The document discusses the role of a Chief Data Officer in establishing a data governance structure and data quality management program. It notes that currently, data ownership and management is fragmented across different departments with no single party responsible. A CDO would create rules and policies for data governance, establish a data quality team, and ensure standards and accountability for high quality data as a strategic asset. This would help address issues like high costs of poor data quality and system failures due to bad data.

1. Data/M.I. Steering Group
Workshop Presentation
The Chief Data Officer: My Role
Presented by
Ged Mirfin
Chief Data Officer, BLNW

2. “Bad data! I got bad, bad
data. . . bad, bad data,
and bad data's worse
than no data at all!”
Words and Music A. Marscher ©1996

3. “Through 2007, over 50% of
data warehouse and CRM
deployments will suffer
limited acceptance, if not
outright failure, due to lack
of attention to data quality
issues (0.8 probability).”
T. Friedman
Gartner Inc.
January 2005

4. “Little wonder that one third of
businesses have been forced to scrap or
delay the introduction of a new computer
system due to data problems…
Business users are rightly intolerant of
new systems that are delivered filled with
rubbish data and may even fail to adopt
the system. It’s like investing in a new
sports car, filling it with the oil and fuel
drained from your old vehicle, and then
wondering why it fails to perform as it did
on the test drive.”
(source: PricewaterhouseCoopers)

5. “Many businesses
blindly pursue costly
CRM initiatives without
understanding the
challenges and costs
involved.”
Beth Eisenfeld, Research Director,
Gartner.

6. Data Quality
Data Quality refers to:
1. the quality of data. Data are of high quality "if
they are fit for their intended uses in
operations, decision making and planning"
(J.M. Juran).
2. the state of completeness, validity,
consistency, timeliness and accuracy that
makes data appropriate for a specific use.
(Government of British Columbia)
3. One industry study estimated the total cost to
the US economy of data quality problems at
over US$600 billion per annum (Eckerson,
2002).

7. The Gartner View
•Biggest contributory factor in “outright
failure” CRM is lack of process ownership.
•The problem is nobody owns it. When CRM
cuts across different departments it breaks
down at the interface between different
departments. There's no understanding of
the end-to-end process.
•No helicopter overview.
•Therefore no CRM “grand strategy”.

8. Who owns Data?
•Marketing
•Operations
•Brokers
•IT
•CRM Manager
•M. I. Team
•Business Improvement

9. Who has ultimate “ownership” of
Data?
•Everyone and No-One
•Everyone has responsibility for their
individual piece of the jig-saw
•Devolved Control of the Management of
Data is dispersed throughout the
Business
•Has led to anarchical and fragmented
decision-making and ineffective quality
control mechanisms
•No one Individual or Team is in charge

10. Data Stewardship
“Creation of a formalized management
structure including the formalization of
systems and processes and the
establishment of standards, procedures
and accountability for the processing of
data throughout the business as part of
the implementation of a programme
designed to increase operational
effectiveness, transforming data and its
use into a highly valued “strategic
asset”.”

11. “Many companies are spending
millions of dollars on data
warehousing, but many of these
companies are not receiving
optimum return on investments.
Business professionals need to
understand that data quality is not
something IT can fix. IT professionals
can help identify problems and
suggest new ways to eliminate data
quality issues. However, the
business must be willing to own
data, and change its processes to
ensure data accuracy. "
Scott Barnes, Director of the Data Services
Practice at Collaborative Consulting

13. Chief Data Officer
•Data Stewardship Programme led by Chief Data Officer
(CDO)
•CDO responsible establishing a high-level data
governance structure with clearly laid out roles and
responsibilities
•Establishment of rules and policies for “data
governance”
•Formation of a dedicated Data Total Quality
Management Team tasked with measuring and
improving the quality of data including Total Quality
Systems & Data Audit Manager
•CDO is responsible for resolving conflicts across
disparate groups and establishing enterprise standards
on the use of data
•When there are multiple approaches and Team
Managers, Business/M.I. Analysts and Information
Architects cannot seem to be able to agree on an
approach, this person will step in to facilitate the best
approach in the interests of the larger organization
•Principal objective will be to drive greater value out of
data through development of a well thought out data

14. Rationale
•Companies have increasingly become aware of
the value of data as a corporate
•Management of data has become more visible
and crucial.
•With this new visibility, demand and importance
of data, many companies have realized that they
must better define strategic priorities for
management and delivery of data throughout the
enterprise, identify potential service users
through the analysis of data, and significantly
improve their performance through more effective
use of data.
•Increased recognition of need for a person who is
responsible for crafting and implementing data
strategies, standards, procedures and
accountability policies.
•As IT systems grow in size, complexity and cost,
it will be increasingly critical to maintain oversight
of data.

15. Data Governance
• “Encompasses the people, processes and
technology required to create a consistent, business
view of an organization’s data in order to:
• Increase consistency & confidence in decision
making.
• Establish consistent information quality across an
organization.
• Maximize the creation of added value
opportunities from the potential use and
exploitation of data.
• Designate accountability for information quality.”
•The sheer scale of investment in building and
maintaining enterprise-wide data architectures and
integrating their applications and systems across
the Business means it is absolutely vital to create
standards, policies and procedures for data.
•Data Governance initiatives improve data quality by
assigning responsibility to Chief Data Officer solely
for data's accuracy, accessibility, consistency, and
completeness, among other metrics.

