Data Strategy, Data Management Strategy, AI Strategy, and Data Monetization Strategy: What are the differences?

No doubt data?is now the most valuable?asset within an organization. Therefore, over the past decade, many organizations have developed several strategies to collect and manage their data and generate value from it. Whether you work in IT, data, or business function, you may have heard about different strategies around data including data strategy, data management strategy, data monetization strategy, and AI strategy. As many of these terms are often misused and misunderstood, I will explain the differences and why organizations need it.

Data Strategy:

A plan to use information and data to competitive advantage and support enterprise goals. Data strategy defines the people, processes, and technology to put in place to solve data challenges and support business goals. In addition, it is the foundation of data practices toward data-driven culture and it answers questions such as what data the organization needs to support strategic business objectives, how it will collect the data and for what purpose, what technology it needs to support this, how it will manage it and ensure its reliability over time, how it uses data to make better strategic decisions. Data strategy shall include:

• A vision, mission, clear goals, and measurable objectives for data strategy
• Business objectives you plan to achieve
• Economic benefits of implementing a strategy
• Competitor analysis
• Measures of data initiatives success
• Tools/systems/ infrastructure to store and manage data
• Organizational roles
• A data strategy roadmap with projects and initiatives

Data Management Strategy:

A plan for maintaining and improving data quality, data integrity, access, and security while mitigating known and implied risks/challenges. A data management strategy is required to support the Data strategy, and it is owned by the CDO and enacted through a data governance team. Data management strategy shall include:

• A vision, mission, and long-term directional goals for data management
• A summary business case for data management
• Guiding principles, values, and management perspectives
• Measures of data management success
• Short-term (1-2 years) Data Management program objectives
• Descriptions of data management roles and organizations, along with a summary of their responsibilities and decision rights
• Descriptions of Data Management program initiatives
• Change Management
• Implementation roadmap with projects and action items

Data Monetization Strategy:

A plan to generate economic value from data. In other words, it is turning data into money. There are two ways organizations can make money from data:

Direct Monetization (Externally): Where the organization passes the charge directly to the customer by providing access to the insights by:

• Selling analysis and insights (Dashboard or report subscription)
• Offering AI Model as a service (via API) based on a chargeable fee per prediction
• Embedded analytics in a product, on a monthly or annual subscription fee
• Selling minimal limited data (I don't recommend it)

Indirect Monetization (Internally): Where the organization uses insights/analytics to improve business services & operations and reduce cost. Here are examples of potential opportunities:

• Minimize customer churn
• Detect and prevent fraud and risks
• Reduce operational cost
• Improve efficiencies and productivity for processes and operation
• Increase customer retention
• Optimize sales and marketing activities
• Enhance existing services or products
• Feed AI and RPA to automate processes
• Identify bottlenecks in processes and services to reduce time

AI Strategy:

A plan with a vision, mission, and strategic goals for developing and implementing AI?and ML capabilities within an organization to automate processes, improve customer experience, reduce service time, enhance existing services, optimize?operation, segment, and predict customer behavior. AI strategy shall include:

• Vision, mission, and objectives
• Identified use cases for implementing AI
• A detailed description of use cases
• Calculated ROI for each use case
• AI maturity assessment (culture, people. processes, data, governance, policies, ?skillset)
• Data availability assessment
• Roadmap for implementation (2- 4 years)

Summary

Organizations need different strategies around their data to fulfill several needs:

• What data the organization needs to support business and how to collect and manage it (Data Strategy)
• How to manage/enrich data and resolve its issues (Data Management Strategy)
• How to generate value from data and develop data products (Data Monetization Strategy)
• How to utilize AI technology and capabilities to support/improve business ?(AI Strategy)