Identify and prioritize business drivers
Business drivers are the key factors that drive the business’s operations and decisions. They could be things like increasing revenue, improving customer satisfaction, or reducing costs. Identifying these drivers helps to align the data strategy with the business goals. 


Discussion of goals for data-driven decision making and strategy
This involves defining the objectives for using data in decision making and strategic planning. The goals could include improving the accuracy of decisions, speeding up decision-making processes, or enabling new insights from data.  



Review of current strategy to inventory architecture
This involves evaluating the existing data strategy and the current state of the data architecture. It includes assessing the data sources, data quality, data governance, and existing technology infrastructure. The goal is to understand the strengths and weaknesses of the current strategy and architecture and identify areas for improvement.

Review of current data estate:  
This involves a comprehensive review of the organization’s existing data assets, often referred to as the “data estate”. It includes all the data that the organization owns, manages, and stores.

Audit of Processes:
An examination of the processes involved in data management within the organization. It includes data collection, storage, processing, and analysis. The goal is to identify any inefficiencies, redundancies, or gaps in these processes.

Data Sources:
Identification and evaluation all the sources from which the organization collects data. These could be internal sources like operational databases, or external sources like social media or third-party data providers. The reliability and relevance of these data sources are also assessed.

Confidence in Data Quality:
This refers to the level of trust that the organization has in the accuracy, completeness, and consistency of its data. It involves assessing the data quality management practices of the organization and identifying any issues that might be affecting data quality.

Review of reporting, analytics, data sources like APIs, & other consumption points

Review of Reporting: An evaluation of the current reporting practices within the organization. It includes assessing the types of reports generated, the frequency of reporting, the stakeholders who receive the reports, and the effectiveness of these reports in supporting decision-making.

Analytics: This refers to the methods and tools used by the organization to analyze its data and derive insights. The review would assess the analytical capabilities of the organization, including the use of statistical analysis, predictive modeling, machine learning, and other advanced analytics techniques.

APIs: The review would identify and evaluate the APIs that the organization uses to source data.

Other Consumption Points: These are the various ways in which the data is consumed within the organization. It could be through dashboards, data visualization tools, business intelligence platforms, etc. The review would assess these consumption points to understand how data is being used and the value itis providing to the organization.

Acknowledge Governance Roles: involves recognizing and defining the responsibilities of various roles in the data governance framework such as:

Data Owners: These are typically senior executives who have overall responsibility for the data. They make decisions about the data’s use and ensure it supports the organization’s goals.

Data Stewards: They are responsible for the quality and use of the data. They define data standards, ensure data quality, and handle data issues.

Data Custodians: They handle the technical aspects of data storage, security, and access. They ensure that data is stored securely and is accessible to those who need it.

Data Users: They are the end users who use the data for various purposes like reporting, analysis, decision making etc. They must understand their responsibilities in using the data ethically and correctly.

Data Governance Committee: This is a group of people, often made up of data owners, stewards, and other stakeholders, who oversee the data governance program. They set the policies and procedures and ensure they are followed.

Assess technical landscape of current Data Estate

Data Storage and Management: Review the systems used for data storage and management. This could include databases, data warehouses, data lakes, and cloud storage solutions.

Data Processing: Evaluate the tools and technologies used for data processing. This could involve ETL (Extract, Transform, Load) tools, data cleaning tools, and more.

Data Analysis and Reporting: Assess the solutions used for data analysis and reporting. This could include Business Intelligence (BI) tools, data visualization tools, and statistical analysis software.

Data Security and Privacy: Review the measures in place for data security and privacy. This includes access controls, encryption, anonymization techniques, and compliance with data protection regulations.

Data Integration: Evaluate the methods used for data integration. This involves assessing how data from different sources is combined and made consistent.

Data Governance: Review the technologies supporting data governance. This includes tools for data cataloging, metadata management, data quality management, and more.

Assessed current state and discussed future state

Requirements to move forward:

Data Requirements: A description of the data needed to support the business objectives. This includes a description of the type of data (structured, unstructured), the source of the data (internal, external), and the timeliness of data (real-time, batch).

Technological Requirements: A description and proposed architecture of the infrastructure needed to support the data strategy. This includes data storage, data integration, data analysis, and reporting tools.

Skills and Capabilities: A summary of the skills and capabilities needed within the organization to implement and manage the data strategy.

Implementation Plan: Develop a roadmap for implementing the data strategy. This includes a definition of the projects, timelines, resources, and milestones.

Impact analysis for proposed data strategy to move forward:

Business Impact: Assessment of how the new data strategy will affect business operations and outcomes. This could include improvements in decision-making, increased efficiency, cost savings, etc.

Technical Impact: A description of the changes required in the technical infrastructure. This could involve new data storage systems, data processing tools, analytics software, etc.

Organizational Impact: A description of how the new strategy will affect the people in the organization. This could involve changes in roles and responsibilities, need for training, changes in workflows, etc.

Risk Assessment: An indication of the potential risks associated with the new strategy. This could include data security risks, compliance risks, implementation risks, etc.

Cost-Benefit Analysis: An evaluation of the costs of implemention (including technology costs, training costs, etc.) versus the expected benefits (such as improved efficiency, better decision-making, etc.)