An overview of Santander's data journey in developing its strategic roadmap using enterprise architecture thinking, best practice data management frameworks and model driven initiatives. References are given to find information on theory and frameworks with case study examples from Santander.

1. Santander’s Data Transformation
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Umran Rafi
Head of Data Intelligence
October 2018

2. Introduction
• Why is defining data strategy (relatively) easy, whilst execution can
be like herding cats?
Key takeways:
• Strategy: Align, define, socialise, repeat!
• Operating Model: Marry the what with the how!
• Build Data Capabilities: Not just data outputs!
• Assess: What works, what doesn’t, what does good look like?
• Roadmap: Articulate your journey with capability improvement!

3. Defining strategy is difficult…
“A cohesive response
to an important
challenge. Not too
much as to stifle the
innovation.
Remember, Culture
eats strategy for
breakfast! But, only if
you ignore it!”
Our strategy is to remove all data
silos so that…
Our strategy is to use predictive
analytics so that…
Our strategy is to move all our data
into the data lake so that…
Strategy refers collectively to: mission,
vision, goals, objectives, strategies and
tactics.
Strategy as strategic
landscape
Edward Dening
Identify what strategy is before you do it
Strategy as a goal
Strategy as direction
Strategy as a plan

4. The problems with strategy execution…
There is a lot of activity going on out
there with data, how do I know we are
doing the right things?
Where can we take advantage of data
synergies across the major change
programmes? Why does everyone
want to create their own Single
Customer View?
Is my data truly an asset or do we just pay
word homage to terms like this? Is the
value of our Customer data on the balance
sheet?
How does this affect EBITDA?
Are we investing in the right areas
of data across the business?
Is our change portfolio aligned with regards
to our data needs, or for that matter, are
they the right strategic programmes?
   Strategy not
sufficiently tied to
operations
(Business &
Operating Models)
Needed Capabilities
not properly
understood or
measured
Planners not
accountable for
delivery
(Programmes &
Projects)
Outcomes/Benefits
aren’t quantified or
traced back to
original goals
The drivers &
motivation of
the strategy are
often misaligned

Surely, a data lake solves all our data
problems…I mean data science…I mean
single customer view…I mean AI,
blockchain perhaps?
Do we just build it and they will come?
How do we truly ‘transform’ without
cleaning up decades of EUC, shadow
IT and multiple single sources of truth?

5. Addressing the failure points between strategy and execution
To address these failure points we should focus on the following three areas

6. Data strategy in practice…don’t make it your third step!

7. Strategy to execution: “5 Step Approach”
INFLUENCERS
ASSESSMENT
MEANS ENDS
MOTIVATION
MODEL
OPERATING
MODEL
CAPABILITY
MAP
CAPABILITY
HEATMAP
DATA STRATEGY
ROADMAP

8. Driver’s influence the direction of data strategy
https://www.omg.org/spec/BMM/About-BMM/
Influencer
External Influencer
Environment Technology Regulation
Supplier Customer Competitor Partner
Internal Influencer
Corporate Value
Implicit
Explicit
Infrastructure
Issue Assumption
Resource
Habit
Management
Perogative
Means End
Desired ResultCourse of Action
Planned by means of
a component of the plan for
Vision
Goal
Objective
Directive
channels efforts toward
supported by
amplified by
amplifies
makes operative
made operative by
Implemented by
implements
quantified by
quantifies
supports the achievement of
has achievement supported by
formulated based on
source of
governed by
governs
Business
Policy
Business
Rule
Tactic
Strategy
Mission
effects enforcement level of
has enforcement level affected by
motivated by
provides impetus for
Assessment
Strength Weakness
Opportunity Threat
Potential Impact
Risk Reward`
identifies
significant to
The Business Motivation Model specification provides a scheme or
structure for developing, communicating, and managing business
plans in an organized manner

