountable to ensure data is managed through its life-cycle, accountabilities are assigned, and quality assurance is executed
2. Data Definition Owner (DDO): accountable to ensure data definitions and data business rules are defined. Less critical as process matures.
3. Data Process Design Owner (DPDO) accountable to ensure data processes fit for purpose and current with business needs. Less critical as process matures.
4. Data Value Owners (DVOs): accountable to ensure data is of high quality, enabling the execution of business processes without failures. Mandatory.
5. Data Requestors (DRs): accountable for identifying data values on behalf of DVO
6. Data Process Managers (PMs): accountable for maintaining the data and managing optimizing and improving data processes
DE/PO
DDO
DPDO
DVO
PM
Global
Local
1
2 4
3
6
DR5
Copyright of Shell Shared Services (Asia) B.V.
“Provocateurs” disrupt the dynamic that leads to hidden data factories
• Dissatisfied with the status quo.• Courage to try something new.• Great corporate citizens.• Achieve “real results” within their spans of control.• At all levels!
There is a little provocateur in all of us
Used with PermissionThomas C. Redman, Ph.D.© Data Quality Solutions
2000-2016
Copyright of Shell Shared Services (Asia) B.V.
Provocateurs Can Go Only So Far
Progress of a Typical Data Quality Program
time
pene
trat
ion
of D
Q a
cros
s or
gani
zati
on
traction
real results
plateau
next level= more data
Order-of-magnitude improvement on some data
Used with PermissionThomas C. Redman, Ph.D.© Data Quality Solutions
2000-2016
Copyright of Shell Shared Services (Asia) B.V. 32
Data Governance Assessment• What is it that is working well in regards to leaders taking
accountability for their data journey?• What is missing or not working so well?
Do you have someone who is taking responsibility for the data journey in each of the businesses?
Do you have someone in the business that can make the following things happen? Is IT seen as an enabler rather than an owner of data? Are there subject matter experts in the business that willingly assist you to identify, value
and fix pain points? Are critical data fields understood? Do you have data quality standards that are owned by the businesses they support? Do
you have a business focal point for working on them? Do you have business partners who are committed to first time right requests/data entry.
Are they held accountable? Do you know to whom to send data quality reports? Are the businesses committed to cleansing critical defects? Do you have someone with the authority to change/fix a process (or allow it to be
changed/fixed by your team) in the event the Business/IT/Data Team requirements change? When new opportunities for work to be migrated to the Data Team are identified, can you
make it happen? Do you measure the River errors and COPDQ on a high level dashboard visible to business
VPs? Do you have support for building and using an easy to access/use metadata repository? Are the businesses interested in and do they understand the importance of data accuracy? Do you have go to persons who can commit resources to CI projects to solve data
problems?
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Governance when it is working
33
• Data processes are managed• Collective understanding of the E2E data processes with supporting metrics• Master data and its importance understood – critical fields are known• Business takes ownership for data quality• Data Requestors get it right the first time• Feedback loops are working• Process designers are valued• Metadata is managed• Continuous Improvement is a mindset• Results are more important than politics
Copyright of Shell Shared Services (Asia) B.V. 34
Summary
Data Governance is a consultants dream Stop waiting Find the coalition of the willing and go to work You do not need sophisticated IT tools to start on the journey Timing is everything There are prizes to be won Welcome to the world of change management
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Questions and Answers