The Doppler Quarterly Summer 2016 | Page 38

Scope and Planning
Data Inventory
Application Inventory
Infrastructure Inventory
Big Data Vision and Business Case
Implementation Strategy
• Kickoff and Resource Plan
• Scope and Deliverables Finalization
• Resource Schedule
• Detailed Project Plan
• Review of current systmems of record
• Review of current corporate standards for Data Governance
• Document corporate quality standards for inbound data
• Inventory existing systmes related to BI , Analytics , Reporting and DW
• Infrastructure Technology Portfolio
• SDLC Environments
• Model existing • Shadow IT Analysis systems workflows and data relationships • Capacity and Growth
Analysis
• Assess current model management and • Ops Model and Cost execution methods
• Service Tiers
• Change Management
• Technology Portfolio
• Operations Organization
• Cloud Endpoints and Service Tiers
• Cloud Principles
• Reference Architecture and capabilities
• Constraints and Dependencies
• Business Drivers and Opportunities
• Risks
• Business Case
• Platform Endpoint Strategy
• App Migration Strategy
• Rapid Implementation Plans
• Budget and Resources
• Implementation Roadmap
• Operational Model
• Cost Model
Figure 1 : Analytical Capability Deployment Strategy
Machine learning is an emerging trend within the technology space , but not a new technology . Machine learning capabilities have been researched for decades and leveraged for many years by mature technology organizations . The difference now is the ability for more organizations to leverage the work in the machine learning community , including easy to consume APIs and pretrained models specific to certain domains . Machine learning complements the growing work in the predictive analytics space by ensuring outcomes and recommendations are more accurate and highly personalized to the person , organization , domain and purpose .
Building a Data Lake in the cloud takes special considerations , and provides for advanced capabilities not economically available in on-premise deployments , including elasticity , automated recovery , multi-zone availability and PaaS based analytical services for data consumption .
Many organizations will evaluate the best location to deploy a data lake . Because of the need to ingest and integrate data from many existing systems , the location and connectivity of a data lake are key to its effectiveness and usability . Cloud-based data lakes provide an advantage because of their ability to quickly spin up and down new resources , to connect to a variety of networks and data sources , and , most importantly , to leverage the powerful tools and expertise that the providers offer and have proven in running their own complex , world-wide services .
36 | THE DOPPLER | SUMMER 2016