Connecting your Business with AI and Big Data

Transcription

1 Connecting your Business with AI and Big Thomas Reske, September , Amazon Web Services, Inc. or its Affiliates. All rights reserved.

2 Why?

3 A Flywheel For More Big Better Analytics More Users Better Products Machine Learning & Artificial Intelligence

4 How?

5 Who are internal stakeholders?

6 Interests Architect Scientist Engineer quality Structure Governance discovery Security Compliance Long-term platform discovery quality Exploratory Analysis Visualization Building models Scalability Model discovery Integration ETL Scalability Robustness Delivery + Pipeline platform SDKs + APIs Service endpoints

7 Interests Analyst Product/Program Owner Executive Management Discovery Business models Processes Metrics + KPI Monitoring Reporting Visualisation Cycle times Flow and delivery Roadmap Learning Costs Friction Vision Strategy Innovation + Ideation Time to Value (R)Evolution HRM + Talent Collaboration Costs...

8 Challenges and Goals Executive Management Architect Scientist Engineer Analyst Product/Program Owner Harness artificial intelligence and machine learning to take and gain (competitive) advantage Shape a compliant, secure data solution from which the firm benefits over the long term Minimize plumbing and clean-up work and maximize value creation via analyzing, building and evaluating models Provide secure access to clean, easy-to-use data for a variety of consumers and building robust, scalable data pipelines Dissect complex business problems and creatively identify use cases for machine learning and artificial intelligence Describe requirements and vision of the product and manage its complete lifecycle from inception to operations

9 Process Business Preparation Deployment Modeling Evaluation

10 Process Business Preparation Key Activities ingestion and/or acquisition of data manage data storage, e.g. lifecycle policies data governance, e.g. ACLs or licensing Deployment Evaluation Modeling

11 Process Key Activities scenario definition and problem formulation cast business problem as data science problem identify key metrics discovery of data sources Business Preparation Deployment Modeling Evaluation

12 Process Key Activities Business Preparation assess strengths and limitations, e.g. reliability estimate cost of data arrange data collection and acquisition initial cleaning and matching data sources surface and uncover relation of data to business problem Deployment Modeling Evaluation

13 Process Business Deployment Preparation Modeling Key Activities data manipulation and conversion, e.g. formatting infer missing values normalization of data addressing leakage issues Evaluation

14 Process Business Deployment Preparation Modeling Key Activities formulate, create and build model apply machine learning and data mining techniques and algorithms Evaluation

15 Process Business Preparation Key Activities assessment of results and practicability test model and gain confidence review match with business needs in vivo evaluation and experiments sign-off Deployment Evaluation Modeling

16 Process Business Key Activities re-code for production deployment of systems, processes or procedures monitor KPIs... Deployment Preparation Modeling Evaluation

17 Process Business Key Activities manage and optimize process lifecycle, e.g. reduce friction, cycle times and facilitate collaboration Deployment Preparation Modeling Evaluation

18 Key Points focus on common understanding of end-to-end process, adjust appropriately use model to gauge and assess maturity model serves well to describe vision (not mature) or to structure concerns and issues (very mature) not (necessarily) those that have the smartest people or algorithm succeed, but those that master the cycle and process flow

19 Technology

20 What will you build?

21 Thank You Thomas Reske,