9. Our Mission

10. Our Vision
https://www.jimcollins.com/article_topics/articles/building-companies.html
Big Hairy Audacious Goals

11. Our strategic approach
Manage
Organise
Prioritise Exploit
Understand

12. Target Operating Model on a page
Metadata
Metamodel
Metadata
Strategy
Organisational
Culture
Metadata
Governance
Metadata Capabilities
Gartner I&T
Operating Model
Business
Model Canvas
Gartner
Pace Layering
OpenGroup
IT4IT

13. Introducing capabilities
Capabilities link strategy to execution
“An ability that an organisation, person or system possesses. Capabilities are typically expressed in general
and high level terms and typically require a combination of organisation, people, processes and technology
to achieve. “ The Open Group

14. Our Data Capability Map: Level 1EXPLOIT
BI & Analytics
Management
Data
Engineering
Data
Delivery
Data
Science
MANAGE
Data
Quality
Data
Governance
Data
Security
Management
Document &
Content
Management
Reference &
Master Data
Management
UNDERSTAND
Data
Architecture
Data
Strategy
Management
Metadata
Management
Data
Design
CMMI Data
Management Maturity
DAMA Data Management
Body of Knowledge
EDM Council Data Capability
Assessment Model

15. Our Data Capability Map: Level 2

16. Assess the maturity of each capability
Why? Because this will:
• Provide a fact-based
method for identifying
strengths and
weaknesses
• Identify the
capabilities that limit
our ability to achieve
strategic goals
• Act as an input into the
planning and
prioritisation of
investments and
resources
“Maturity“: The degree of formality and optimization of processes, from ad hoc practices, to formally defined steps, to managed result metrics,
to active optimization of the processes (CMMI)
Managed
MATURITY
LEVEL
2
Defined
MATURITY
LEVEL
3
Optimised
MATURITY
LEVEL
5
Measured
MATURITY
LEVEL
4
Performed
MATURITY
LEVEL
1
Unpredictable and reactive
Data objectives, priorities and scope reflect stakeholder objectives within current
projects. No formal framework to analyse cause and effect exists.
Managed on the project level
Data objectives, priorities and scope are aligned with business objectives. A process for prioritising
projects across business units, from a data perspective, as well as traceability to business objectives, is
established and followed. Custom frameworks are used to articulate and model the strategy.
Proactive rather than reactive
The organisation's data strategy representing an organisation wide scope is established, approved, promulgated and
maintained. A repeatable process is established for analysing strategic influences and their impact on defined drivers,
strategies, goals and objectives. Industry recognised frameworks are used to model the strategy with training programmes
established to further improve strategic analysis. The data strategy is consistent with data management policies.
Measured and controlled
Statistical and other quantitative techniques are used to evaluate the effectives of strategic data objectives in achieving business
objectives, and modifications are made based on metrics. The organisation researches innovative business processes and emerging
requirements to ensure that the data strategy is compatible with future business needs.
Stable and flexible
The organisation researches and adopts selected industry best practiCes for strategy and objectives development. Contributions are made to industry
best practices for data strategy development and implementation.
1
2
3
5
4

17. Use dimensions for a more granular assessment
DATA (DMM)
TECHNOLOGY (Fit)
PROCESS (CMMI)
PEOPLE (P-CMM)
DIMENSION 4: MEASURED 5: OPTIMISED3: DEFINED2: MANAGED1: PERFORMED
ADEQUATELY
RESOURCED AND
OPTIMALLY SKILLED
OPTIMALLY
RESOURCED AND
HIGHLY SKILLED
ADEQUATELY
RESOURCED AND
SKILLED
SKILLED BUT POORLY
RESOURCED
POORLY RESOURCED
OR UNSKILLED
METRICS DEFINED AND
MEASURED
CONTINUOUSLY
IMPROVED THROUGH
MEASUREMENT
STANDARDISED,
CONSISTENTLY
FOLLOWED
MONITORED AND
CONTROLLED
PERFORMED ADHOC
MAINTAINS INTEGRITY
AND IS ACCURATE
SEEN AS CRITICAL TO
SURVIVAL
CONSISTENT,
ACCURATE AND VALID
AVAILABLE BUT POOR
QUALITY
UNAVAILABLE OR
POOR QUALITY
SUPPORTS BUSINESS
AND IT NEEDS
CONTRIBUTES TO THE
EFFICIENCY OF THE
CAPABILITY
SUPPORTS THE
BUSINESS NEEDS
EASY TO USE BUT NOT
RELIABLE
DIFFICULT TO USE OR
FREQUENT ERRORS
Create a standard unit of measure

