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Overcoming the Challenges of your Master Data Management Journey

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This Presentaion runs you through all the key steps of an MDM initiative. It considers and showcase the key milestones and building blocks that you will have to roll-out to make your MDM
journey
-> Please contact Talend for a dedicated interactive sessions with a storyboard by customer domain

Veröffentlicht in: Technologie

Overcoming the Challenges of your Master Data Management Journey

  1. 1. © Talend 2014 1 Workshop: Overcoming the Five Challenges of your MDM journey Presented by: Didier Josephine & Jean-Michel Franco
  2. 2. © Talend 2014 2 Your Interlocutors Jean-Michel Franco Director, Product Marketing Didier Joséphine Sales Engineer, MDM expert Key Facts about Talend •Founded in 2006 •400 employees in 7 countries •Highly scalable integration solutions addressing Big Data, Application Integration, Data Integration, Data Quality, MDM, BPM •Dual HQ in Los Altos, CA and Paris, France •Open Core business model •Subscription license •Services & training 2007 2008 2009 2010 2011 2012 2013
  3. 3. © Talend 2014 3 OVERCOMING THE FIVE CHALLENGES OF YOUR MDM JOURNEY Master Data Management 101 The five challenges to deliver on the promises of MDM - Modeling Agility: creating the single version of the truth - Data Accuracy: managing the data quality - Lines of Business Accountability: establishing data stewardship - Data Accessibility: Connecting enterprise sources and beyond - Master Data Actionability: connecting to processes, real time Outlook and future trends Wrap-up
  4. 4. © Talend 2014 4 Master Data Management is a cornerstone for data-driven processes Know Your Customer Know Your Products Know Your Suppliers
  5. 5. © Talend 2014 5 Talend MDM Customers Suppliers Products Assets Agencies Stores Organiza- tions and Reference Data Employees MDM is about creating and managing the golden records of your business What ? (44%) Who ? (33%) How ? (21%) Where ? (3%) Number sources : Gartner
  6. 6. © Talend 2014 6 Definition Master data management (MDM) is the process of creating a single point of reference for highly shared types of data, including customer, products, suppliers, sites, organizations and employees. Master data management requires companies to create a single view of their shared master data asset. It then links together multiple data sources, and ensures the enforcement of policies for accessing and updating the master data, handling data quality and the routing of exceptions to people. This “Data Stewardship” capability allows the lines of businesses to take ownership of the content they need for their data centric processes. Once a single view is created, that data can be operationally applied, and eventually in real-time, to business problems and opportunities. MDM is a strategic initiative for data-driven organization seeking to improve business results such as better customer experience and service, increasing cross-sell and up-sell revenue, and streamlining supply chains.
  7. 7. © Talend 2014 8 The journey from Data Integration to Information Governance From a fully IT driven model… …to a federated and collaborative responsibility model IT Lines of Business Evolution path From Data Management… …to Information Governance
  8. 8. © Talend 2014 9 The Business cases for MDM M&A and restructuring 0101010110101010101010101011010101010101010101010101010101010101011010101010101010101010110101010101010101011010101010101011010101010101010101011010101010101 360° Views Managed Data Accuracy Collaborative Data Governance Information Accessibility Information Accountability MDM Platform Governance, Risk Compliance and fraud mgmt. Just-in-time and lean operations Customer centric processes Customer Experience Management Time to market
  9. 9. © Talend 2014 10 MDM : why change? why now? And how ? Source : Gartner 2014 survey Enterprise Information and MDM MDM is a hot topic •in top 3 initiative for 50% of IT execs There is a urgent need to refresh current processes linked to master data •Ratings of the current capability: 3,6 on 7 ; average for 79%; poor for 21% A lot of companies have engaged, but most are at early steps •61% still on planning/prototyping phases Only 49% have a clear business case •and 31% through an ROI model
  10. 10. © Talend 2014 11 Why MDM ? https://info.talend.com/tdwinextgen.html
  11. 11. © Talend 2014 12 So Where to start your journey to data governance ? Define your business needs and your roadmap Set up your stewardship organization Design the platform Engage your MDM programs
  12. 12. © Talend 2014 13 Turning MDM from a discipline to a program “The biggest observed change entails a shift from organizations viewing MDM as an abstract discipline to treating it a tangible program. The successful organizations exhibit the later” Bill O Kane Discipline Program Vision What can be done What we will do Goals Monolithic and long term Incremental and Time- Phased Metrics General Specific to each project/process Governance What is quality data How to fix it Organization Data Stewardship Accountability and leadership Technology Keeping the golden records Promoting collaboration and communication Sources : Gartner maturity model and MDM presentations
