A talk specially prepared for McMaster University. There is more benefit to thinking about big data as a paradigm rather than as a technology, as it helps shape these projects in the context of resolving some of the enterprise's greatest challenges, including its competitive positioning. This approach integrates the operating model, the business model and the strategy in the solution, which improves the ability of the project to actually deliver its intended value. I support this position with a case study that created audited financial value for a major global bank.

1. Leading enterprise-scale big data
business outcomes
Specially prepared for McMaster University by
Guy Pearce
7 November 2016
Value created Opportunity location

2. INTRODUCTION
1

3. So what is big data?
2
Gartner's enhanced definition in 2011, based on Meta Group's (now Gartner) 2001 definition

4. Why big data?
3
Image Source: https://media.licdn.com/mpr/mpr/AAEAAQAAAAAAAAlTAAAAJDJjYWVmNTNjLTc1MmMtNGE1Zi1hMWY3LTk3MmE0ZjNmMWE4OQ.png
Data and business agility constitute today's new competitive frontiers
Today's data accessibility, its rapid propagation, its diversity and the dynamics of innovation around it
all means that traditional areas of competitiveness, like differentiation, niche business, pricing,
suppliers, channels, patents and even sources of funding, are less sustainable than they once were.

5. Seeing a big data initiative as a paradigm rather than as just a technology can
be key to its business success
4
Technology
Technology, of which Hadoop could be a solution, is but one part of the business ecosystem

6. Critical success factors
5
Team alignment
Technology success doesn't imply business success

7. Measuring success
6
Having a clearly articulated business objective makes it easy to define the appropriate
measures of (IT) success

8. LEADING ENTERPRISE-SCALE
BIG DATA BUSINESS OUTCOMES
Based on a case study in a global bank
7

9. About the big data case study
• Conducted in a global bank (70,000 employees)
• Positively impacted 1,300 branches and almost 5,000 employees
• Focused on big data variety, data fusion and analytics as a means to generate incremental
financial value for the bank
• Specialist aspects of the case study have been presented in:
• Canada
• Sauder Business School , BC (behavioural analytics)
• Dalhousie, NS (high strike rate data-based marketing)
• USA
• Marketing seminar in Las Vegas (Big data analytics value generation)
• Marketing seminar in Los Angeles (Big data analytics value generation)
• UK
• University of Birmingham (data-based brand equity optimization)
• The case study was also available from a server at St Mary's University, NS, for 6 years
• Incremental investment (hardware and software) < $1m
• Incremental business benefits (6 months) = $94.95m
• 6 months ROI > 9495%
8
The small investment is a hidden gem in this
story. It was enabled by sweating some of the
bank's under-utilized information assets, and by
negotiating sharing arrangements with another
BU for a high performance analytics server

10. Begin technology enablement projects with the end goal in mind
What is the big picture business problem you're trying to solve?
• Market research showed the bank positioned less favourably than historically
• 3rd place for “have competent and knowledgeable staff”
• 3rd place for “understand me”
The findings included that not understanding the customer was a primary reason for customer attrition
• 3rd place for “make an effort to understand me”
• Market share was under pressure
9
The golden thread in all of these strategic issues was the customer.
A data-driven customer-centricity initiative was born.

11. Incremental or enterprise-wide design?
Plan with the end in mind, but …
10
Incremental Enterprise-wide
Focused on
enterprise
consistency
Focused
on benefits
realization
Risk: Inconsistent deployment
across the enterprise
Best For?
• Where most benefits are
realizable across few BUs
• Easily facilitates POC
Risk: Extended time to deploy
and complex design
Best For?
• Where consistency is a
strategic or regulatory
imperative
• Can be difficult to deploy
POCs until the whole tool
is stood up
Management's expectation of the project was to create rapid auditable financial value using
the bank's data. Followed an enterprise-wide approach but with incremental POC's
… balance strategy with tactics
Quote source: https://s-media-cache-ak0.pinimg.com/originals/6c/d2/64/6cd264ec9acec33c6580f96194519d42.jpg
POC enabled glowing word-of-mouth
reviews, facilitating tool adoption

12. Project organization and governance
11
Nearly half of enterprise data initiatives fail because of poor integration between the operating
model and the business model KPMG 2014
Strategy, Governance and Enabling Stakeholders
Marketing
Finance
Group IT
Audit
Business Model Stakeholders
Product
Management
Channel
Management
Segment
Management
Operating Model
Skills and Capacity
Operations
(Enabling
Processes)
Data, Analytics
and Technology
Project Objectives
As complex as an enterprise
technology deployment is, it’s
only a part of the big picture
Group Strategy
Positioning big data in the big
picture eliminates doing big
data for big data’s sake
Processes
Processes
MajorstakeholdergroupsatAVPorhigher

13. Data fusion (data enrichment)
One of the key big data competencies to get right
12
Relational
table
Relational
table
Relational
table
For data from the same source: Database joins
For data from disparate sources: Finding
common data for joins (caution)
Primary Key
Relational
table
External
table
Fused
data
e.g. national assets
(loans) by SIC code
e.g. national GDP by
NAICS code
SIC to NAICS
transformation table
e.g. relationship
between advances and
GDP per industrial
classification
No defined
relationship
Data integration is a CSF for achieving a single view of customer across the bank. The bank's
data was integrated with data from eight qualified external sources