18. Capability assessment: “7 Step Approach”
FIVE
Assessment results
are reported by
creating a Capability
Heatmap as a current
state assessment.
This approach
indicates Capability
Performance only, no
representation of
business importance
for each business
capability (at this
stage).
REPORT THE
RESULTS
SEVEN
Leveraging
information obtained
in the current state
assessment can help
drive the
conversation about
how to close the gap
between current
state and desired
future-state.
Gap statements
should be:
 Forward looking
 Specific
 Action-oriented
 SMART
USE THE RESULTS
SIX
There may be a high
degree of
disagreement about
the performance
level of our data
management
capabilities.
Recommended
approach is to
conduct a workshop
with key
stakeholders and try
and reach alignment
before moving
forward.
Alternatively, we can
take a weighted
average.
GAIN BUSINESS
ALIGNMENT
FOUR
Maintain consistency
from respondent to
respondent.
No right or
wrong answers.
Participant s answers
kept confidential.
Evaluator should ask
for clarification,
where needed, but
not confront
answers.
Participant s role
captured for
reporting purposes
only.
PERFORM THE
ASSESSMENT
Option A: Include a
wide-range of
impacted individuals
both inside and
outside the
organisation.
Option B: Include a
small internal group
associated with
planning investment
decisions.
Option C: Include a
range of internal
individuals to get a
broad perspective.
THREE
IDENTIFY WHO TO
INCLUDE
TWO
The most critical step
to performing a
capability
assessment.
Each individual
completing the
assessment will be
asked to use the
same unit of
measure to score
their answers.
CREATE A
STANDARD UNIT
OF MEASURE
ONE
Capabiiltes can be
assessed at Level 1
(high level) or at
Level 2 (detailed).
Option A: Select all
capabilities within
the Data
Management
Capability Map and
complete a high-level
assessment.
Option B: Only select
those capabilities
that the bank has
identified as being
most important.
IDENTIFY
CAPABILITIES TO
ASSESS

19. Assessment: Transformation on a page
Describe current, transitionary and desired future states for each capability

20. Conduct gap analyses to determine investments
• Identify underperforming capabilities and
capabilities that are being over or under
invested in
• Rank and prioritise capabilities for
investment and sequence to the strategic
intention (from the motivation model)
• Can now highlight where performance is the
constraint or the enabler
• SWOT analysis can help
• Matrices help identify duplication and gaps:
• CAP v Programmes / Projects
• CAP v Goals / Objectives
• CAP v Issues
• CAP v Processes
• CAP v People
• CAP v Data
• CAP v Applications
• CAP v Technologies
Capabilities
1: Data Governance
2: Data Quality
3: Data Architecture
4: BI & Analytics
5: Data Engineering

21. Gap assessment example
Definition: Establish the processes and infrastructure for specifying and extending clear and organised information
about the structured and unstructured data assets under management, fostering and supporting data sharing, ensuring
compliant use of data, improving responsiveness to business changes and reducing data-related risks.
Metadata Management
People Process Data Technology
Current State 2 2 1 1
Future State 3 3 4 3
1 3Level of Change Required
Transformational
Incremental, Transformational, Remedial or Developmental
An example of applying the concept for
arriving at a Desired Future State at a
lower level. Participants are asked to
identify a desired future state for each
of these dimensions:
• People
• Process
• Data
• Technology
Leveraging information obtained in the
current-state assessment can help drive
the conversation about how to close
the gap between current state and
desired future-state.
People: Identify all stakeholders managing each repository, socialise strategy and vision, reorganise and where
necessary train workforce to manage all assets under management.
Process: Map existing processes for curation and maintenance of metadata in each repository, identify gaps and
duplication, re-engineer processes into one integrated value stream.
Data: Analyse state of quality in each repository (using DQ dimensions), map gaps where metadata does not join up
(e.g. applications link to servers), create overall metamodel for repository integration.
Technology: Decommission legacy tools and spreadsheet asset repositories, extend and build upon Ab Initio’s
Metadata Hub from GDPR use to become the asset marketplace integrating all repositories.
Current v Desired Future State
How to Achieve Desired Future State

