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

A New Approach to Data Management in the Digital EraSeptember 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

NewData

Management

Regulatory Drivers Business Drivers

Risk and regulatory managementEnhanced productivity and efficiency

Discovery of new business opportunities Data-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

InsightsDiscovery

Data Monetizatio

nStrategic Decisions

Investment Choices

Process Automation

Low Storage Costs

High Scalability

CFO and CRO

IntegrationReal-time

SimulationsRegulatory Reporting


DataGovernance

Data Architecture

Data Management

Data Conversion

DataSecurity

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

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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 processesbased 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

Data

qua

lity

man

agem

ent

Met

adat

a m

anag

emen

t

Func

tiona

l dat

a ar

chite

ctur

e

Data Management

Tech

nica

l dat

a ar

chite

ctur

e

Data

pro

tecti

on

an

d se

curit

y

Stor

age

and

arch

ivin

g

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 issuesin 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 taxonomyfree of contradictions

• Reconcilability within risk data and to financial data using consistent storage and definition of data

• Groupwide clear and completeassignment of responsibility for data

Reductionof complexity

Compliance with regulatory requirements

TransparencyImproving

efficiency

Data governance usesData 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

Acce

lera

tor F

eatu

res

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 dataAccenture Data Governance Suite of Services


Core

Fea

ture

s


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 valueBenefits 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 availabilityThe “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 treeFunctional level

Data lineageTechnical 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

Key

Achi

evem

ents

• 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

DPAwareness Cloud

DP Approval

DemoSign-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

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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 aspectsData 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

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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

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