  13. 13. © Talend 2014 14 Organizing for MDM : best practices 1. State the problem you're trying to address. 2.Determine the project's mission and business value, and link the initiative to actionable insights. 3.Devise a good IT strategy. 4.Business users must take full ownership of the master data initiative. 5.Align success criteria for MDM across the organisational chart 6.Pay attention to organizational governance and change management. 7.Develop Master Data Services for Application Integration 8.Map business needs to technology acquisition https://info.talend.com/mdmlisttdwi.html http://www.informationweek.com/big- data/big-data-analytics/7-master-data- management-project-best-practices/d/d- id/1107222
  14. 14. © Talend 2014 17 OVERCOMING THE FIVE CHALLENGES OF YOUR MDM JOURNEY Master Data Management 101 The five challenges to deliver on the promises of MDM - Modeling Agility: creating the single version of the truth - Data Accuracy: managing the data quality - Data Accessibility: Connecting enterprise sources and beyond - Lines of Business Accountability: establishing data stewardship - Master Data Actionability: connecting to processes and application, real time Outlook and future trends Wrap-up
  15. 15. © Talend 2014 18 0101010110101010101010101011010101010101010101010101010101010101011010101010101010101010110101010101010101011010101010101011010101010101010101011010101010101 Key objectives for successful MDM design Modeling Agility Data Accuracy Data steward- ship Data Integration Data actionability •Unified views •Embedded Rules and Controls •Role based access •Creating master data services •Connecting to systems, real time •Profiling for new data sources •Standardization & matching •Quality analytics and control •Authoring and user interfaces •Tasks management & resolution •Workflows and BPM •Integrating and cross referencing internal systems •Augmenting with external data MDM
  16. 16. © Talend 2014 19 Modeling your data Key steps to consider •Creating the data model •Defining the business rules •Defining Data Validation controls •Defining the roles , and the security Modeling Managing the data quality Enabling stewardship Integrating & propagating the data Operationalizing the master data
  17. 17. © Talend 2014 20 Organizing for MDM: Defining the implementation Style MDM ERP CRM COTS DWH Consolidation MDM ERP SFA CRM DWH Centralized MDM CRM E- Commerce Marketing DWH Coexistence MDM ERP SFA CRM DWH Registry Less Intrusive Most MDM Configuration Most ESB Configuration Less Intrusive Standard MDM Configuration More Intrusive Standard MDM Configuration Optional ESB Configuration Most Intrusive Moderate MDM Configuration Required ESB Configuration
  18. 18. © Talend 2014 21 Modeling best practices Functional Engage heavily the LOBs in the designing effort Reach consensus ASAP on the data definition of golden record Start at the core and keep it simple, then expand Make the model as self explanatory as possible for the business users, and document your business glossary Create your own primary key Manage the design and validation phase carefully, as changing a data model at run time once the data is populated may be a tedious exercise Leverage views and roles for usability Value: ➜Establish sustainable foundations for your MDM model ➜Establish the cornerstone for collaboration (Stewardship and IT integration) Technical Create an internal permanent key for Master Data records Define modeling standards and respect them Use a graphic Case tool for the design Establish naming rules Reuse definition, rules and patterns Anticipate the performance impact of controls, enrichment and propagation rules
  19. 19. © Talend 2014 22 Managing the Data Quality Key steps to consider •Data Profiling •Collect the referential to enriching the data •Defining parsing, standardization, validation •Defining the matching and survivorship •Building Address validation rules Modeling Managing the data quality Enable stewardship Integrating & propagating the data Operationalizing the master data
  20. 20. © Talend 2014 23 Use case: Growing the Business With an Extended Product Portfolio Challenge: •Extend the direct supply catalog with a long tail through an online marketplace with millions items •Delegate administrative task related to product introduction to supplier through a self-service portal Key capabilities needed : •Close the gap between the back end application (supplier self service) and the existing front end (Customer facing MDM for product data) •Data quality and stewardship •Data and application Integration Value: •Increased revenue through better exposure of features, benefits and reviews •Streamlined product on-boarding
  21. 21. © Talend 2014 24 Data Quality best practices Functional Know your data before starting the design: content, availability volume, typology, reliability, reference data Understand the information supply chain: who creates, imports, update, consumes (and when/where…) Establish strong collaboration with stewards in charge of manual resolution to fine tune your matching algorithms iteratively Define business and project metrics to be monitored over time, in order to size the data stewardship efforts and to show the progress Value: ➜Illuminate the data quality problems and its impact for lines of business ➜Establish clear metrics for measuring the progress and success of the MDM program Technical Use a data profiling tool Integrate the data quality rules as gatekeepers in your data integration process Understand the constraints and objective that are behind the matching policies, including performance, impact of mismatches, cost of manual efforts… Anticipate the need for adjustments, including for undoing redoing data resolution activities
