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© 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
September 2017
Connecting your Business with AI and Big Data
Thomas Reske, [email protected]
Why?
Machine Learning &
Artificial Intelligence
Big Data
More Users Better Products
More Data Better Analytics
A Flywheel For Data
How?
Who are internal stakeholders?
Interests
Data Architect Data Scientist Data Engineer
• Data quality• Structure• Governance• Data discovery• Security• Compliance• Long-term platform• …
• Data discovery• Data quality• Exploratory Analysis• Visualization• Building models• Scalability• Model discovery• …
• Integration• ETL• Scalability• Robustness• Delivery + Pipeline• Data platform• SDKs + APIs• Service endpoints• …
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• ...
Challenges and Goals
Executive Management Harness artificial intelligence and machine learning to take and gain
(competitive) advantage
Data Architect Shape a compliant, secure data solution from which the firm benefits over the long term
Data Scientist Minimize plumbing and clean-up work and maximize value creationvia analyzing, building and evaluating models
Data Engineer Provide secure access to clean, easy-to-use data for a variety of consumers and building robust, scalable data pipelines
Analyst Dissect complex business problems and creatively identify use cases
for machine learning and artificial intelligence
Product/Program Owner Describe requirements and vision of the product and manage its complete lifecycle from inception to operations
Process
Business
Understanding
Data
Preparation
ModelingDeployment
Evaluation
Data
Understanding
Process
Business
Understanding
Data
Preparation
ModelingDeployment
Evaluation
Data
Understanding
Key Activities
• ingestion and/or acquisition of data
• manage data storage, e.g. lifecycle policies
• data governance, e.g. ACLs or licensing
• …
Process
Business
Understanding
Data
Preparation
ModelingDeployment
Evaluation
Data
Understanding
Key Activities
• scenario definition and problem formulation
• cast business problem as data science problem
• identify key metrics • discovery of data sources• …
Process
Business
Understanding
Data
Preparation
ModelingDeployment
Evaluation
Data
Understanding
Key Activities
• 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
• …
Process
Business
Understanding
Data
Preparation
ModelingDeployment
Evaluation
Data
Understanding
Key Activities
• data manipulation and conversion, e.g. formatting
• infer missing values• normalization of data• addressing leakage issues• …
Process
Business
Understanding
Data
Preparation
ModelingDeployment
Evaluation
Data
Understanding
Key Activities
• formulate, create and build model
• apply machine learning and data mining techniques and algorithms
• …
Process
Business
Understanding
Data
Preparation
ModelingDeployment
Evaluation
Data
Understanding
Key Activities
• assessment of results and practicability
• test model and gain confidence
• review match with business needs
• “in vivo” evaluation and experiments
• sign-off• …
Process
Business
Understanding
Data
Preparation
ModelingDeployment
Evaluation
Data
Understanding
Key Activities
• re-code for production• deployment of systems,
processes or procedures• monitor KPIs• ...
Process
Business
Understanding
Data
Preparation
ModelingDeployment
Evaluation
Data
Understanding
Key Activities
• manage and optimize process lifecycle, e.g. reduce friction, cycle times and facilitate collaboration
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
Technology
https://www.slideshare.net/AmazonWebServices/big-ddata-architectural-patterns-and-best-practices-on-aws
What will you build?
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