Financial Services - New Approach to Data Management in the Digital Era

A New Approach to
Data Management
in the Digital Era
September 2016

• Digital agenda
• Multichannel integration
• Customer centricity and
Customer experience
management
• New products – connected
auto, “insurance on
demand,” connected life
• Cost efficiency
• Underwriting profitability
• New flexible and fast
competitors (Fintech and
digital by design)
• Internal steering
• Solvency II
• International Financial
Reporting Standards:
IFRS 4.2, IFRS 9
• Local Generally Accepted
Accounting Practices (GAAP)
• Global Systemically Important
Insurers (G-SIIs)
• Insurance Distribution Directive
• Packaged Retail and Insurance-
based Investment Products
• Federal Data Protection Act
Key drivers for a new approach to data management
New regulatory and business drivers in combination with emerging
technologies require new data management thinking in a digital era
Copyright © 2016 Accenture. All rights reserved. 2
New
Data
Management
Regulatory Drivers Business Drivers

Risk and regulatory managementEnhanced productivity and efficiency
Discovery of new business opportunitiesData-driven decision making
3
Advanced technologies and capabilities to extract value in new digital era
and provide opportunities for CFOs to play a greater strategic role
Copyright © 2016 Accenture. All rights reserved.
The timely availability of large amounts and different types
of data allows for decision-making processes based on
data rather than intuition
New technologies to automate manual business
processes and handle large volumes of unstructured
data at lower costs
New solutions to extract valuable insights and facilitate
the discovery of new business opportunities, and allow
CFOs to become trusted advisors to the CEO
Agile infrastructures and processes able to manage what
is required now, and what is likely to be required in the
future by regulators
Opportunities
for CFOs to
play a larger
strategic role
The potential value behind big data adoption
Cost
Reduction
Revenue
Growth
Insights
Discovery
Data
Monetization
Strategic
Decisions
Investment
Choices
Process
Automation
Low Storage
Costs
High
Scalability
CFO and CRO
Integration
Real-time
Simulations
Regulatory
Reporting

Data
Governance
Data
Architecture
Data
Management
Data
Conversion
Data
Security
Data Strategy
Data
Quality
Data organization
Data policies and
procedures
Master data
management
Metadata
management
Data standards
Data profiling
Data cleansing
Data monitoring and
compliance
Data modeling and
taxonomy
Data storage and
access
Data classification
Data privacy and
masking
Data retention and archiving
Data
Movement
Data
Storage
Data
Creation
Data
Retirement
Enterprise
Data
Management
 Privacy
 Liability
 Sensitivity
 Intellectual property
 Lack of skills (data scientist)
 Changing business models and
technical solutions
Data management disciplines: Key big data obstacles
Key differences and implications for data management can be found in
three key building blocks of Accenture’s Data Management Framework
1
2 3
Data integration
Data
Usage

Copyright © 2015 Accenture All rights reserved. 5
The goal of data governance is to
deliver comprehensive, complete,
correct, clear, reliable and therefore
high-quality data for supporting
managerial decisions
Data governance assigns the
responsibility for company data and
data-related business processes
based on binding rules, roles and tasks
Data governance is not a one-time
action, but a continuous process to
help improve the quality and usability
of data
Data governance focuses primarily on data quality
management, metadata management and
formulating obligatory rules:
• for data quality and metadata management
topic areas
• partial for functional data architecture
• not for data protection and archiving
Additional data management topic areas are currently
covered by other functions
Goal
Function
Duration
Data Governance
Dataquality
management
Metadata
management
Functionaldata
architecture
Data Management
Technicaldata
architecture
Dataprotection
andsecurity
Storageand
archiving
What is data governance?
Data governance is a continuous process to deliver high-quality data

Data
Governance
• Complying with internal
guidelines and responding to
increasing regulatory
provisions, such as:
– Solvency II, IFRS9, IFRS4PII
requirements
– Requirements stemming from
audit standards and general
guidelines
• Controllability of increasing
complexity and volume of data
by establishing and
standardizing data management
processes
• Increasing applicability and
common usability of company
data, especially by creating
unified definitions
• Eliminating redundancies
• Reducing effort for the
remediation of quality issues
in operative run, as well as
during changes to IT systems
• More effective database control
processes through improved
data quality and availability
• Creating transparency within a
data system and a taxonomy
free of contradictions
• Reconcilability within risk data
and to financial data using
consistent storage and
definition of data
• Groupwide clear and complete
assignment of responsibility
for data
Reduction
of complexity
Compliance with
regulatory
requirements
Transparency
Improving
efficiency
Data governance uses
Data governance helps insurers comply with external requirements