  22. 22. © Talend 2014 25 Synchronizing with the existing systems in batch or real time Key steps to consider •Batch/real time, Bulk or incremental load, propagation : defining the integration policies •Integrating with applications: internal, cloud based, external Modeling Managing the Data Quality Enable stewardship Integrating & propagating the data Operationalizing the master data
  23. 23. © Talend 2014 26 Challenge: Support hyper growth of members in a non profit and highly regulated healthcare market Re-engineering customer facing processes Use case: Re-engineering member relationship in a heavily regulated environment Key capabilities need: Start with strong Data quality and data reconciliation capabilities Manage external data standards and connect in real time with exchanges in the healthcare industry Implement workflow driven processes for customer facing activities (on-boarding, claims, billing…) Value: •Compliance (with HIPAA regulations) •Scalable processes to meet hyper growth (+250% members acquisition rate) •Lower TCO and automated processing
  24. 24. © Talend 2014 27 Integration best practices Functional Define the integration architecture and the decision criteria to inform data integration scenarios for each source and targets Design the integration layer as a moving object that will have to evolve on a regular basis, with its own lifecycle (new systems to connect, upgrades…) Use design mechanisms like publish and subscribe or Master data services to avoid dependencies between system and have clear segregation of duties Value: ➜A shared service to bring trusted data across your IT trough a well defined and rapid to deploy process ➜Manage change info your MDM program and take advantage into new sources of data and accelerate the roll-out of new applications Technical Invest on productivity and change management tools, since this makes a substantial part of your TCO Identify the volume now…and for the future Identify the MDM multiple environments Define procedures for Delivery between environments Integration ServicesData StagingMetaDataRepositoryWeb LayerHybrisTCP/IP - KereberosLegendCustomer Data Management – Static ArchitectureIntegration ServicesBatchAdaptorsReal-timeAdaptorsReal time data servicesFile basedMasterRepository@ComResACDSPegaTracsVisionData Quality ServicesTalend Integration PlatformParsing& enrichment(Experian) MatchingServicesBatch data servicesData LayerMaster Data GovernanceTalendAdministrationData QualityDashboardMigrationAdaptorsStandardisation Services Integration Layer ActiveDirectorySOAP over JMSGetCustomerDetailsCoreGeCustomerinteractionsCreateCustomerUpdateCustomerPublishCustomerGetCustomerEngagementsGetCustomerProfileSearchCustomerMatchCustomerPublishCustomerMerge Integration Layer MatchCustomerBulkSOAP over HttpTalend ESB
  25. 25. © Talend 2014 28 Engage your Lines of Businesses Key steps to consider •Organize data stewardship tasks by roles •Managing the day to day tasks related to master data •Accessing and authoring the master data •Defining the workflows for collaborative authoring Modeling Managing the Data Quality Enable stewardship Operationalize the master data Operationalize the master data
  26. 26. © Talend 2014 29 Use case: Monetizing content and increasing ARPU in the media industry Challenge: •Manage 28,000 hours of multimedia content delivered monthly from 340 content providers to 75 million households Value: •Increase ARPU (Average Revenue Per User) and improved customer experience with data to promote the movies •Decreased costs and time for adding new content to the movie catalog Key capabilities needed : •Start with Data Integration and data quality to deliver quickly an improved centralized catalog •Progressively replace a non intrusive a posteriori process to reconcile data and manage errors with a reengineered collaborative process driven by workflows
  27. 27. © Talend 2014 30 Best practices for Data Stewardship Functional Define and document the data governance policies (incl inventories roles, permissions, workflows) Make sure that the lines of businesses are engaged and accountable Define clear roles & tasks for data stewards and define their working environment and workflows accordingly ; Engage the data stewards early in the project, well before the training and roll-out phase Value: ➜Engage the lines of business in the success of data centric initiatives ➜Organize for a MDM roll-out and continuous improvement Technical Integrate the people driven tasks related to data authoring, validation and correction into the overall landscape, rather than as a separate flow Target the right environment for the right roles (designers, data stewards, authors and contributors, end users)
