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ADV Slides: Increasing Artificial Intelligence Success with Master Data Management
The document discusses the importance of Master Data Management (MDM) in enhancing the success of artificial intelligence (AI) applications across various industries, including healthcare, finance, and smart cities. It highlights that effective MDM is essential for leveraging data for AI initiatives, which can improve operational efficiency, customer experiences, and predictive capabilities. The presentation by William McKnight emphasizes a structured approach to integrating MDM with AI to optimize data utilization and support enterprise objectives.
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ADV Slides: Increasing Artificial Intelligence Success with Master Data Management
- 1. Increasing Artificial Intelligence Successwith Master Data Management Presented by: William McKnight “#1 Global Influencer in Master Data Management” Onalytica President, McKnight Consulting Group Inc 5000 @williammcknight www.mcknightcg.com (214) 514-1444
- 3. Our planet isbecoming a very different place 3
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- 5. Deepfakes, Sophia &Identifying People
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- 11. Enhance in-car navigation usingcomputer vision Reduce cost of handling misplaced items improve call center experiences with chatbots Improve financial fraud detection and reduce costly false positives Automate paper-based, human-intensive process and reduce Document Verification Predict flight delays based on maintenance records and past flights, in order to reduce cost associated with delays AI in Action in the Enterprise
- 12. Smart Cities Track vehiclemovements, traffic data, environmental factors to optimize traffic lights, ensure smooth flow and manage tolling Retail, Manufacturing Supply flow, Customer flow Cybersecurity Proactive data collection and analysis of threats Marketing Segmentation analysis, campaign effectiveness Oil & Gas Determine drilling patterns, ensure maximum utilization of assets, manage operational expenses, ensure safety, predictive maintenance Life Sciences Study human genome (100s MB/person) for improving More AI Business Use Case Examples
- 13. Where to Lookfor AI Opportunities The products you make and the services you offer 13 The supply chain for those products and services Business operations (hiring, procurement, after-sale service, etc.) The intelligence used in the marketing/ approval funnel for your products and services The intelligence used in designing your product and service set
- 14. AI Affects theEntire Organization • Strategic • Technical • Operational • Talent • Data 14
- 15. AI is onthe Data Maturity Spectrum Maturity Level 3 (of 5): All in on AI
- 16. Data to Manage •This is wide ranging, spanning all current data – eCommerce – ERP / CRM – IoT (e.g., Heavy Industry, Factory, Consumer, Health, Aircraft) • Equipment performance • Forecast breakdowns • Health risk – Publicly available (e.g., governmental) – Third party 16
- 17. Customer account data and purchasehistory AI Data Examples Call center recordings and chat logs 17 Streaming sensor data, historical maintenance records and search logs Email response metrics Product catalogs and data sheets Sentiment analysis, user-generated content, social graph data, and other external data sources User website behaviors YouTube video content audio tracks Public references
- 18. dob_id cust_status_id marital_status_id gender_id mailable_addr_id cust_type_id mail_allowed_id returned_mail.id cust_title first_name middle_inits last_name name_siffix_1 name_suffix_2 date_of_birth area_code full_phone_nbr email_address city_name … Empowering Attributes last_channel_used_id last_visit_date most_used_channel_id lifetime value lifetime_txns lifetime_spend lifetime_margin lifetime_markdown satisfaction segment catspurchased from Propensity churn Propensity buy prod Propensity social … E m p o w e r i n g C o r e
- 19. Data is readywhen it is… • In a leverageable platform • In an appropriate platform for its profile and usage • With high non-functionals (Availability, performance, scalability, stability, durability, secure) • Data is captured at the most granular level • Data is at a data quality standard (as defined by Data Governance) 19 Projects are a series of subject area mastery
- 20. Robust MDM ishalf of the effort for AI Success • Fraud Detection • Call Center Chatbot • Self-Driving/Transportation • Predict Flight Delays • Marketing – segmentation analysis, campaign effectiveness • Smart Cities • Retail, Manufacturing – Supply flow, Customer flow • Oil and Gas Exploration • Customer • Employee • Partner • Patient • Supplier • Product • Bill of Materials • Assets • Equipment • Media • Geography • Citizen • Agencies • Branches • Facilities • Franchises • Stores • Account • Certifications • Contracts • Financials • Policies • Weather Enterprise Data Domains
- 21. MDM: Not anOption for AI Apps Application Focus Focus is on an Application’s Master Data Needs First Usually a work effort to get to 2nd, 3rd, etc. applications Build to Scale! Enterprise Focus Focus is on a Subject Area First Higher Chance of Creating New Organizational Possibilities! Danger: Build it and They Will Come Either Initial Focus Needs a Secondary Focus on the Other It’s the MDM Leadership Challenge! You’ll do MDM but without a discrete focus on it, not well
- 22. Either Way Do itwith data specialists Data modeling, integration, quality Use a Tool It’s Operational and Real-Time Let the Hub create analytical/empowering elements Make it a discrete project With high touchpoints with application Focus on Total Cost of Ownership first for Justification Build to Scale It doesn’t take longer to consider all known requirements Ramp up engagement efforts early (and do often!) You’ll do MDM but without a discrete focus on it, not well
- 23. The Real DecisionPoints Sponsorship Subject Area Publishers or Workflow Don’t Forget Third-Party Data Subscribers AI Applications Don’t Forget “Common” Artifacts like Data Warehouse, Data Lake, and Operational Communications Data Governance/Stewardship Roadmapping Around:
- 24. DaaS SLAs Build anSLA for the Master Data MDM Communication and COE Integration Planning? Hub Model and Rule Expansion Mapping elements from Hub to Subscriber? Customization of elements and DQ rules for Subscriber? Every new integration will have some! How Far Does the Build Team Go?
