Webinar: 10-Step Guide to Creating a Single View of your Business
3 likes2,291 views
The document outlines a 10-step methodology for creating a 'single view' of business entities by aggregating data from various sources to improve visibility and operational efficiency. It highlights the technical requirements and challenges involved in establishing this unified view, such as data governance and system integration. Use cases include enhanced customer profiling, inventory management, and healthcare records, demonstrating the potential benefits across multiple industries.
![Step 5: Develop Single View Data Model
• Key inputs
– Required data attributes
– Query patterns
• Define common fields & data types
– Create rules to validate common data
• Define primary & secondary indexes
• Identify dynamic fields
– No need to pre-declare when using a document database
• Localize data into a single document (where
appropriate)
{
_id : “mark.smith@mongodb.com”,
first_name : "Mark",
last_name : "Smith",
city : "San Francisco",
phones: [ {
number : “1-212-777-1212”,
dnc : true,
type : “home”
},
{
number : “1-212-777-1213”,
type : “cell”
}]}
Single View
Develop](https://image.slidesharecdn.com/singleviewwebinar1-170308180950/85/Webinar-10-Step-Guide-to-Creating-a-Single-View-of-your-Business-13-320.jpg)
Webinar: 10-Step Guide to Creating a Single View of your Business
- 1. 10-Step Methodology to Building a Single View MatKeep,DirectorofProduct&MarketAnalysis. mat.keep@mongodb.com @matkeep JonRangel,DirectorofProfessionalServices,EMEA. jon.rangel@mongodb.com
- 2. What You Will Learn 1. Single View: Opportunities & Challenges 2. Repeatable 10-Step Methodology 3. Required Technical Capabilities
- 4. Single View Defined • What – Single, real-time representation of a business entity or domain – Customer, product, supply chain, financial asset class, & more • How – Gathers and organizes data from multiple, disconnected sources; – Aggregates information into a standardized format and joint information model • Why – Improves business visibility – Serve operational applications – Foundation for analytics
- 5. Single View Use Cases • Comparative view of traders or products • Firm-wide view of asset exposure • Aggregated transactions for fraud models • Omni-channel view of customers for personalized marketing • Inventory control & management • Single view of product across channels & demographics • Management of patient medical records for treatment plans • Macro-analysis view for public health • Medical history to identify insurance risk Finance Retail Healthcare
- 6. Challenges • Current State – Data dispersed across multitude of systems – Different structures, different attributes – Apps built to meet specific business requirements, not integrated – New data sources from new apps, M&A • Governance Processes – How to deliver & maintain single view in face of constant business change • Technology Limitations – Traditional databases not well suited to single view required capabilities
- 8. ETLorMessageQueue Web Mobile CRM Mainframe Single View Call Center Analytics Technical Support Billing Source Systems Consuming Systems Load Reads High Level Architecture
- 9. 10-Step Methodology Step 1: Define Scope Step 4: Appoint Data Stewards Step 5: Develop Data Model Step 6: Load & Standardize Step 7: Merge, Test & Reconcile Step 8: Infrastructure Design Step 3: Identify Data Producers Step 2: Identify Data Consumers Step 9: Modify Consuming Systems Step 10: Maintenance Processes Discover Develop Deploy
- 10. Step 1: Define Scope & Sponsorship • Scope needs to be realistic, defined by specific success metric – Long term: aggregate all customer data into a single view, serving all business functions – Initial phase: collecting all customer interactions on digital channels over past 3-months to improve call center MTTR • Appoint executive sponsors – Senior: allocate resources and command credibility – Combination of senior title from the business, and from the technology group Discover
- 11. Web Mobile CRM Mainframe Source Systems Steps 2 & 3: Identify Data Consumers & Producers • Single View Consumers Define – Typical queries and SLAs – Required data attributes – Current data sources • Identify apps generating the source data – Identify application owners + associated databases – Profile apps: operational, analytical Step 2: Data Consumers Step 3: Data Producers Discover