14. Analytics on large volumes of diverse data, all appropriately fused, provided
deep strategic insights
 One internal data source (internal bank data)
 Eight external data sources
 Structured and "unstructured" data
 Longitudinal Behavioural Analytics,
by customer
 Risk-Return Portfolio Modelling, by
customer
 Contribution Profiling, by customer
 “Next Best” Predictive Analytics, by
customer
 Geospatial rendition of “next best”,
aggregated by municipal area
13
 Data fusion
Identifying regional management centres with
detailed qualification of sales potential

15. Technology is but a part of a business data value chain
14
Value
Customers
Front Line Staff
CRM
Big Data
Analytics
B
A
B = Stakeholders and Team
A = Strategy Alignment. Purpose
Training and change
management
The means of getting
info to the front line
The reason we did all
this: customer-
centricity
From customer
retention to financial
value
Processes: The glue holding it all together, from strategy to value
The engine room
Strategically aligned.
Top level visibility
One vision. Change
management

16. Project performance
15
The big data engine identified and qualified product
and services gaps in the customer's product
portfolio by:
• The single view of customer
• Knowing all the products and services
the customer used across the bank
• Data fusion
• Made inferences about what products
the customer might have at competitors
• Identified opportunities for expanding the
customer's product portfolio
• Identified the geographical locations
where sales activity should focus
Distributed probability-ranked sales opportunities to
1,300 branches by means of the CRM system
Sales staff could now have very specific
conversations with their customers given detailed
information in the CRM system (personalization).
Knowledge of the bank's products also improved
Sales were captured by the sales person into the
CRM system against an identified item
• Validation by finance was important because
bonuses are paid on sales
Measurement: Sales strike rate was
nearly 1 in 2 (over 40%), enabling
$94.95 million in value in six months
Notes: Addressed the strategic issues of
sales staff not knowing their customers,
and not knowing the bank's products
Notes: The bank's data was fused with
eight qualified* external data sources
*Qualified with respect to data quality
Notes: Data quality was assessed and a
year-long data cleansing initiative was
undertaken
Great financial outcomes (1) were driven by improved customer centricity (2), in turn enabled
by big data analytics (3). Note that (3) produces no business value without action
1
2
3

17. Outcomes
16
What they said:
“We will use your work to boost sales scorecard performance” AVP sales
“Come and help us meet our scorecard targets” AVP New Business
“Where have you been all our lives?” Provincial sales manager
“When are you coming to help us” Provincial sales manager
“The great thing is that it’s not rocket science” EVP
“We need to entrench your work” VP
“This is big” VP
“Go big” EVP
What they got:
Audited value enabled

18. 15 considerations
1. Use atomic data, not derived data
2. Reference data may need cleansing
3. Get out and be seen in the organization
4. Technology matters, but it is not everything
5. Metadata may need to be updated or formalized
6. Big Data enables ROI, it doesn’t generate it by itself
7. Big Data success demands multidisciplinary inputs
8. Sustained, cross-functional stakeholder engagement is critical
9. Enterprise information policies may need updating or formalizing
10. Data (Variety) – external can impact the organizational risk profile
11. Repeatability, consistency and efficiency demands detailed processes
12. Data quality assessments and (tactical) cleansing amplify performance
13. Internal and external data will be mismatched, requiring data fusion skills
14. A data governance organization simplifies the process of identifying SMEs
15. If there is no compelling business case for "big data", don’t do it (resource allocation)
• “We need to entrench your work” VP
• Institutionalization is critical for sustainability
17

19. Conclusion
18
• Data is a new competitive frontier
• Begin with the end in mind
• Technology, processes, skills and capacity are all enablers (operating model). None of them
can sustainably enable today's competitive enterprise on its own
• Technology success is as good as the business success it enables. To achieve business
success, the alignment of technology with business objectives of the business is key
• Technology success requires the diverse business and technology stakeholders to have the
same vision for the technology; this requires skilled negotiations and stakeholder interactions
• Processes serve as the enterprise glue, linking the operating model to the business model, and
both of them to the execution of the business strategy
• Data leverage can negatively impact the risk profile of the organization. Good corporate
governance requires that this is appropriately monitored

20. APPENDIX
19

21. About the presenter
• Have served on the Boards of Directors of public and private companies in Banking, Financial
Services and Retail over the last decade
• Currently serve on the Board of Directors of a Canadian Not-For-Profit
• Have created ~$150 million in auditable value directly attributable to enterprise data
interventions, one being tonight's big data analytics case study ($94.95 million), and another
in Business Intelligence for $50 million (also published in the public domain)
• Guest on live TV debating data privacy in Canada. Keynote at the National Privacy and Data
Governance Congress in Calgary
• Invited as committee member of a hackathon hosted by the Ontario Securities Commission,
looking for practical innovations that can streamline the regulatory environment
• Previous semester guest lecturer at a global business school, delivering and examining a
course on Organizing and Leveraging Corporate Data for Master's students
• Independent consultant specializing in strategy, risk, data, IT, analytics and governance
20
Please join me on Linkedin at https://ca.linkedin.com/in/guypearce (put "McMaster" in the
message section so I know who you are), or contact me at pearcegf@gmail.com