Legacy blocks Should they stay or should they go? - Migrating legacy data - Accenture
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Legacy blocks Should they stay or should they go? Migrating legacy data
Data is the currency of today’s digital
economy, and it’s never been easier
and more secure to migrate legacy
data and unlock business value
Insurers know they need to modernize their 3. Data services and data integration
policy administration systems to grow their technology improve access to a wider variety of
businesses more profitably. So why would some data types, and the cloud provides the scale
insurers replace their administration system, yet businesses need to manage volumes of data.
leave policies on the legacy platform? Some
insurers lack the resource capacity or capability Migration is much more than moving volumes
in house. Others consider the costs and of policies. It's about reducing operational risk to
potential risks too great compared to the the business and clients. Therefore, it’s critical
potential return on investment. These were valid that the data work in the target system and
concerns, until now. across internal and external business systems.
There in lies the secret to successful data
New tools, technologies and delivery migration – a strategic enterprise data approach
approaches make it more cost effective than – one that delivers business value without
before to migrate legacy blocks with compromising business operations.
substantially less risk. They also unlock business
What follows is a proven approach, successfully
value from the data and provide valuable
applied by some of the largest U.S. life
insights that can drive product innovation,
insurance and annuity providers, to migrate
market opportunities and deliver a consistent
legacy policies.
consumer and agent experience across product
lines and channels.
Consider the following advancements
supporting the migration movement:
1. New iterative methodologies avoid
massive lengthy migrations; and instead,
break down migrations into smaller projects,
which can reduce risk and deliver
incremental value up front.
2. Source format-agnostic conversion tools
enable you to not only extract, but also
transform the data to work in the target
system. These tools use automated table
and data mapping and balancing, along
with data validation testing and cleansing
reports. They streamline the process and
ensure data integrity.
Source: Accenture Research Digital Decoupling survey of 1018
C-suite executives, July 2018
1
Proven migration framework
combines delivery expertise and
technology tools to minimize cost
and risk
A successful migration framework emphasizes data veracity throughout the migration process and
beyond. This is essential to not only minimize cost and risk, but also to deliver confidence in the
accuracy of your data wherever it’s used throughout your ecosystem.
Begin the migration project with a discovery phase to establish the scope and strategy for the
migration. Iterative data analysis, design, and development and test of migration rules by business
topic help address data issues early in the project, which lowers costs and condenses the project
timeline.
FIGURE 1: MIGRATION FRAMEWORK.
Discovery Anaylsis Development & Test
High-level
Migration analysis and plan
Deliverables
Cleansed source data
Migration rules and automated processes Rehearsal Deployment
Validation, test plans and reports
Converted data - masked/encrypted for
testing, unmasked for production migration
2
Organize for success
Evaluate your capacity and available resource After assessing the organization’s resources,
capability to keep the project on track and ensure establish a governance structure to ensure
uninterrupted service to your customers. It’s far teams (internal and external) are aligned on the
more efficient to staff appropriately than react to following:
a migration gone wrong, which could have a
significant impact, particularly on costs related to • Migration data within project scope and
intangible items such as brand reputation and where it resides
regulatory compliance.
• Steps to create and maintain the data
Conduct a gap analysis of your internal resources
• Understand how the data will be used and
to determine if you’ll need to bring in external
how it will work with other systems
expertise and/or capacity. When considering a
third-party engagement, vendors should provide Then develop the migration plan, keeping in
tiers of support based on your specific needs, as mind enterprise data management best
shown in figure 2. practices (figure 3).
FIGURE 2: SUPPLEMENT INTERNAL RESOURCES BASED ON THREE MIGRATION CATEGORIES.
1. Extract 2. Map & transform 3. Load
Complete system integration
Support to map, transform and load
Support to load
FIGURE 3: BEST PRACTICE FRAMEWORK ENSURES GOOD ENTERPRISE DATA HYGIENE.
Data Data
Creation Storage
Enterprise
Data
Management
Data Data
Usage Movement
Data Strategy
Data Data Data Data Data Data
Governance Management Quality Migration Architecture Security
Data Master Data Data Modeling/ Data
Organization Management Data Profiling Taxonomy Classification
Data Policies/ Metadata Data Storage/ Data Privacy/
Data Cleansing
Procedures Management Access Masking
Data Monitoring/ Data
Data Standards Data Retention/Archiving
Compliance Integration
Data Sustainment (information management office, data as a service...)
