CDO Slides: Real World Data Strategy Success Stories
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A common question from upper management is “Does this really work? Can you show me where there has been success?” Well, the answer is “Yes, this works.” Join John and Kelle for a review of Data Strategy success stories. We will review success stories for data governance, data quality, and other types of data. Some successes we will examine are: - Standing up data governance in difficult cultures - EIM programs that created value for the organization - Several small case studies of organizations that have had success in DQ, Analytics, and MDM
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CDO Slides: Real World Data Strategy Success Stories
- 1. The First Step in Information Management www.firstsanfranciscopartners.com Produced by: Real World Data Strategy Success Stories Monthly CDO Webinar Series Brought to you in partnership with: #CDOVision November 3, 2016
- 2. Agenda § What is success? § EIM strategy success stories § Reporting/data-centric execution success story § Data governance success story § Governance and MDM success stories Sponsored by: © 2016 First San Francisco Partners www.firstsanfranciscopartners.com pg 2
- 3. What is success? § Define “done” § Potential benefits that apply across EIM − Raising visibility of data to an organization − Pulling vision together − Become more data-centric − Improving efficiency and productivity − Reduced costs of IT, processes, data, etc. − Improved cost and delivery of projects pg 3© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Sponsored by:
- 5. Challenge: Large energy company realized cost of data mismanagement 40 million in SAP development Massive data clean-up Upheaval across organization No trust in data Still no data consistency or reliability Costly denial of rate increase Inability to adapt to smart grid evolution Sponsored by: pg 5© 2016 First San Francisco Partners www.firstsanfranciscopartners.com
- 6. Solution: Data-centric enterprise model – data management/governance engagement Senior Executives All Business Management Customers Future Users All Business Area Staff Vendor Product Management All DBAs External Data Suppliers Executive Sponsorship Senior IT Management Senior Business Management Support Desk All Intended Users Application Development IT Operations Management Vendor Account Management Vendor Product Support PMO Key Testing Users Training Development & Execution Source System Technical Application Admin Business Data Subject Experts Dashboard Designers and Developers Project Management Information Architects Data Stewards Data Warehouse Architect Business Unit Information Managers Core Team à Extended Team à Involved Individuals à Support Dashed lines indicate direct engagement of Data Governance Ad hoc Query- Builders Stewards and Owners pg 6© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Sponsored by:
- 7. Outcome: Flexible and adaptive architecture App1 App2 App3 App4 App5 App6 App7 Appn.. Apps Physical Databases Current Physical Views SAP Other Reverse Engineering Forward Engineering Abstraction Processes “Mapping” Views Meta Data Layer Semantics Measures Data Assets Work Flow Rules Future Logical Views Future Physical DBMS Future Apps Access layer - DW,ODS, Web Services, Analytics, etc. 1 2 3 1 2 3 31 2 Data Management Synchronize to other data 3 2 1 pg 7© 2016 First San Francisco Partners Sponsored by:
- 8. Results and takeaways § Results − Reorganization around data − Improvement in operational application use − Improvement in data quality for reporting − Deployment of change teams § Takeaways − Non-standard operating models are OK − Improved data quality is key to application usage − Middle management embraced changes − Solution architecture can be a hybrid of old and new Sponsored by: pg 8© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Lessons learned…
- 10. Challenge: Large specialty retailer faced with huge operational issues Multiple product data stores Inconsistent merchandising Destructive data redundancy Ancient applications and infrastructure No trust in data Unreliable inventory Less agile in their market Stock price explicitly affected pg 10© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Sponsored by:
- 11. Message Processing Layer Data Access Layer (DAL) Publish/ Subscribe Request/ Reply Message Queuing Bulk/ Batch Publish/ Subscribe Request/ Reply Message Queuing Bulk/ Batch ODBC Applications Portfolio Application Application End Point-to-Point Interfaces LOCAL DATA STORE Application Application Application LOCAL DATA STORE Enterprise Data Stores (Physical and Logical) ODS/DW Transaction Data Operational and Analytical Data ANALYTICAL DATA TRANSFORMATION DM Activity Product Customer Store Enterprise Data Stores populated over time to establish Single Version of the Truth Application Services Technology Services Data Services Canonical Model Metadata Repository EIS CUBE Solution: Full reload of data architecture Sponsored by: pg 11
