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We propose a framework as part of our Data Strategy Service offerings.
As and where applicable, a scoring matrix is thus developed based on Criteria, Weightage, and Scores
1. Readiness and Gap Analysis
An assessment of your current data capabilities, comparing them against industry best practices and your organizational goals. This is best understood via working through a questionnaire comprising of the following 5 areas and questions within each of these areas.
These areas are:
Alignment between organization data consumers and producers
Data initiatives driven by business users’ understanding and expectations with clear definitions of roles, ownership, and accountability.
Extent of domain knowledge and specialization both within IT and business teams.
Organizational factors that influence the adoption of technology and building the necessary skills.
Technology dependence and its role in shaping the current data architecture both from a logical and physical standpoint.
2. Data Platform Assessment
Examining your existing data architecture and performing gap analysis with the intent of building and optimizing usage of your desired modern data platform ecosystem.
Map your present data ecosystem to the desired state along areas that include but not confined to Data Storage, Compute, Data Pipelines Automation, Data ingestion techniques, Data Integration and ETL\ELT processes, DevOps, Access Controls and Security, and Overall Logical Architecture.
Evaluate cloud specific capabilities such as Data Migration, Storage, and Services (Azure Functions, AWS Lambda) that can augment data modernization efforts.
Assess existing data technology stack and Propose a Solution Architecture that conforms to your analytical requirements and accounts for your current data sources, storage systems, and analytics tools.
Perform a Technology Assessment using a build or buy approach by performing a cost-benefit analysis for different technology choices and assess vendor offerings and their alignment with your needs and which need to be integrated into the chosen cloud data platform of choice.
Consider factors like scalability, maintenance, and total cost of ownership and provide unbiased recommendations based on your unique requirements.
3. Analytical Use Cases Development
Build a prioritized list of analytical requirements based on KPIS, Subject Matters, Metrics, Business Benefits, and Domain Ownership.
This accounts for impact along four different Weighted Criteria, each of which can be scored:
4. De-centralized Domain based approach for Data Access and Security
Design and Implement a Persona based domain-ownership approach for data access and security, with the role of each personas explained below:
A. Business Data Domain Owner:
Ensures Enterprise business processes drive data into business value by:
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Specifying and sharing business (quality) demands with data owner.
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Making decisions on access, creation, update and deletion rights.
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Ensuring data quality levels are adhered.
B. Domain Data Steward:
Provides guidance to Enterprise business on interpretation and implementation by:
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Implementation of domain data principles, policies and procedures.
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Coordination between different data producers and users within a data domain.
C. Domain Data Product Owner:
Enables planning and delivery of data products via:
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Efficient Business and IT stakeholders’ expectations management.
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Efficient Agile DevOps practices.
D. Data Platform Stewards:
Enable data driven trusted insights by:
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Efficient data management ensuring re-use of building blocks.
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Provisioning trusted and secured data for analytics at scale supported by DevOps/DataOps/MLOps.
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Adapting “Infrastructure as code” principles towards data platform infrastructure and security management.
E. Analytical Engineers, Data Scientists:
Produce and democratize insights across Enterprise business streams via:
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Managed & Self-service analytics.
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Agile and rapid data experimentation.
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Model development and operationalization.
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