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A New Approach to
Data Management
in the Digital Era
September 2016
• Digital agenda
• Multichannel integration
• Customer centricity and
Customer experience
management
• New products – conn...
Risk and regulatory managementEnhanced productivity and efficiency
Discovery of new business opportunitiesData-driven deci...
Copyright © 2016 Accenture. All rights reserved. 4
Data
Governance
Data
Architecture
Data
Management
Data
Conversion
Data
...
Copyright © 2015 Accenture All rights reserved. 5
The goal of data governance is to
deliver comprehensive, complete,
corre...
Copyright © 2015 Accenture All rights reserved. 6
Data
Governance
• Complying with internal
guidelines and responding to
i...
Copyright © 2015 Accenture All rights reserved. 7
Data Governance Suite of Services Value Proposition
Top Challenges
Accen...
Copyright © 2015 Accenture All rights reserved. 8
Cost-effectiveness: Significant reduction in development effort or licen...
Copyright © 2015 Accenture All rights reserved. 9
Accenture Data Quality
Tool with “Collibra NV”
Accenture has developed a...
Copyright © 2015 Accenture All rights reserved. 10
Why
Data Lineage: Traceability of data elements from the original entry...
Copyright © 2016 Accenture. All rights reserved. 11
Differences, challenges and consequences
Big data analytics initiative...
Copyright © 2015 Accenture All rights reserved. 12
During metadata gathering stage, functional and technical objects are
d...
Perspectives on data protection
A well-established data governance connects legal to technology by translating data protec...
Road to rapid analytics implementation from a data protection (DP) perspective
Data protection process from assessment to ...
Copyright © 2015 Accenture All rights reserved. 15
Monitoring
• Monitoring of data quality
• Identification of data qualit...
Copyright © 2015 Accenture All rights reserved. 16
New data quality tool should be considered in response to fast-changing...
A New Approach to
Data Management
in the Digital Era
17
Disclaimer:
This presentation is intended for general informationa...
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Financial Services - New Approach to Data Management in the Digital Era

How current is your data management strategy? As technology—and the requirements and business drivers around it—changes, financial services firms will need to change their approach to data management. To guide your approach, see the three building blocks to Accenture’s data management framework covered in this presentation.

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

  1. 1. A New Approach to Data Management in the Digital Era September 2016
  2. 2. • 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
  3. 3. 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
  4. 4. Copyright © 2016 Accenture. All rights reserved. 4 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
  5. 5. 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 Copyright © 2016 Accenture. All rights reserved.
  6. 6. Copyright © 2015 Accenture All rights reserved. 6 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 Copyright © 2016 Accenture. All rights reserved.
  7. 7. Copyright © 2015 Accenture All rights reserved. 7 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 Copyright © 2016 Accenture. All rights reserved. CoreFeatures
  8. 8. Copyright © 2015 Accenture All rights reserved. 8 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 Copyright © 2016 Accenture. All rights reserved.
  9. 9. Copyright © 2015 Accenture All rights reserved. 9 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 Copyright © 2016 Accenture. All rights reserved.
  10. 10. Copyright © 2015 Accenture All rights reserved. 10 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 Copyright © 2016 Accenture. All rights reserved.
  11. 11. Copyright © 2016 Accenture. All rights reserved. 11 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
  12. 12. Copyright © 2015 Accenture All rights reserved. 12 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 Copyright © 2016 Accenture. All rights reserved.
  13. 13. 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 Copyright © 2016 Accenture. All rights reserved. 13 Technology
  14. 14. 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.
  15. 15. Copyright © 2015 Accenture All rights reserved. 15 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 Copyright © 2016 Accenture. All rights reserved.
  16. 16. Copyright © 2015 Accenture All rights reserved. 16 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 Copyright © 2016 Accenture. All rights reserved.
  17. 17. 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. Copyright © 2016 Accenture. All rights reserved.

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