ADV Slides: The Data Needed to Evolve an Enterprise Artificial Intelligence Strategy
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This webinar will focus on the promise AI holds for organizations in every industry and every size, and how to overcome some of the challenges today of how to prepare for AI in the organization and how to plan AI applications. The foundation for AI is data. You must have enough data to analyze to build models. Your data determines the depth of AI you can achieve – for example, statistical modeling, machine learning, or deep learning – and its accuracy. The increased availability of data is the single biggest contributor to the uptake in AI where it is thriving. Indeed, data’s highest use in the organization soon will be training algorithms. AI is providing a powerful foundation for impending competitive advantage and business disruption.
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ADV Slides: The Data Needed to Evolve an Enterprise Artificial Intelligence Strategy
- 1. The Data Needed to Evolve an Enterprise Artificial Intelligence Strategy Presented by: William McKnight President, McKnight Consulting Group @williammcknight www.mcknightcg.com (214) 514-1444
- 2. AI Whiskey, AI Music, AI Paintings 2 https://techcrunch.com/2019/05/15/oh-no-theres-a-i-whiskey-now/
- 3. Deepfakes, Sophia and Analyzing Emotions
- 4. 4
- 5. 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
- 8. Satellite or Aerial Data Management https://medium.com/the-downlinq/car-localization-and-counting-with-overhead-imagery-an-interactive-exploration-9d5a029a596b
- 9. More AI Business Use Case Examples • Marketing – segmentation analysis, campaign effectiveness • Cybersecurity – proactive data collection and analysis of threats • 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 • Oil and 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 health
- 10. What’s New is Deep Learning • AI: 1950s • Machine Learning: 2000s – supervised learning, unsupervised learning, reinforcement learning • Deep Learning: 2010s – Higher Predictive Accuracy – Can Analyze All Data Sets Deep Learning allows more complex problems to be tackled, and others to be solved with higher accuracy, with less cumbersome manual fine-tuning
- 11. AI Affects the Entire Organization • Strategic • Technical • Operational • Talent • Data 11
- 12. Where to Look for AI Opportunities • The products you make and the services you offer • The supply chain for those products and services • Business operations (hiring, procurement, after-sale service, etc.) • The intelligence used in designing your product and service set • The intelligence used in the marketing/approval funnel for your products and services 12
- 13. AI is on the Data Maturity Spectrum Maturity Level 3 (of 5): Data Strategy Data as asset in financial statements / executives; All development is within architecture; All in on AI Architecture EDW with DQ above standard; 3 & 5 year architecture plans Technology DI=streaming; Graph db for relationship data; Specialized analytic stores for workloads with requirements not suited for the EDW; EDW columnar; No ODS; minimal cubes; MDM – all functions for all major subject areas Organization Data Governance by subject area across all major subject areas; Organizational Change Management program is part of all projects; True Self-Service Business Intelligence; Chief Information Officer
- 14. Get Data Under Management 14 • In a leveragable platform • In an appropriate platform – For the data – For the usage • Used effectively by multiple business groups • High NFRs – Availability, performance, scalability, stability, durability, secure • Granular capture • Data at data quality standard – As defined by Data Governance
- 15. Data to Collect • 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 15
- 16. AI Data Examples • Call center recordings and chat logs • Streaming sensor data, historical maintenance records and search logs • Customer account data and purchase history • Email response metrics • Product catalogs and data sheets • Public references • YouTube video content audio tracks • User website behaviors • Sentiment analysis, user-generated content, social graph data, and other external data sources 16
- 17. Data is integral to 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 Robust Data is half of the effort for AI success
- 18. Data 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
- 19. Data 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
- 20. Data 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
- 21. Data 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
- 22. Data 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
- 23. Data 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 • Certification s • Contracts • Financials • Policies • Weather Enterprise Data Domains
- 24. Data is integral to AI Success • Retail, Manufacturing – Supply Flow, Customer Flow • 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. Data 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
- 26. Where to put data for Machine Learning • Cloud Storage/Data Lake • DBMS • Master Data Management • HDFS – optimized for sequential read/writes • Unstructured Data Stores • Text-based serializations 26
- 27. Analytics Reference Architecture Logs (Apps, Web, Devices) User tracking Operational Metrics Offload data Raw Data Topics JSON, AVRO Processed Data Topics Sensors and / or Transactiona l/ Context Data OLTP/ODS ETL Or ELT Batch Low Latency Applications Files In- database analytics Reach through or ETL/ELT or Stream Processing or Stream Processing Q Q Data Warehouse
- 28. AI Pattern 1. Hire/Grow Data Science 2. Uncouple AI from Organizational Constraints – While Conforming the Organization 3. Ideation 4. Compile Data! – Internal and External 5. Label Data 6. Build Model 7. Prototype 8. Iterate 9. Productionalize 10. Scale 28
- 29. Algorithm & Data Matching • Naive Bayes Classification • Decision Tree • Regression Try Multiple; Run Contests 29
- 30. Corporate Requirements > Data • The split of the necessary AI/ML between the 'edge' of corporate users and the software itself is still to be determined • Math – floating point arithmetic, deep statistics, and linear algebra • GPUs • Python – easy to program and it good enough – NumPy and pandas libraries are available • TensorFlow – adds a computational/symbolic graph to Python • R and MATLAB – optimized for math with features such as direct slice and dice of matrices and rich libraries to draw from • Java and Scala – work well with Hadoop and Spark respectively 30
- 31. The Data Needed to Evolve an Enterprise Artificial Intelligence Strategy Presented by: William McKnight President, McKnight Consulting Group @williammcknight www.mcknightcg.com (214) 514-1444