Cloud Adoption Framework
Cloud solutions offer modern, intuitive, feature-rich web-applications which are easy to deploy and maintain. Butterfly Data has a team of highly capable cloud migration specialists keen to help our customers adopt cloud frameworks securely and effectively. With our extensive experience in migrating data and processes to cloud infrastructure, we can help you plan your transition to the cloud to minimize cost and maximize opportunity.
FEATURES
Legacy system analysis as preparation for replacement and migration
Migration strategy, risk mitigation, and platform selection
Source-to-Target mappings to document the existing and required data structures
Data exploration to agree necessary data transformations and business rules
Transform, filter, extract, and load (TFEL) scripts and processes
Testing data feeds for integrity, quality, and usability
Metadata outputs and reports for each data source
Pilot migration to validate business impacts
Support for deployment to production and the decommissioning of legacy feeds
SAS Analytics platform built on AWS
BENEFITS
Source systems remain fully operational during the migration
Supports homogenous migrations such as Oracle to Oracle
Supports heterogeneous migrations between platforms, e.g. Oracle to Amazon Aurora
Saving on license fees and infrastructure costs
Supports most widely used databases, Schema Conversion Tool (SCT) available
SCT automatically converts most views, stored procedures, and functions
Multi-phase approach, including development and testing for production
Full warehouse migration and database consolidation
Addition of auto-scaling and geo-resilience on target architecture
Planning
Planning Cloud adoption projects (particularly for large complex organizations) can be very challenging. Several departments may be at various adoption stages, and often have competing priorities.
Legacy system dependencies and workarounds are not always compatible and it will likely be necessary to catalogue and evaluate any existing infrastructure, prior to any Cloud considerations. A like-for-like solution will not likely deliver the long-term benefits of commodity computing, which requires a detailed understanding of resource utilization (including projections).
Butterfly’s planning service builds on the trusted CRISP-DM methodology, to provide our clients with practical business analysis and cloud architecture support.
We offer tailorable support to most effectively assist you in planning a cloud adoption project using cloud-first technologies, including:
Defining your objectives, capabilities, and refining your business case
Assessing the challenge, identifying needs, dependencies, and options
Preparing for change, engaging users, and managing stakeholders
Developing effective solutions, proof of concepts, and viable products
Evaluating options for optimal suitability and effectiveness
Deploying change efficiently, migrating data and models
Monitoring results and decommissioning legacy solutions
We are practiced in Agile project delivery, and recognize the need to balance value and control with flexibility, without compromising on our Service Standards.
A typical cloud adoption plan would include the following sections:
Define
Agree business objectives, requirements, and scope
Establish acceptance and success criteria
Determine domain knowledge and common terminology
Define data access, sharing, and security principles
Draft high-level deliverables and a project plan
Capture any assumptions and constraints
Identify risks and appropriate mitigation strategies
Estimate costs and anticipated benefits
Assess
Identify the data to be used for development
Explore and profile the data to improve understanding
Highlight data quality issues and remediation options
Consult with key downstream analytics and reporting users
Document the existing and expected data structures
Produce a high-level solution and/or design
Prepare
Determine data selection & exclusion criteria
Perform any necessary data cleansing, remediation & enhancement
Determine any necessary data transformation logic
Integrate, merge, and aggregate disparate data
Develop
Explore development techniques, assumptions & parameters
Agree validation strategy and identify test data
Extract domain insights (feature engineering)
Build the models, data pipelines, and necessary interfaces
Refine for accurate and consistent results
Evaluate
Perform testing, validation, and benchmarking activities
Review the results against the business objectives
Obtain feedback from users and stakeholders
Evaluate options and agree next actions
Seek approval for implementation
Deploy
Clarify implementation plan and procedures
Agree parallel running and/or phased switchover approach
Migrate any dependencies and deploy
Handover, training, and knowledge sharing
Monitor
Review results via operational reports and dashboards
Document maintenance plan and procedures
Obtain client feedback and write up final report