- 25. MDM is integralto AI Success • Fraud Detection • Customer • Employee • Partner • Patient • Supplier • Product • Bill of Materials • Assets • Equipment • Media • Geography • Citizen • Agencies • Branches • Facilities • Franchises • Stores • Account • Certifications • Contracts • Financials • Policies • Weather Enterprise Data Domains
- 26. MDM is integralto AI Success • Call Center Chatbot • Customer • Employee • Partner • Patient • Supplier • Product • Bill of Materials • Assets • Equipment • Media • Geography • Citizen • Agencies • Branches • Facilities • Franchises • Stores • Account • Certifications • Contracts • Financials • Policies • Weather Enterprise Data Domains
- 27. MDM is integralto AI Success • Self-Driving/Transportation • Customer • Employee • Partner • Patient • Supplier • Product • Bill of Materials • Assets • Equipment • Media • Geography • Citizen • Agencies • Branches • Facilities • Franchises • Stores • Account • Certifications • Contracts • Financials • Policies • Weather Enterprise Data Domains
- 28. MDM is integralto AI Success • Predict Flight Delays • Customer • Employee • Partner • Patient • Supplier • Product • Bill of Materials • Assets • Equipment • Media • Geography • Citizen • Agencies • Branches • Facilities • Franchises • Stores • Account • Certifications • Contracts • Financials • Policies • Weather Enterprise Data Domains
- 29. MDM is integralto AI Success • Marketing – segmentation analysis, campaign effectiveness • Customer • Employee • Partner • Patient • Supplier • Product • Bill of Materials • Assets • Equipment • Media • Geography • Citizen • Agencies • Branches • Facilities • Franchises • Stores • Account • Certifications • Contracts • Financials • Policies • Weather Enterprise Data Domains
- 30. MDM is integralto AI Success • Smart Cities • Customer • Employee • Partner • Patient • Supplier • Product • Bill of Materials • Assets • Equipment • Media • Geography • Citizen • Agencies • Branches • Facilities • Franchises • Stores • Account • Certifications • Contracts • Financials • Policies • Weather Enterprise Data Domains
- 31. MDM is integralto AI Success • Retail, Manufacturing – Supply Chain • Customer • Employee • Partner • Patient • Supplier • Product • Bill of Materials • Assets • Equipment • Media • Geography • Citizen • Agencies • Branches • Facilities • Franchises • Stores • Account • Certifications • Contracts • Financials • Policies • Weather Enterprise Data Domains
- 32. MDM is integralto AI Success • Oil and Gas Exploration • Customer • Employee • Partner • Patient • Supplier • Product • Bill of Materials • Assets • Equipment • Media • Geography • Citizen • Agencies • Branches • Facilities • Franchises • Stores • Account • Certifications • Contracts • Financials • Policies • Weather Enterprise Data Domains
- 33. Q1 Q2 Q3Q4 Q5 Q6 Q7 Q8 Q9 MDM Roadmap 33 Dadta Governance Tool Procurement & Installation Business Prioritization MDM Architecture Stakeholders & Roles Workflow & Data Flow MDM Lifecycle Planning Delivery Phase 1 Customer Delivery Delivery Phase 2 Product Phase 3 Supplier
- 34. Artificial Intelligence Applicationsin the Enterprise are about putting AI to work on master data built with artificial intelligence.
- 35. Increasing Artificial Intelligence Successwith Master Data Management Presented by: William McKnight “#1 Global Influencer in Master Data Management” Onalytica President, McKnight Consulting Group Inc 5000 @williammcknight www.mcknightcg.com (214) 514-1444