22. Build the business case for data investment
Data Architecture & Design
Align data with business and technology
Maximise the strategic use of data establishing a new
architecture capability. Identify and promote project
synergies, share knowledge by removing SME dependencies,
reduce duplication in technical processes and control
mechanisms. Enterprise wide data consistency is delivered
through a two speed architecture model that simultaneously
caters for business agility (speed) and trust (quality).
X%
INVESTMENT ROI
£Xm
Metadata Management
Catalogue data and IT assets
Rationalise the entire data and IT asset inventory estate,
streamline and automate processes, reduce headcount and
open a new wave of metadata driven development
processes to accelerate the digital agenda. Catalogue the
most valuable data, its origins and destination, what it
means and reduce all risk related implications.
X%
INVESTMENT ROI
£Xm
BI & Analytics
Exploit data for decision making
Increase adoption of a data-first paradigm with faster,
autonomous access to data, streamline investments in BI
skills and technologies through convergence and
consolidation. Improve the accuracy and consistency of
operational, regulatory and business performance reporting
and reduce headcounts through automation of manual,
labour-intensive data manipulation activity.
X%
INVESTMENT ROI
£Xm
Data Science
Transform data into value
Better understand, access and model cleansed data for
machine learning use cases that reduce costs, optimise
spending, detect revenue, protect revenue and improve risk
decision making. Improve value-driven hypothesis
development, tools for faster data wrangling and the
management of statistical models and a greater operational
readiness for moving from idea to benefit to production.
X%
INVESTMENT ROI
£Xm
Data Engineering
Build data products
Build high quality data components for customer focused
digital products that are more reliant, performant, scalable
and delivered faster in a more cost effective manner. Upskill
internal resources and deliver test driven development,
continuous integration and continuous delivery.
X%
INVESTMENT ROI
£Xm
Data Governance
Exercise authority and control over data
Optimise the management of data issues across the bank
with an effective governance structure that provides clear
distribution of data decision making. Create a lean,
streamlined data organisation that aligns with business
priorities whilst ensuring compliance with regulatory,
legislative and group demands concerning data.
X%
INVESTMENT ROI
£Xm
Data Quality
Ensure data is fit for purpose
Drive a strategic, holistic approach to tackling data quality
across the bank, prioritising data improvement based on
value generation, significantly reduce arbitrary governance,
controls and operational costs and develop a catalogue of
clear, business focused data quality services. Use AI for auto
detection and correction.
X%
INVESTMENT ROI
£Xm

23. Roadmap: Putting it all together
Strategic Intention
(why and what is to be done)
Current State
(as is blueprint)
Strategic Initiatives
(work organised and phased)
Target State
(to be blueprint)
Capabilities Considered
(what needs to change)
Data Principles
(guide behaviour)
Reference Model
(ensure architectural consistency)

24. Summary
• Data Strategy: align the why, socialise the what, embed the how
• Operating Model: organise how you make it work
• Data Capabilities: start comprehensive but prioritise areas of focus
• Capability Assessment: make your destination as real as possible
and build business cases for data investment by calculating ROI
(not forgetting that strategic enablement is priceless)
• Roadmap: the journey is more important than the destination!
DATA
CAPABILITIES
DATA
STRATEGY
OPERATING
MODEL
ROADMAP
CAPABILITY
ASSESSMENT

25. Thank you
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