3
Plan for success
The goal of a migration plan is first and
foremost to minimize risk — whether risk
to your company’s reputation or
compliance risk — followed by seamless
data integration across your internal and
external business systems. Start with a
process that allows for flexibility and
automation where possible, to ensure
data accuracy without compromising the
project’s timeline. Consider the following
areas:
1. Process
2. Data Model and Standards
3. Data Cleansing and Testing
4. Technology Tools
4Plan for success
1. The Process 2. Data Model and Data Standards
Discovery and analysis provide a deep Data model and standards are driven by a sound
understanding of the source data—legacy enterprise data strategy, as illustrated in figure 3.
policies, product rules, data availability from one Together they ensure data quality during
or various sources, and quality—among other migration and validate that migrated data works
characteristics such as existing extracts and in the target system. Data quality and data
knowledge about legacy products and their migration rely on data profiling and cleansing.
data. The end-to-end migration approach
shown in figure 4 highlights two critical Figure 5 illustrates the extract, transform and
concepts, automated transformation and the load (ETL) process. Notice the transform area
configurable migration gateway, that ensure the applies business rules. This is a critical area of
extracted data is transformed to work in the new migration. It requires an understanding of
system. These source-agnostic conversion tools insurance industry best practices to not only
enable greater speed and flexibility to iteratively develop the rules, but also adapt them in real
test for: (1) issues within the conversion process time, based on validation testing. With industry
itself, and (2) issues with how the data behaves expertise, automation in this area can speed the
in the target system and ecosystem, including process, while ensuring data accuracy.
accounting, reporting and data warehousing,
among other systems.
FIGURE 4: ACCENTURE DATA MIGRATION APPROACH.
Transform
Legacy (Automated Transformation) Target Exception Balancing L4
Flat Files Flat Files Load Reports Online
Cleansing Reports
Validation & Data
Table and Data
Load Load
Data Audit
Extracts Mapping
Source Gateway Target
Encryption
Balancing L1
(UT/AT)
Balancing L3 Balancing L2 Balancing L2 Balancing L3
(DB to DB) (Flat Files to (Flat Files to (DB to DB)
Flat Files) Flat Files)
FIGURE 5: THE ETL PROCESS REQUIRES KEEN UNDERSTANDING OF INSURANCE INDUSTRY BEST PRACTICES.
data pro
lin
g
Manual or automated
data extraction from
source system
Extract
ng
nsi
ea
l
Technical
ac
dat
business migration
rules, based on
Transform Accenture’s
knowledge capital
data va and industry best
lida
ti practices
on
Pre and post
validation are key for
migration success
Load
53. Data Cleansing and Testing Continuous Testing
Apply a pragmatic four-component approach Continuous validation testing throughout
to streamline data cleansing without migration ensures data quality. It enables the
compromising data quality during business to assess data readiness and adjust
transformation, including: cleansing and rules on a real-time basis. This is
the underpinning of a successful go live.
1. Relevancy rules
Check data inconsistencies across the
whole enterprise by performing cross 4. Comprehensive Technology
checks between master and Toolbox
transactional data. The migration process relies on a comprehensive
set of tools and technologies, along with
2. Data standardization extensive insurance industry expertise to apply
Use standardized third-party data them effectively. Automation, where possible,
sources for addresses, postal codes and streamlines processes and further ensures data
country codes. veracity.
3. Data deduplication The combination of tools, technologies and
Enable the business user with a tool to expertise makes migration and the business
identify and manage duplicate records. value from the data more feasible than in the
past. More importantly, it provides an effective
4. Data construction
framework and process for the business to
Simplify how new data is handled by
continue to use and enhance its own internal
building it when the data doesn’t exist
data capabilities.
in the legacy system.
FIGURE 6: CONTINUOUS VALIDATION TESTING ENSURES DATA QUALITY THROUGHOUT THE MIGRATION PROCESS.
Extract Transform Load
Source Conversion Target
system environment system
Data Data Data
validation validation validation
6FIGURE 7: DATA MIGRATION TECHNOLOGY TOOLBOX.
Accenture Data Data Discovery Comparison Automated
Migration Tools and Profiling Testing Regression
& Technologies Technical Rules Source to staging Testing
Integrity Rules Staging to Target Worksoft-based
Business Rules Source to Target automated regression
Functional Reconciliation testing
Data Mapping Rules-Driven Data Privacy Variance Migration
Unification of data Data Cleansing Data migration routines Thresholds Management
elements - Shuffling Min Workbench
Splitting of data - Encryption Max
demands - Suppression Mean
Single data mapping Median
Standard Deviation
Count
Patterns
Formats
PAS Agnostic Meta Data Data Dashboards Data
Layer for Transformation and Analytics Archiving
Business Users Code Generator
Definition of elements Type conversion
Business rules Complex defaults
Data lineage Conditional validations
Data format rules
Grouping, sorting
7Putting it all Use the data migration framework to delve deep
into the composition of the data, ensuring data
together integrity while minimizing cost and risk throughout
migration. The framework outlines best practices
for establishing controls and audit processes
around the data. It identifies resources, including
subject matter experts, and proven tools that can
achieve the expected outcome and compress the
timeline.
FIGURE 8: MIGRATION FRAMEWORK.
