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ADV Slides: Increasing Artificial Intelligence Success with Master Data Management

Companies all over the world are going through a digital transformation now, which in many cases, is all about maturing the data environment and the use of data. Master data is key to this effort. All transformative projects require master data and usually many subject areas. Current efforts to deliver master data to the enterprise are cumbersome, inefficient, and met with limited acceptance.

We’ll look at enterprise use cases of artificial intelligence and show the master data that is needed. We’ll see what some MDM vendors are doing with AI and how the future of MDM will be shaped by looking at some specific MDM actions influenced by AI.

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ADV Slides: Increasing Artificial Intelligence Success with Master Data Management

  1. 1. Increasing Artificial Intelligence Success with 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
  2. 2. Our planet is becoming a very different place 3
  3. 3. AI Art 4
  4. 4. Deepfakes, Sophia & Identifying People
  5. 5. Reading 6
  6. 6. Sensors 7
  7. 7. Medicine 8
  8. 8. 9
  9. 9. GPT-3 10
  10. 10. Enhance in-car navigation using computer 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
  11. 11. Smart Cities Track vehicle movements, 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
  12. 12. Where to Look for 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
  13. 13. AI Affects the Entire Organization • Strategic • Technical • Operational • Talent • Data 14
  14. 14. AI is on the Data Maturity Spectrum Maturity Level 3 (of 5): All in on AI
  15. 15. 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
  16. 16. Customer account data and purchase history 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
  17. 17. 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 cats purchased from Propensity churn Propensity buy prod Propensity social … E m p o w e r i n g C o r e
  18. 18. Data is ready when 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
  19. 19. Robust MDM is half 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
  20. 20. MDM: Not an Option 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
  21. 21. Either Way Do it with 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
  22. 22. The Real Decision Points 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:
  23. 23. DaaS SLAs Build an SLA 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?
  24. 24. MDM is integral to 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
  25. 25. MDM is integral to 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
  26. 26. MDM is integral to 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
  27. 27. MDM is integral to 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
  28. 28. MDM is integral to 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
  29. 29. MDM is integral to 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
  30. 30. MDM is integral to 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
  31. 31. MDM is integral to 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
  32. 32. Q1 Q2 Q3 Q4 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
  33. 33. Artificial Intelligence Applications in the Enterprise are about putting AI to work on master data built with artificial intelligence.
  34. 34. Increasing Artificial Intelligence Success with 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

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