Securing executive support for data governance - John Morton

John Morton
MDM DG
April 2017
18:30 – 20:00
1
http://www.irmuk.co.uk/dg2017/
SECURING EXECUTIVE SUPPORT FOR
DATA GOVERNANCE

Copyright © 10/04/2017, Computers, Processes and Management Limited.. All rights reserved.
“The organizing framework
for establishing strategy,
objectives, and policies for
corporate data.”
-Dyché & Levy
INDUSTRY DEFINITIONS OF DATA GOVERNANCE
“The process by which an
organization formalizes the
‘fiduciary duty’ for the
management of data assets.”
“The overall management of the
availability, integrity, and security of
the data employed in an enterprise. A
sound data governance program
includes a governing body or council, a
defined set of procedures, and a plan
to execute those procedures.”

Corporate
Drivers
Business
Framework
Process
& Policy
Data
Management
Data
Governance
Execution
Process
Data
Governance
Charter
Guiding
Principles
Decision-
making
Bodies
Decision
Rights
Strategic Priorities: Voice of the
Customer; Compliance Mandates,
Mergers & Acquisitions
Business Drivers: At-Risk Projects: Data
Quality Improvement; Operational
Efficiencies
Data Stewardship Roles & Tasks
Mechanisms: Stewardship Dashboards,
Workflow Automation, Data Profiling Tools
People: Council, Stakeholders, Meeting Agendas
Process: Metrics Definition, Workflow, Council By-Laws
Data
Requirement
Data
Architecture
Data
Administration
Metadata
Management
Data
Quality
Security &
Access
Rights
Data Governance Framework

What else can we do?
How can we increase
productivity?
What more can
we do to
compete?
Staying in business
07/04/2017 4
BOARD ROOM DILEMMA

BOARD DYNAMICS
09/04/2017 5
Apathy
High
Challenge
GroupThing
High
Performance
Cohesion
High
Low
Fracture

SO WHAT ABOUT CUSTOMER VALUE ?
09/04/2017 6
Activity costing
Product(s)/
Service(s) cost
Implementation
costing
Local, location
or transfer costs
Cost of Customer
(Acq. /Dev. /Ret.)
Cost of
infrastructure
$
¥
£
€
} = ?

CHALLENGERS
09/04/2017 7
P
ChallengerSegment
Profit
Challenger Segment
- Pricing
- Customers
- Locations
- Methods
- Targets
- Revenue
- Profit
- Challenger Value
- Flexible
- Focussed
- Better relevant data
- Responsive
ChallengerSegment
ChallengerSegment
ChallengerSegment

DATA VS. INFORMATION
Data
raw facts
no context
just numbers and text
Information
data with context
processed data
value-added to data
 summarized
 organized
 analyzed

Data: 51007
Information:
 5/10/07 The date of your final exam.
 $51,007 The average starting salary of an accounting major.
 51007 Zip code of Bronson Iowa.

Data
6.34
6.45
6.39
6.62
6.57
6.64
6.71
6.82
7.12
7.06
SIRIUS SATELLITE RADIO INC.
$5.80
$6.00
$6.20
$6.40
$6.60
$6.80
$7.00
$7.20
1 2 3 4 5 6 7 8 9 10
Last 10 Days
StockPrice
Information

Data
Summarizing the data
Averaging the data
Segmenting data
Graphing the data
Understanding variations
Adding context
Adding value
How does info affect outcomes?
Are there any patterns ?
What decisions rely on this Info?
What info is relevant, and why?
How does this info effect the system?
What is the best way to use the info?
How can we add more value to the info?
Data Information Knowledge

DATA AND CHANNELS
08/04/2017 12
Data
Sources
Marketing
Attitudinal
Interaction
Web
Mobile
Call-center
Operational
IOT
Partner data
Open Data
Customer Contact
Channels
Website
Email
Phone
Mail
Branch
ATM
B2B
Agent
Mobile
IOT…
What are the optimum channels to
engage with the customer?
What type of forum/device/tool is relevant for this
customer ?
What content and offers are relevant/not relevant ?
What contact mechanisms, themes and treatments
increase customer value?
What data do I need to increase
customer value/retention/advocacy?