- 12. Analytics Reporting Transaction processing Data capture Merchandising/SupplyChain Distribution/Manufacturing NewBusinessDevelopment StoreOps/Sales Marketing/BrandMgmt Accounting/Financials RadioShack.com Systemsupport ENTERPRISE ARCHITECTURE DIRECTION Application “stovepipes” aligned with functional organizations Enterprise Architecture Flexible application layers mapped to major business processes • Applications aligned with core business processes • Flexible reporting and analytics respond to business changes • Reuse of common business functions simplifies creation and maintenance of applications • Build analytical capability across enterprise • Data-centric focus enables learning organization Data capture Transaction Processing Enterprise Data Stores Reporting Analytics Logical Physical • Applications are rigid and do not easily provide cross-enterprise functions or information-centric views • Data capture and transaction processing capabilities do not promote speed • Reporting and analytic capabilities are not flexible enough Core processes App App App App Solution: Followed enterprise architecture pg 12© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Sponsored by:
- 13. Results and takeaways § Results − Creation of a top data job (TDJ) − Implementation of information management area − Refresh of data warehouse and reporting technologies − Created change teams § Generated internal accountability for successful change § Takeaways − Engage enterprise architects in a solution − Engage with executive team and consider a TDJ pg 13© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Lessons learned… Sponsored by:
- 15. Challenge: Reporting project Data is stored as a byproduct of the applications that capture the data, not in a way that is accessible to the business user Data descriptions are not centrally defined resulting in inconsistent usage and understanding of the data Data transformations are complex and only understood by IT resource Formal data quality monitoring practices do not exist Lack of business ownership Lack of trust in data Inability to do analytics Business is forced to rely only on gut and intuition vs. making informed, fact-based decisions Low level of data management maturity pg 15© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Sponsored by:
- 16. Outcome: Reusable, subject-specific data marts Source Application Database Abstraction Layer (inc. DQ & ETL) Business View of Data Business View of Data Business View of Data Business View of Data Business View of Data Today: Data is stored as a byproduct of the application vs. Future: Data is stored in a way that is optimized for business retrieval § Data strategy execution is aligned to project engagement § Reporting project provides an opportunity to improve the way that data requirements are captured and source data is analyzed pg 16© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Sponsored by:
- 17. Results and takeaways § Results − Fact-based decision-making − Improved data analytics capabilities (advanced and predictive analysis) − Business ownership of information assets § Takeaways − Implementing new data standards and guidelines via a project grounds governance in reality − Adjusting an “SDLC” to be data-centric catches potential data discrepancies before they become issues − Incremental change is easier – especially when aligned with a tangible outcome pg 17© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Lessons learned… Sponsored by:
- 19. Goal: Improved coordination of enterprise-wide data governance Business Problems • Business decisions are being made on unreliable data sources due to lack of understanding of data definitions, calculations and lineage • Data governance is centered around discrete business lines and not across the enterprise • Business and IT have no common understanding of data sources and their usage, resulting in increased effort to verify and remediate Desired Business Outcomes • Delivering the right data across different levels of the organization through the consistent execution of defined processes spanning the complete data life cycle • A baseline of data governance practices, process and metrics implemented across all business lines • A discrete set of Core Data Concepts governed through a federated approach • A roadmap of improvement initiatives that will drive continuous business improvement pg 19© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Sponsored by:
- 20. Solution: Approved data governance accountability Steering Committee Data Governance Council (DGC) CRM Reporting ERP Enterprise Department – Local Data Governance (LDG) Example Projects/ Applications PMO Enterprise Architecture, Architects Projects and Programs Existing Structures Metadata 1% at the SC Escalation/Resolution 19% at the DGC 80% at the LDG pg 20© 2016 First San Francisco Partners www.firstsanfranciscopartners.com OPS SERV SALES HR FIN OPS DG WORKING GROUP FIN DG WORKING GROUP SERV DG WORKING GROUP SALES DG WORKING GROUP HR DG WORKING GROUP Sponsored by:
- 21. Outcome: Aligned data governance roadmap and actions pg 21 Sponsored by: Data Concepts Data Handing Standards and Processes Oversight 2016 2017 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Business Benefit Realized Projects Process with Projects : concept and infomap engagement refined BOR evaluation process engaged DG Metrics integrated with project process (estimated) Modify SDLC process for integration with solution planning gates for DG and PMO oversight DG Program Operations Change Management Activities Refine processes/Release 1 Refine and Release 2 Refine and Release 3 Release n Concepts and Metadata standard (InfoMap) BOR process standard DG Metrics standard Other high-interest topics Launch DG practices with Aligned LDG Roadmaps Refine Metrics Metrics program implementation Refine Architecture with DG involvement with projects Privacy and Security review Potential DGC or LDG focus Implement Rotating DGC Chair Metrics program operations Align Local Data Governance to standard roles, processes, and roadmaps
- 22. Results and takeaways § Results − Rolled out the concept of a minimum viable state of governance within each division − Defined a set of critical data concepts where previously couldn’t agree − Enabled improved data sharing and consistency § Takeaways − A minimum viable state is still progress − Create a flexible, culturally-tuned operating model to ensure it is adopted – a referee body can be acceptable vs. a centralized authority − Focus on high-priority areas to make incremental progress pg 22© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Lessons learned… Sponsored by:
- 24. Challenge: taking a business- driven approach to MDM Align MDM Requirements to Business Requirements Assess Data Align Data Governance pg 24© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Sponsored by:
- 25. Solution: Roadmap prioritization based on use cases Value Effort HighLow Med High Low Med 1 17 16 15 14 13 12 11 10 9 8 7 6 54 32 MDM Use Cases: 1.Unique Global Identifier 2.3600 View 3.Customer Groupings 4.Customer Reference Data Management 5.Data Quality at point of entry 6.Data Harmonization 7.Centralized Stewardship 8.Centralized Onboarding Repository 9.Master Data Enrichment 10. Customer Documentation Linkage 11. Event Based Data Remediation 12. Mastering Privacy & Preference 13. Security & Access Management 14. Mastering Customer/Account Relationships 15. Data Quality Dashboards 16. Audit & History 17. Early Warning System Client-centric strategic initiatives, such as the Consumer Online effort and business use cases that were documented as an outcome of the requirements gathering phase, formed the foundation for the layout and scheduling of the roadmap. pg 25© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Sponsored by:
- 26. Roadmap: Integrated MDM evolution Enterprise MDM will deliver incremental value to the business – first in support of In-Flight Programs such as Consumer Online, and then expanding to incrementally deliver on the program objectives and requirements across the enterprise. Deploy Build Enhance Pilot § To engage and confirm the decision makers able to define customer data attributes and processes § Confirm existing definitions and decisions regarding customer data § Install INFA MDM in non-production environments (NP2/NP3) § Develop foundational customer master data model § Onboard deposits, lending (consumer) & FSC customer SORs on to MDM § Develop foundational data quality & de-duplication rules § Develop core MDM web services to support consumer online development § Continuous improvement to mastering processes, assess effectiveness of rules, measure improvements and extend across organization § Augment MDM to enable capture of customer privacy & preference § Onboard remaining SORs – FRIM, FX § Develop ETL for IDL and delta for additional sources § Finalize data quality & de-duplication rules § Configure hierarchy manager (HM) to support customer/account relationship management § Augment MDM to enable capture of existing Householding relationships (tax ID & mega- household) § Refine data stewardship tools & capabilities (IDD) § Enhance MDM web services to deliver full CrUD capabilities and conform to augmented data model § Develop outbound data feed for downstream consumption § Execute system integration testing § Execute UAT § Deploy to Production § Create foundation for centralized master data creation § Align business processes to consume master data § Manage data quality at the source § Augment MDM to capture broader & other flavors of householding relationships § Augment HM to support householding § Implement business rules & ETL to derive and populate Householding relationships § Leverage BPM to develop data stewardship workflows § Augment stewardship capabilities (IDD) § Commence harmonizing mastered customer data with SORs § Determine MDM rules, create foundational processes and identify initial measurements § Identify and define master relationships § Expand foundational data model to include all customer demographic and relation attributes § Onboard additional sources – Lending (Commercial) & FRTC § Develop ETL for IDL and delta for all onboarded sources § Deliver stewardship capabilities (IDD) for match resolution, centralized data maintenance & DQ dashboard § Enhance data quality & de-duplication rules § Build foundation for relationship management Q1 Q2 Q3 Q4 Use Cases 1 17 16 15 14 13 12 1110 9 8 7 6 54 3 2 24 9 13 1316 15 16 Change Management, Communication , Training and Awareness Sponsored by:
- 27. Results and takeaways EDCI Group Operational/Execution Group Data Steward Data Governance Council (Business Data Owner/Data Governance Lead) DO - Customer DO - Products DO - Finance Enterprise Data Architect Chief Data Officer/ Executive Sponsor Group/ Individual IndividualRoles Operating People/Process Data Custodian Data Users Illustrative MDM#Design#Team# MDM#Opera/ons#Team# Business#SMEs# Master#Data# Stewards# IT#MDM#Opera/ons# Team# MDM#Architect# MDM#Delivery# pg 27© 2016 First San Francisco Partners www.firstsanfranciscopartners.com § Results − Business accountability for master data aligns expectations and outcomes to deliver business value − Prioritized use cases based on business needs ensures master data usage and value § Takeaway − Mastering data is a business process and should be treated as such Lessons learned Sponsored by:
- 29. Challenge: Support business growth through MDM and DG Revenue Growth • Small increase in cross-sell/up-sell success results in appreciable increase in overall premium volume Profitability • Account view improves decision- making and pricing ability Improved Customer Experience • Improve Net Promoter Scores and customer retention Improved Customer Knowledge • Creation of 360o view of Customer to aid analytics Regulatory Compliance • Consistent application of data classification standards across organization Employee Satisfaction • Enabling employees to be more effective and efficient - increases satisfaction pg 29 Sponsored by: © 2016 First San Francisco Partners www.firstsanfranciscopartners.com
- 30. Solution: Incremental value through MDM Roadmap Tactical Operations Strategic Operations Operational Analytics Predictive Analytics Improved Capabilities Time pg 30© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Sponsored by:
- 31. Today Year 1 Year 2 Year 3 Year 4 Future Today Build Complete Operationalize Complete Prepare Complete Approval PMO Process Prepare Prep Hub Data Quality (DQ) Data Governance (DG) Build Personal Lines Flood Commercial Outbound Integration with DW Implement & Integrate CRM Operationalize Establish DQ metrics DQ Mgmt for Party Establish DG metrics Data Stewardship for Party Establish MDM Metrics Monitor Metrics Change Leadership, Training & Communication Outcome: Business-aligned, long-term plan pg 31© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Sponsored by:
- 32. Results and takeaways § Results − Alignment with a strategic initiative ensured funding and business involvement − Iterative implementation ensured the pace reflected the available funding − Clear goals and expectations rallied stakeholders § Takeaways − MDM in isolation creates a challenge; aligning to a strategic effort is a good approach − Constraints will exist, plan accordingly − Follow the 5-Step process to align a data-driven culture § Vision à Purpose à Picture à Plan à Participation pg 32© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Lessons learned… Sponsored by:
- 33. Questions pg 33© 2016 First San Francisco Partners www.firstsanfranciscopartners.com