Discovery Analysis Development & Test
High-level
Deliverables
Composition of the legacy policies Source extract - create flat file versions Data cleansing - cleanse in the source Migration analysis
- Variety of products, rules and of source system data system prior to extract (where possible) and plan
associated processes or as data is extracted or transformed
- Open and closed blocks Encryption and masking of PII - Cleansed source data
- Active and inactive (terminated) policies security protocols and steps taken to Transform - transform the source data
- Regulatory and contractual differences encrypt/mask predefined data using mappings of tables and fields, and Migration rules and
populating fields with default values; automated processes
Data availability - what systems have Souce data audit - analyze source requires defined business rules for
required data (aside from the PASs), system data to identify volumes and transformations or calculations Validation, test plans
and what critical information may be issues at the table and field level (e.g., and reports
available? errors or “missing” data) Load - loading transformed data into
ALIP Converted data -
Data quality - what errors exist, what Full functional understanding - what masked/encrypted for
needs to be fixed, what processes will it take to make data work in other Validation - conversion validation testing, unmasked for
currently exist to fix errors and omissions? systems? criteria at multiple levels: when running production migration
the data conversion programs, before
Existing extracts - are there existing loading ALIP, and after ALIP has been
extracts for source system data which loaded
are nonproprietary? Rehearsal
Functional testing support - providing
Integration to downstream systems - iterations of data, and researching and
how will data be used in the ecosystem? fixing defects while ALIP is tested with Deployment
converted data
Knowledge - are there experts and/or
documentation to explain the legacy End-to-end testing - in all relevant
products and available data? systems in the ecosystem
8Integrate with success
Up-front emphasis on planning and testing pays
dividends later. It not only unlocks legacy data,
but also ensures that it works within the source
system and integrates seamlessly across your
ecosystem. Additionally, the process, data
models, standards and tools can be re-used for
future data-related projects, including: one-time
ERP implementations; on-going data exchanges
between systems in production; organizational
changes from mergers, acquisitions and
divestitures; and any technical project where data
movement is a key component.
Source: Accenture Research Digital Decoupling survey
of 1018 C-suite executives, July 2018
Get more value from your data and
a modern policy administration
system
Don’t let extracts, business rules and processes Legacy blocks no longer need to remain a legacy.
become obstacles. Leverage your legacy data. Learn how Accenture’s experienced subject
Migrate it to benefit the customer, channel matter experts, tools, automated processes,
partners and your business. Use it to help cloud flexibility and database technology
advance next generation technologies including advances can help you migrate more feasibly.
artificial intelligence and machine learning that
rely on data to deliver a more personalized digital
consumer experience and more profitable
operation.
9About Accenture’s
insurance migration
practice
Conversion experts
80+
CONVERSIONS
PERFORMED
GLOBALLY
Increased Achieve
Visibility and Confidence Greater Cost Savings
60+
• Full audit trail, control and • Conversion accelerator tools MILLION
transparency • SaaS model option POLICIES
• Out-of-the-box metrics and • Global Delivery Centers CONVERTED
reporting that provide objective
insights
INDUSTRIALIZED
APPROACH
INSURANCE DATA
FOCUSED
END TO END
TRANSFORMATION 70+ INSURANCE
SPECIALISTS
10Further reading About Accenture
“Shift Left: An Iterative Approach to Software Accenture is a global professional services company with
Testing,”Michael Butrym, Accenture; Mitchel F. Ludwig, leading capabilities in digital, cloud and security.
Accenture Combining unmatched experience and specialized skills
across more than 40 industries, we offer Strategy and
Contact the authors Consulting, Interactive, Technology and Operations
Mitchel F. Ludwig services—all powered by the world’s largest network of
Managing Director Advanced Technology and Intelligent Operations centers.
mitchel.f.ludwig@accenture.com Our 500,000+ people deliver on the promise of
technology and human ingenuity every day, serving
Michael Perry clients in more than 120 countries. We embrace the
Application Development Manager
power of change to create value and shared success for
michael.perry@accenture.com
our clients, people, shareholders, partners and
communities. Visit us at www.accenture.com
Contact us
Nancy Bass
Accenture’s life and annuity software is part of Accenture
Sales and Client Management Lead
Life Insurance Services, within Accenture Financial
Accenture Life and Annuity Software
nancy.bass@accenture.com Services. By applying extensive industry knowledge
Or, visit www.accenture.com/lifeandannuitysoftware to continuously enhance its software, Accenture helps
insurers reduce operating costs, manage risk and
drive growth through improved product development
and distribution, enhanced policy administration and
distribution, and technology platform consolidation and
modernization. The homepage is
www.accenture.com/lifeandannuitysoftware.
Copyright © 2021 Accenture All
rights reserved.
Accenture, its logo, and
High Performance Delivered are
trademarks of Accenture.You can also read