- 12. Step 4: Appoint Data Stewards • Data steward appointed for each data source. • Deep knowledge of: – Source system schema – Which tables store required attributes, what format – Clients and apps that generate & consume the source data • Advise on data loading strategies Develop
- 13. Step 5: Develop Single View Data Model • Key inputs – Required data attributes – Query patterns • Define common fields & data types – Create rules to validate common data • Define primary & secondary indexes • Identify dynamic fields – No need to pre-declare when using a document database • Localize data into a single document (where appropriate) { _id : “mark.smith@mongodb.com”, first_name : "Mark", last_name : "Smith", city : "San Francisco", phones: [ { number : “1-212-777-1212”, dnc : true, type : “home” }, { number : “1-212-777-1213”, type : “cell” }]} Single View Develop
- 14. Resources to Support Schema Design MongoDB Documentation MongoDB Development Rapid Start Develop
- 15. Step 6: Load 2 phases: Initial Load & Delta Load Emit JSON to preserve data types. Use Extended JSON Load ETLorMessageQueue Single View Develop Initial Load • ETL Tools • Custom Loaders Delta Load • Batch loads: use tools above • Real-time loads: Message queue
- 16. Step 6 (cont’d): Standardize Data Source A Data Source B Data Source C 14 77 26 cust_id: 14 f_name: James l_name: Bond dob: 07/14/1968 eMail: 007@spook.com fno: 77 first: Jim last: Bond born: 1968-07-14 email: 007@spook.com xc_id: 26 name: James Bind bdate: July 14, 68 Email: 007@spook.com Develop
- 17. Step 7: Match, Merge & Reconcile Develop cust_id: 14 f_name: James l_name: Bond dob: 07/14/1968 eMail: 007@spook.com xc_id: 26 name: James Bind bdate: July 14, 68 Email: 007@spook.com source_id: A_14 first_name: James last_name: Bond dob: 1968-07-14 eMail: 007@spook.com source_id: B_77 first_name: Jim last_name: Bond dob: 1968-07-14 eMail: 007@spook.com source_id: C_26 first_name: James last_name: Bind dob: 1968-07-14 eMail: 007@spook.com _id: 007@spook.com first_name: James last_name: Bond dob: 1968-07-14 Source Data Standardized Data Field names & data types Single View Data merged, tested & reconciled fno: 77 first: Jim last: Bond born: 1968-07-14 email: 007@spook.com
- 18. Step 7 (cont’d): Match, Merge & Reconcile • Use iterative grouping functions to cluster records with similar attributes 1. Match against unique, authoritative attributes (email address, credit card #) 2. Match by combining attributes (last name, DoB, zip code) 3. Use fuzzy matching to catch errors in source data (i.e. different spellings of customer name) • Apply confidence factor to dictate merging – Automatically merge records with 95%+ confidence – Manually inspect records with lower confidence Develop
- 19. Step 7 (cont’d): MongoDB Tools • Workers framework to parallelize document comparisons • Grouping tool to cluster documents based on attribute similarity – Levenshtein to calculate distances, single-linkage clustering for matching Develop
- 20. Step 8: Architecture Design Deploy • Deployment infrastructure • MongoDB Production Readiness Consulting Package provides recommendations: – Hardware sizing – HA/DR strategies – Scaling – Security for corporate and regulatory compliance • Follow-on services for implementation
- 21. Step 9: Modify Consuming Systems Deploy • Modify the apps that consume the single view – Create an API that exposes the single view (i.e. RESTful web service) – Re-point apps to the web service (reads initially) • Modify one consuming application at time Call Center Analytics Technical Support Billing Consuming Systems Reads Single View
- 22. Step 10: Implement Maintenance Processes Deploy • Frequency of application launch & evolution is accelerating • Impacts to single view – Adding new attributes from source systems – Onboarding new data sources or digital channels – Creating new apps that consume the single view • Single view team needs to institutionalize governance around on-going maintenance – Repeat the 10-step process – Dynamic schema is HUGE!