Trusted Data
UnStructuredStructured
Source systems
Semi-Trusted/
Augmenting data
Structured
UnStructured
Untrusted/Influencing
Structured
UnStructured
Structured world –
Succinct Defined Predictable
Succinct Defined Formatted
Unified Information Support Architecture
Security Meta-Data Rules Workflow
Data Relationships
Data Patterns
Data Models
Data Federation
Source: The Conversation: Brian Solis and Jess3
Lets talk about Data…..

LETS TALK ABOUT DATA AND DECISIONS…..
Trusted Data
UnStructuredStructured
Source systems
Semi-Trusted/
Augmenting data
Structured
UnStructured
Untrusted/Influencing
Structured
UnStructured
Succinct Defined Formatted
Unified Information Support Architecture
Security Meta-Data Rules Workflow
Data Relationships
Data Patterns
Data Models
Data Federation
Framework
Patterns/Models
Tools/ process Utilities
Process Patterns
Governance
Data
Technology
Interpretation
People
Economics
Traditional
Applications
Analytics
Exploratory
Applications

09/04/2017 15 13
sustainability
Remuneration
surveillance
Suitability/Appr
opriateness
Whistle-Blowing
Occupier onboarding
Collateral
Management
Financing
Investor
Recommendations
Staff Training
Refurbish and refits
Valuations
Evidence
Valuations
Health and
Safety
Certificates
Asset Permits
Independent price
transparency/verification
Supplier Onboarding
Tax transparency
Pricing transparency
Use of Data
Asset
updating
XXXXX Act
XXXXXXX Act
Companies Act
Tax Transparency
Arrangements
Supplier
Safety
verifications
Fire Certificates
Platforms
Health and Safety
procedures
Incident logs
Law of XXXXX
Act
Trade Reporting
Property
Contracts
Data Protection
Act
GDPR
Wi-Fi location
Guidelines
Slavery Act
Health and
Safety
Electronic
Equipment Act
Construction and
Planning
Risk AssessmentsReplacements
Secure by Design
Privacy by Design
Data Registration
Wifi
Usage
Information
Compliance

Operational Metrics
· Customer Revenue:
· Customer Satisfaction:
· Customer Profitability:
· Customer Lifetime Value:
· Brand Awareness:
· Customer Loyalty:
· Conversion Rate:
· Completion Rate:
· Churn Rate:
· Retention Rate:
CEO Metrics
1) Customer Acquisition Cost (CAC)
2) Marketing % of Customer Acquisition Cost
(M%-CAC)
3) Ratio of Customer Lifetime Value to CAC
(LTV:CAC)
4) Time to Payback CAC
5) Marketing Originated Customer %
6) Marketing Influenced Customer %
08/04/2017 16
CLASSIC METRICS

17

….OR THEORY?
08/04/2017 18
Velocity
Variety
Volume
Gartner 2000

BIG DATA AND GOVERNANCE
Big Data
Velocity
Volume
Variety
Noise
Important
Tactical Decision
making
Critical
Possible Data
Mining Source
Lineage
Common
Understanding
Quality
Strategic Decision
making
Regulatory Impact

DATA EQUITY
20
Data Equity
Intrinsic EfficiencyMarket Value
Valuation EconomicImpact

EQUITY
US$2bn for some freely available Weather Data
£2723 – Value for your data
US$BN value of satellite data.
06/04/2017 21

22
0
Business Value
+-
+
0
Data
Effectiveness
-
Improved Data
effectiveness
with no business
value penalty
Improved business
value with little or
no data
effectiveness
penalty
Data Driven
Value
Requires
incremental budget
Necessary, but low
value
Creates resistance
from line of
business and users
Failure
Failure
Failure
DATA BUSINESS VALUE FRAMEWORK