  28. 28. © Talend 2014 31 To BPM or not to BPM ? Functional ➜Clearly identify the actors ➜Nominate champions for roles and involve them in the project to define the processes and activities ➜Use agile methodologies to define the workflows and interfaces ➜Carefully design the users interface ➜Leverage Business Activity Management for alerts and continuous improvement When to use BPM in MDM projects ? MDM has the lead for data authoring Lines of businesses are highly engaged Business users are involved in the authoring process -> need for guided procedures There are clear links between MDM and business processes (e.g.: onboarding a customer/employee, referencing a product…). Technical Use a BPM tool that can go beyond pure MDM authoring capabilities Keep it simple and anticipate frequent change since people centric processes are subject change and to deal with exception much more frequently that automated processes Don’t underestimate efforts and time related to the user interface Value: •Re-engineer your processes with a data centric approach
  29. 29. © Talend 2014 32 Making MDM actionable Key Capabilities •Integrate Master Data Services real time into processes •Bring context into applications such as Big Data, web or Mobile Applications Modeling Managing the Data Quality Enable stewardship Integrating & propagating the data Operationalizing the master data
  30. 30. © Talend 2014 33 Best practices for Operationalizing the Master data Functional Identify the touch points where you need to integrate MDM data services, and prioritize the roll out interactively. Define metrics to show the business impact, e.g. on transformation rates, click rates… Understand the performance and availability impact of invoking MDM real time for the external applications Define a small set of reusable, well documented master data services Connect your master data to your Big Data via Entity Resolution to boost the relevance of your bog data analytics Value: ➜360 view are populated at the right time, right place, when insights or actions are needed. Technical Closely integrate this capability into your existing enterprise service bus capability Define Service level agreements for the MDM services and monitor them closely Create sets of tests cases to industrialize and automate the testing capabilities MDM Business Applications Mobile Applications Big Data Web applications
  31. 31. © Talend 2014 34 Use Case Bring Actionable Customer Data across Touch Points in Travel & Transportation Challenge: Drive loyalty and customer retention in an industry disrupted by digital transformation Key capability needed: •Fast & easy collection, cleansing and reconciling of data for 15 million customers •Definition of Master data services to bring customer context and progressive delivery across touch points in a real time mode Value: ➜Improved marketing, sales and service through knowledge and personalization ➜Better transformation rates, cross sell/upsell ➜Multi-Channel consistent Customer Experience
  32. 32. © Talend 2014 35 Example in CRM: the customer data platform Multiple customer touch point, many innovative offers, but broken customer journeys Customer Data Platform
  33. 33. © Talend 2014 36 Building the « customer data platform » to get a true Customer 360° view… Customer Data Platform
  34. 34. © Talend 2014 37 From customer 360 view to the customer timeline Get the loyalty card Clicks for The coupon Receive a promotion Orders On line Complain Searches For television Connect to wifi Search In amazon Acquires television
  35. 35. © Talend 2014 38 From clickstream to customer analytics and to real time recommendations From analytics to actionable recommendations •Create personalized journeys -Personalization for outbound marketing (e-mails, SMS, mobile notifications…) -Real time recommendations for inbound marketing (mobile, web…) -Next best actions for the field (contact center, clienteling at the point of sales…) •Customer touch-points are integrated iteratively into real time scenarios •Business benefits: Sales efficiency is improved, and every marketing activities (campaigns, promotions…) can be measured at a very fine grain -> click rates, transformation rates, campaign effectiveness…
  36. 36. © Talend 2014 39 OVERCOMING THE FIVE CHALLENGES OF YOUR MDM JOURNEY Master Data Management 101 The five challenges to deliver on the promises of MDM - Trends and wrap-up Wrap-up
  37. 37. © Talend 2014 40 Trends in MDM Ten priorities to guide organizations into next generation MDM 1.Multi-domain MDM 2.Multi department, multi application MDM 3.Bi-directional MDM 4.Real time MDM 5.Consolidating multiple MDM Solutions 6.Coordination with other disciplines 7.Richer Modeling 8.Beyond Enterprise Data 9.Workflow and Process Management 10.MDM solutions build atop vendor tools and platforms Source : TDWI next generation MDM Key technologies challenges for next generation MDM 1.Complex relationships 2.Mobile 3.Social 4.Big Data 5.Time-travel 6.Cloud 7.Action enablement 8.Real time 9.Extreme scalability 10.Proactive, integrated governance Source : The MDM Institute
  38. 38. © Talend 2014 41 Thank your for your attention Overcoming the Five Challenges of your MDM journey Contact us: jfranco@talend.com djosephine@talend. com Learn more: www.talend.com.product/mdm

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