- 23. Single View Maturity Model
- 24. Scope BusinessBenefits Transactions are written first to the single view, which propagates the data back to the source system of record. Writes are performed concurrently to the source systems as well as the single view The single view data model is enriched with additional sources to serve more applications, including real-time analytics. The single view becomes a platform serving multiple applications Single View Platform Records are copied via ETL or message queue mechanisms from the source systems into the single view, serving read queries. The single view serves one specific application Single View Application Single View First Dual Writes Read Centric Transforming the role of the single view Reads & Writes Single View Maturity Model • Advantages of writing to the single view – Fresher data – Reduced app complexity – Improved application agility
- 25. Architecture for Writes to the Single View ETLorMessageQueue Web Mobile CRM Mainframe Single View Call Center Analytics Technical Support Billing Update Queue Reads Writes Source Systems Consuming Systems Load
- 26. Required Capabilities for Single View
- 27. Single View with a Relational Database
- 28. Required Database Capabilities • Data model flexibility with a dynamic schema • Real-time analytics • Performance, scale & always-on • Enterprise deployment model
- 29. MongoDB Compass MongoDB Connector for BI MongoDB Enterprise Server Enterprise Deployment Model24x7Support (1hourSLA) CommercialLicense (NoAGPLCopyleftRestrictions) Platform Certifications MongoDB Ops Manager Monitoring & Alerting Query Optimization Backup & Recovery Automation & Configuration Schema Visualization Data Exploration Ad-Hoc Queries Visualization Analysis Reporting Authorization Auditing Encryption (In Flight & at Rest) Authentication REST APIEmergency Patches Customer Success Program On-Demand Online Training Warranty Limitation of Liability Indemnification
- 30. Single View In Action
- 31. Single View of Customer Insurance leader generates coveted single view of customers in 90 days – “The Wall” Problem Why MongoDB ResultsProblem Solution Results No single view of customer, leading to poor customer experience and churn 145 years of policy data, 70+ systems, 24 800 numbers, 15+ front-end apps that are not integrated Spent 2 years, $25M trying build single view with RDBMS – failed Built “The Wall,” pulling in disparate data and serving single view to customer service reps in real time Flexible data model to aggregate disparate data into single data store Expressive query language and secondary indexes to serve any field in real time Prototyped in 2 weeks Deployed to production in 90 days Decreased churn and improved ability to upsell/cross-sell
- 32. Single View of LHC Analytics Data aggregation system to accelerate scientific research & discovery Problem Why MongoDB ResultsProblem Solution Results Raw data from LHC & experiments distributed across multitude of source systems Scientists don’t know location of source data, or how to extract it Relational databases rigid data model prevented aggregation of data from different sources Data Aggregation System built on MongoDB, consolidating analytics into a single view Dynamic schema represents data of any structure MongoDB query language supports simple lookups to complex search, traversals & analytics A single query to MongoDB can return 10,000 documents from different data sources for real time analytics Accelerates scientific time to insight Accessed by 3,000 physicists from 200 research institutions across the globe
- 33. Wrap Up
- 34. Where to Go from Here? • Single view projects are challenging – Partner with a vendor offering proven methodology, tools & technologies • Learn More – Download the whitepaper – 10-Step Methodology to Building a Single View • Engage – MongoDB Global Consulting Services can help you scope the project and get started – Book a workshop
- 35. 10-Step Methodology to Building a Single View
- 36. Single View of the Customer 360° view of the customer increases customer satisfaction, cross-sell & up-sell with MongoDB, Spark, & Hadoop Problem Why MongoDB ResultsProblem Solution Results Customer data scattered across 100+ different systems Poor customer experience: no personalization, no consistent experience across brands or devices No way to analyze customer behavior to deliver targeted offers Single View application on MongoDB flexible data model, expressive query language, secondary indexes, & horizontal scalability Data from old relational systems fed into Spark for analysis and then stored in MongoDB to support real-time CRM Customer data synced from MongoDB to Hadoop for nightly batch jobs, then fed back to MongoDB for personalized recommendations Single view serves customers from any channel Stores 10s of TBs of customer data across multiple data centers Increased revenues from improved customer intimacy, driving cross- sell and upsell Global Airline
- 37. Data Model Flexibility …Mobile App Web Call Centre CRM Social Feed COMMON FIELDS CustomerID | eMail | DYNAMIC FIELDS Can vary from record to record: location, action Single View
- 38. Customer Service Application MongoDB Primary Replica Single View BI & Reporting REST Data Services Real-time Data Services for Regulators & Partners VisualisationsQueries & Updates Aggregates Predictive Analytics MongoDB Secondary Replica Single ViewMongoDB Secondary Replica Single ViewMongoDB Secondary Replica Single ViewMongoDB Secondary Replica Single ViewMongoDB Secondary Replica MongoDB Secondary Replica Data Analytics Pipeline Real-Time Analytics
- 39. Predictable Scale & Always-On Shard 1 Horizontally Scalable Shard 2 Shard 3 Shard n