Masterclass A Tom Kunz , Shell, Presentation at The Chief Data & Analytics Officer Forum, Singapore

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


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.

http://www.shell.com/investor

http://www.sec.gov/

http://www.sec.gov/


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


Shell’s Businesses


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


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

http://sww.shell.com/finance/financeoperations/RTR/home.html

http://sww.shell.com/finance/financeoperations/Revenue/home.html

http://sww.shell.com/finance/financeoperations/MI/home.html

http://sww.shell.com/finance/financeoperations/data/home.html


Finance OperationsOver half of Shell’s Finance staff are now in Finance Operations:

Revenue Expenditure

Hydrocarbon

Reporting & Analysis


The Problem with Data

9

Fr a gm ent a tio nHere a touch…

There a touch…

Everywhere a touch, touch…


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


Why does this have to be so hard?

What’s a Person to do?

2


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)


Are you a believer or are you not?

Making the Business Case

3


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


Friday Afternoon Measurement Protocol

Assemble “last 100 records”

Assemble 2-3 experts

Mark “obviously-erred” data

in red

Summarizeand

interpret results


2000-2016


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!


2000-2016


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



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


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.


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


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$)


Metadata


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


Maybe there really is something we can do

Finally, let’s talk Governance

4


Words of the Day!

Footer Date Month 2016

Word cloud from the Data Governance Financial Services Conference – Sep 8-9 2016


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


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)


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


“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


2000-2016


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


2000-2016


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?


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


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


Questions and Answers

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