DRIVING TOWARDS DATA COMPETENCY
DATA
WASTERS
DATA COLLECTORS
ASPIRING DATA
MANAGERS
STRATEGIC DATA
MANAGERS
Companies
that collect data but
severely underuse
them
Companies
that collect a large
amount of data but do
not consistently
maximize their value
Companies
that understand the
value of data and are
marshaling resources
to take better
advantage of them
Companies
that have well-
defined data
management
strategies that focus
resources on
collecting and
analyzing the most
valuable data
STAGE 1
STAGE 2
STAGE 3
STAGE 4

Consults on corporate direction, validates strategy, acts as tie-breaking
entity, provides funding
Plans and prioritizes DG program management efforts, resolves issues,
ensures DG implementation, acts as sign-off body
Establishes scope, boundaries, and measurement of DG work efforts
per input of Council, drives DG policy & process development & work,
and delegates work to appropriate data steward(s)
Establishes definitions and develops business rules for key data, works
with DG PMO to define data quality measures and represent data
usage – carries out DG policies & processes
Supports Data Stewards by enabling data quality profiling & cleanup,
data requirements, modeling, design, security, business rules, via skills
and tools baked into projects
Own or manage the systems of origin for data, responsible for
provisioning data to business based on established SLAs, adding
quality features to systems, consulting on problems
Executive Leadership
Sponsorship
DG Council
Business Prioritization
DG Program Mgmt Office
Scope & Direction of Work
Business Data Steward
Definition and Measurement
Technology
Execution of Policies and Standards
Source System Owners
Data Sourcing
DG Business Analyst
Data Governance Activity Facilitation
Manages project intake & priority requests, works with Business Data
Stewards to drive data quality & definitions, drives data governance
working teams
Typical Roles Required For Successful DG
W
o
r
k
i
n
g
G
r
o
u
p
s

Cut losses from fraud by 30% in retail banking
Improved retention rates by 40~%, and increase
product holding by customers by 10%. (Retail)
Increased the number of customers by 1.7m pa
assisting to a 15% compound annual growth rate in just
2 years
Increased sales by 40% by identifying customers sales,
and matching the best salespeople to close the
opportunity.
Increased customer purchase by 65% through data
integration and effective targeting.
Maintain bad debt of <0.05%, compared to the
industry norm of 3.45%.
Reduced number of financial reports by 82% -
providing key fiscal information for rapid decision
making.
OUTCOME: IMPACT THE ANNUAL REPORT.

NUMBERS NUMB US
09/04/2017 26

SLIDE OF AS IS ARCHITECTURE
27

EXECUTIVE DECISIONING
© 2012 DATAFLUX CORPORATION. ALL RIGHTS RESERVED.
Risk Reporting
Customer Prediction and
sentiment
Advanced Asset
Surveillance
Profitability analysis
Investment strategy
Support
Business rule sets, Calculation engines
Value/Cost/Spend
Analytics
Risk Modelling
Management
Transparency
Calculations
Multi-Basis Reporting
model
Semantic Layer
Investors Investments Group Ops/Finance Risk Unstructured dataOperations
Asset
InvestmentInvestor
Relationships
Finance
Risk Regulation
Compliance
Ragged Network
Data model
£?

SOME PARTING THOUGHTS?
What new metrics make sense to your board – drive relevance?
How do you convince your board, drive adoption?
What data is costing me more than the value it delivers?
How can I streamline “data capture” to “data action”?
What technology will get the best out of my data?
When my business needs, or competitive landscape change, will the data
adapt?
08/04/2017 30

TIME FOR DISCUSSION
31
Eur. Ing. John Morton BSc, CEng. FBCS, CITP, MIoD
John.Morton@Consult-CPM.co.uk
+44 7771 740203
Twitter @JohnFMorton
Thanks to Pixabay for Images

Securing executive support for data governance - John Morton

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Securing executive support for data governance - John Morton

Similar to Securing executive support for data governance - John Morton (20)

Recently uploaded

Recently uploaded (20)

Securing executive support for data governance - John Morton