Data Governance Suite of Services Value Proposition
Top Challenges
Accenture Contribution
Suitable results
within short
timeframe
Transparency
on bankwide
data quality
Potential for
lower capital
requirements
• Breakthrough siloed processes and IT
architectures and create groupwide view
on data quality
• Align information definitions between
business and IT as well as inter-divisional
• Timeliness of reporting and remediation
• Set of pre-defined and customizable
data quality rules
• Customizable data quality dashboard
for root cause analysis of data quality
anomalies and risk reporting
AcceleratorFeatures
Set of proven
data quality rules
Out-of-the-box
operational and
management
reports
Pre-configured
data governance
workflows
Business
glossary and
data lineage
Flexible
report
designer
Integrated
workflow
designer
DQ tool and
configurable
remediation
process
Accenture’s Data Governance Suite of Services fast tracks projects and
allows for the “fit-for-purpose” of data
Accenture Data Governance Suite of Services
CoreFeatures

Cost-effectiveness: Significant reduction in development effort or licensing fees
Flexibility: Flexible in the scope of services to be consumed and ease with which to extend and
reduce the service scope quickly. The Accenture Data Governance Suite of Services offers the
flexibility to cover all components of the Data Management Framework
Prevention: Including the entire processing chain, data quality anomalies can be detected quickly and
corrected at their source system
Compliance: Accenture distilled the compliance experience of various global data quality programs
and our Data Governance Suite supports Solvency II, IFRS9 and IFRS4PII requirements
1.
2.
4.
3.
Focusing: Internal staff can focus on higher value tasks. Reduction of internal time-consuming efforts
with regards to root cause analysis and coordination5.
Accenture can support insurer’s data governance program and add value
Benefits of Accenture Data Governance Suite of Services

Accenture Data Quality
Tool with “Collibra NV”
Accenture has developed a set of tools for managing data governance
based on our experience and industry knowledge
Set of pre-defined
reports on current
status of data quality
Traceability of data
elements through all
architecture layers,
including transparency
of all transformation
and aggregation steps
Architecture overview
for the Data Governance
framework using
Informatica LLC
products
Data quality monitoring
boards, with self-defined
KPIs and in case of
anomalies the analysis is
supported through drill-
downs in the data flows
Accenture Tools and Accelerators Overview
Accenture Data
Governance Framework in
“Informatica”
Accenture Data Lineage
Tool
Accenture Accelerator for
SAS Institution Inc.
software

Why
Data Lineage: Traceability of data elements from the original entry in the transactional systems through
DWH layers to reporting systems, including transparency of all transformation and aggregation steps
Data Dictionary: Documentation of content and semantics of all data elements. Provide structure and
taxonomy of data elements
Data Management: Documentation of data ownership for all data elements
Production Status: Logging the status of all data provisioning and calculation processes for a given date,
proving completeness and quality of reports
How
A tool for storing, displaying and querying metadata; this tool needs to be technically integrated with all
extract, transform, load (ETL), DWH and reporting systems
Processes to allow manual maintenance of metadata by business and IT analysts where these cannot be
automatically sourced from systems and processes
Appropriate governance to deliver completeness and quality
Metadata serves several purposes:
Metadata management requires:
Stringent metadata management across business units allows for a
higher degree of traceability and data availability
The “Why“ and “How“ of metadata

Differences, challenges and consequences
Big data analytics initiatives require sound metadata management
approaches to be effective
Data warehouse (DWH)
models evolving in cycles
Data is constantly evolving
Data Usually:
• Discovered
• Collected
• Governed
• Stored
• Distributed
Data Often:
• Growing
• Highly dynamic and
proliferating
• Quicker and different
production-consumption
cycles
Usually: ONE central
governance
Often: Multiple governance
processes
Data is mainly structured
Vast amount of
unstructured data
Use Case: Repeatable,
standardized and robust
Use Case: Experimentation
and speed
Consequences
• Erroneous results (e.g. key performance
indicator (KPI) calculation and report definition)
• Project delays (e.g. due to transformation
effort, quality measures and rework)
• Multiple interpretation of results and
consequences in corporate steering
Typical Challenges
• No senior sponsorship for metadata initiative
• Metadata scattered across various
spreadsheets, databases, applications, …
• IT pushed in the lead, limited involvement of
the business
• “Make-work” non-value adding initiatives
Traditional Big Data

During metadata gathering stage, functional and technical objects are
defined and documented
Define and Document
Objects
› Functional and technical
objects/elements are defined
and documented
Attributes
Functional
Objects
Metadata and data quality
Metadata
Collection
Document
Objects
Define
Interrelationships
among Objects
Define
Responsibilities
Attributes
Technical
Objects
Definition of
Responsabilities
› In a business department
map, data owners and data
stewards identified for each
business object/element
Data Governance
Reference Model
› The interrelationships are
described based on object
modeling
Functional data tree
Functional level
Data lineage
Technical level
Metadata Collection
› List of metadata and
attributes to be collected for
business objects/elements
Metadata
Dictionary

