Data Strategy

Vision and Value

We must establish a clear direction and understanding of how data will contribute to the company’s goals and create value. Business goals and data, therefore, should be in harmony. To achieve this, we must:

1. Understand what the company goals are concerning its data
2. Clearly articulate business goals that the data strategy will support
3. Quantify the expected value of the data strategy in terms of revenue, cost savings, and other relevant metrics
4. Establish a Data Governance Committee to oversee the development, implementation, and monitoring of the data strategy
5. Routinely communicate the progress of the data strategy to key stakeholders
6. Develop a data-driven culture by continually promoting the use of data in decision-making across all levels of the organization

People and Skills

To foster a culture that values data literacy and expertise across the company, we must:

1. Define the owner of data in the organization
2. Assess the current skills and knowledge of employees concerning data management and analytics, identifying both gaps and strengths
3. Identify the roles and responsibilities for data governance, analysis, and engineering
4. Develop a training and development plan to address any skills gaps
5. Create opportunities for employees to share their knowledge and expertise with others
6. Encourage a culture of continuous learning and experimentation with data

Operating Model

We must define the framework and processes for managing and leveraging data effectively throughout the company. To understand how teams are structured in the organization, as well as how the competencies are distributed, we must:

1. Define the processes and procedures for data collection, storage, processing, analysis, and use
2. Establish data quality standards to ensure the accuracy and integrity of data
3. Implement data access controls to protect sensitive information
4. Develop a data change management process to manage data definition and structure changes
5. Automate data-driven tasks to improve efficiency and reduce errors

Data Governance

Data Governance will help to drive trust in the data and the outputs from data practices. We must implement policies, procedures, and controls to ensure data quality, security, and compliance. We must:

1. Develop a data governance framework that outlines the policies, procedures, and roles for managing data
2. Define data ownership to ensure accountability for data quality and usage
3. Establish data classification standards to protect sensitive information
4. Implement data security controls to prevent unauthorized access, use, disclosure, modification, or destruction of data
5. Monitor and audit data access and usage to detect and prevent data misuse

Technology and Architecture

We must select and maintain the right tools and infrastructure to support data collection, storage, and analysis. Tooling and integrating tools into apps and services will ease the inputs and outputs of data throughout the development lifecycle. We must:

1. Assess the current technology infrastructure and identify any gaps or limitations
2. Select the proper data storage, processing, and analytics technologies to support the organization's business goals and data needs
3. Integrate data from different sources to create a unified view of the organization's data
4. Implement data virtualization and data lakes to provide flexible access to data
5. Develop a data quality management solution to ensure the accuracy and integrity of data

Roadmap

Developing a structured plan that outlines milestones that will achieve the company’s data-related objectives. We must:

1. Create a roadmap that outlines the steps and timelines for implementing the data strategy
2. Prioritize data initiatives based on their business impact and feasibility
3. Allocate resources to support the implementation of the data strategy
4. Regularly review and update the roadmap as business needs and technology evolve
5. Communicate the roadmap to stakeholders and track progress against milestones