16. Data Strategy
•Identifying ways to improve current data whilst ensuring all appropriate data
is available for campaign selections and modelling and segmentation
including a deep understanding of customer characteristics, attitudes &
behaviours and channel preference in order to drive development of customer
strategy designed to reduce attrition of service usage and drive end user
recruitment.
•Ensuring all marketing campaigns and activities are measured and reported
effectively and in a timely fashion such that a continued improvement in value
creation is achieved.
•Searching the market for external data that can further enrich current dataset
and provide further insight into prospects whilst actively managing a test
programme with data/list suppliers.
•Establishing a programme for managing suppliers.
•Monitor both the quality of this data, how it is imported and the impact that
data imports will have on overall internal data volumes/counts and overall data
quality metrics.
• Implementing effective control procedures to ensure that imported data
conforms to internal rules and standards and in accordance with standard
operating procedures.
•Determine which types of data are critical to the organization and therefore
warrant heightened management and apply change management rules and
workflow processes to highly managed data
•Measure the impact of data overwrites/uploads on M.I. Reporting.
•Establishing submission and review processes to filter which user changes
and record amends and up-dates should be accepted/saved.

17. Communicating the Benefits
of Data as a Corporate Asset
“Clearly articulating and communicating
roles and responsibilities of marketing,
client service managers, consulting and
external partners in the provision and
maintenance of customer and prospect
data”
• Selling the vision for data for the business going
forward.
•Evangelizing the adoption of DTQM measures
internally by publicising Data Quality Issues and
producing Regularised and Ads-Hoc Reports on the
Quality of Data.
•CDO will develop effective working relationships with
key data users and the ICT team to develop
performance management solutions.
•Actively pursue collaborative and cohesive working
relationships with all internal personnel.

18. Data “Total” Quality Management
•Managing the downstream impact of data
improvement strategies and the replacement of
bad data with good data on teams across the
business.
•High level understanding of current “data
blockages” and how bad data is affecting delivery
of BLNW services within key functional areas of
the organization is critical to its continued
success or failure.
•Ensuring the successful implementation and
effective execution of Data Quality Management
Initiatives and projects will involve the
measurement of progress and demonstrable by
using simple metrics to establish and sustain
continuing high levels of Data Quality and
Accuracy - Measuring the success and progress
of the initiative such as a reduction in the level of
error percentages.
•Identifying issues early and escalate support
when necessary, setting clear expectations about
what needs to be delivered and quickly adapting
plans in response to change.

19. The Gartner View
“The challenge of poor data quality
presents a vicious circle. If
business users don’t trust the
existing data in a system, they take
less care themselves when entering
new information, which only
compounds the data quality
problem. With data being
recognised as an organisation’s
second most valuable asset, and
poor data quality losing
organisations up to a quarter of
their revenue, this is a topic that
cannot be ignored.”

20. Maintaining Data Integrity

21. Policies and Procedures
• Need to take the organization out of the reactive mode and
establish a raft of data policies and procedures.
• Will address the roles played by IT, Marketing and
Operations on issues such as data ownership, data sharing,
and data privacy.
• Formulation of a Data Governance Charter, Definition and
Prioritization of activities, establishing Rules and
Procedures, Standard Documents and Forms and detailed
roles and responsibilities for the handling of data.
• Data standardization - a business rules engine that
ensures that data conforms to quality rules
• Monitoring - keeping track of data quality over time and
reporting variations in the quality of data. Software can
also auto-correct the variations based on pre-defined
business rules.
• Maintaining Data Integrity - ensuring that end-users are
prevented from breaking the CRM System’s business rules
• Ensuring that data is "whole" or complete – preserving the
condition in which data is maintained during any operation
such as transfer, storage or retrieval

22. Steps to address data quality
• Define data quality in a broad sense, establish metrics to
monitor and measure it, and determine what should be done if
the data fails to meet these metrics.
• Undertake a comprehensive data profiling.
• Incorporate data quality into all data integration and business
intelligence processes from data sourcing to information
consumption by the business user.
• Data quality issues need to be detected as early in the
processes as possible.
• Presentation data that meets very stringent data quality levels.
• The level of data transparency needed can only result from
establishing a strong commitment to data quality and building
the processes to ensure it.
• The poor design of user interfaces is another source of bad
data quality. Poorly designed data entry screens are
frustrating to the user.

23. Failure to Implement Data Total Quality Management: the
Risks
•Recent studies have indicated and have clearly proven that bad data costs money;
results in poor and uninformed decision-making and eventually missed business
opportunities.
•The cost of poor data can equal anywhere from 10-25% of the total operating costs
of an organization.
•Difficult challenge is maintaining high data quality on an ongoing basis.
•Contact data, one of the most critical elements of a CRM system, typically erodes
at a rate of 33% per year. Without proper attention, the data will inevitably become
incorrect, unusable and ultimately untrustworthy.
•We should not underestimate how critical the need for high quality information is
to the business and how bad data really affects the business.
•Poor Quality Data is puts Operational goals at risk! Operational data issues are
hindering the delivery of Key Targets and Objectives.
•Poor Data Quality is seriously lengthening workflows and increasing timescales for
execution “easy/normally straightforward” tactical Marketing/Broker/Relationship
Management & Building and Business Development activity.
•It has also resulted in an engrained and pervasive lack of user confidence in and
beneficial usage of CRM system.
•CRM is increasingly frustrating users promoting conflict & disharmony amongst
Teams and individual Staff Members as they wrongly accord blame
•Poor quality data leads directly to the poor planning, management and delivery of
marketing campaign activity resulting in an inability to “target” businesses that
should be correctly targetted