Perspectives on data protection
A well-established data governance connects legal to technology by translating data protection
requirements into technical solutions
Data privacy in a big data context needs to be viewed from three
perspectives: legal, data governance and technology
Legal:
A major common denominator derived
from the European Union jurisdiction
and proven principles defines data
protection core requirements
Data Governance:
An analytics-focused data governance
translates data protection core
requirements into technical solutions
Technology:
Technical solutions support and allow
for data protection compliant analytics
Governance
Data
Legal
Data Protection Core
Requirements
(Major Common Denominator)
Roles, Responsibilities, Policies
and Procedures
Platform Architecture, Data
Integration Architecture,
Tool Configurations
and IT Security Measures
Technology

Road to rapid analytics implementation from a data protection (DP) perspective
Data protection process from assessment to operationalizing governance
• Data protection
status quo
determined
• Key stakeholders
identified
• Awareness for data
protection created
• Analytics vision
established
• Big data capabilities
assessed
• Existing data
governance
processes identified
• Analytics use cases fully
specified
• Data dictionaries for data
sources defined
• Data treatment procedures
suggested
• Architecture fully specified
• Data flows designed
• DPO fully involved and
convinced
• Analytics environment
delivered
• Security concept and
roles implemented
• Data quality and
lifecycle management
established
• Data access concept
implemented
• Implementation
completed and DPO-
approved
KeyAchievements
• Primary analytics use
cases identified
• Key data sources
identified and criticality
pre-assessed
• Analytics environment
determined
• Lab and factory concept
established
(separation of concerns)
• Key roles defined
• Data Protection Officer
(DPO) onboarded
• Processes to keep DPO
updated established
• DPO ad-hoc reporting
implemented
• Full data governance
framework established
Start
DP
Awareness Cloud
DP
Approval
Demo
Sign-Off
End
Yes Yes Yes Yes
No
Assess Initial
Situation
Initiate
Analytics
Specify
Analytics
Environment
Implement
Analytics
Environment
and DP Concept
Operationalize
Analytics
YesNo No No
Legal
Assessment
14Copyright © 2016 Accenture. All rights reserved.

Monitoring
• Monitoring of data quality
• Identification of data quality issues
Metadata
Gathering
Data
Profiling
Monitoring
Clean-up
Reporting
Clean-up
• Assessment of data quality
impact
• Perform data cleansing Data Profiling
• Define data quality control
points on data lineage
• Design and implement data
quality controls
• Set data quality thresholds
• Report data quality score in DQ
report
Metadata Gathering
• Identity steering relevant
reports
• Identify key metrics
• Breakdown of functional data
tree elements
• Assign data owners and data
stewards for critical data
items on the functional data
tree
• Map functional data tree to
data lineage
• Documentation in a business
glossary/directory
Reporting
• Regular DQ reporting to
responsible committees
• Assessment of impact of data
quality issues and make
decisions on DQ initiatives
Supported by Accenture‘s Data Governance Suite of Services
Improving data quality is a continuous process and a consistent
methodology is encouraged to address data quality aspects
Data quality (DQ) methodology

New data quality tool should be considered in response to fast-changing
economic environment and digital revolution
Big insurers face a deep evolution in clients’ use of their products and important changes
in market forces and regulation
Revamping
of
market
forces
Radical
evolution in
client
behavior
Building
industry
boundaries
Pressure on
profitability
Emerging
regulation
(data privacy)
New
competitors
in the digital
era
Insure profiles
and uses, not
persons and
goods
Increased
client volatility
Digital pervades all business domains and has important implications on new
business opportunities and risks
In-depth client
understanding
Embedded
risk management
and more accurate
performance management
More
accurate
performance
management

A New Approach to
Data Management
in the Digital Era
17
Disclaimer:
This presentation is intended for general informational purposes only and does not take into account the
reader’s specific circumstances, and may not reflect the most current developments. Accenture
disclaims, to the fullest extent permitted by applicable law, any and all liability for the accuracy and
completeness of the information in this presentation and for any acts or omissions made based on such
information. Accenture does not provide legal, regulatory, audit, or tax advice. Readers are responsible
for obtaining such advice from their own legal counsel or other licensed professionals.
About Accenture
Accenture is a leading global professional services company, providing a broad range of services and
solutions in strategy, consulting, digital, technology and operations. Combining unmatched experience
and specialized skills across more than 40 industries and all business functions—underpinned by the
world’s largest delivery network—Accenture works at the intersection of business and technology to help
clients improve their performance and create sustainable value for their stakeholders. With more than
375,000 people serving clients in more than 120 countries, Accenture drives innovation to improve the
way the world works and lives. Visit us at www.accenture.com
Accenture, its logo, and High Performance Delivered are trademarks of Accenture.

Financial Services - New Approach to Data Management in the Digital Era

More Related Content

What's hot

Similar to Financial Services - New Approach to Data Management in the Digital Era

More from accenture

Recently uploaded

Financial Services - New Approach to Data Management in the Digital Era