This content originally appeared on DEV Community and was authored by Bala Madhusoodhanan
Data Strategy
A data strategy is a plan that outlines how an organization will collect, manage, analyze, and use data to achieve its business objectives. The framework would help organization in making informed decisions about how to acquire, store, process, and share data in a way that supports its business goals.
Solid foundation for your data future would have comprehensive plan ensuring the below to enable more success in leveraging the full potential of their data assets.
| Theme | Description |
|---|---|
| 1. Data governance | The policies, standards, and procedures that ensure the accuracy, consistency, and security of data across the organization. |
| 2. Data architecture | The technical framework for how data will be stored, managed, and accessed within the organization, including databases, data warehouses, and other data management systems. |
| 3. Data quality | The processes and tools used to ensure the accuracy, completeness, and consistency of data, including data profiling, data cleansing, and data enrichment. |
| 4. Data integration | The processes and tools used to combine data from multiple sources and transform it into a format that can be used by business applications and analytics tools. |
| 5. Data analytics | The tools and techniques used to extract insights from data, including descriptive, diagnostic, predictive, and prescriptive analytics. |
AI Strategy
An AI strategy for an organization is a plan for how to leverage artificial intelligence (AI) to achieve the organization's goals and objectives. Adoption of technology should be carefully managed as it comes with understanding the risks and challenges associated with as the technology is in emerging phase.
| Theme | Description |
|---|---|
| 1. Vision | Define the organization's vision for how AI can help it achieve its business goals and objectives. |
| 2. Goals and objectives | Define specific goals and objectives that the organization wants to achieve using AI, such as improving efficiency, reducing costs, or enhancing the customer experience. |
| 3. Data strategy | Define a data strategy that outlines how the organization will collect, store, and manage data in order to train and deploy AI models. |
| 4. Technology strategy | Define a technology strategy that outlines the tools and platforms that the organization will use to develop and deploy AI models, such as machine learning frameworks, cloud services, and data analytics tools. |
| 5. Governance and ethics | Define a governance and ethics framework that outlines the policies and procedures that the organization will follow to ensure that its use of AI is ethical, transparent, and compliant with regulatory requirements. |
Two Sides of the Same Coin
Data strategy involves collecting, managing, and analyzing data in a way that enables AI models to learn from it and make predictions with a high degree of accuracy.
By leveraging ANI (Artificial Narrow Intelligence) to automate data processing and analysis tasks, organizations can gain insights more quickly and efficiently than would be possible using traditional methods. This, in turn, can help organizations to make more informed decisions, identify opportunities for growth and improvement, and ultimately drive better business outcomes.
Data and AI are interdependent. A strong data strategy is the foundation upon which an effective AI strategy can be built, while AI can in turn help to enhance and refine data strategy over time
This content originally appeared on DEV Community and was authored by Bala Madhusoodhanan
Bala Madhusoodhanan | Sciencx (2023-02-28T17:00:33+00:00) Connecting the Dots: How Data Strategy Fuels AI Strategy. Retrieved from https://www.scien.cx/2023/02/28/connecting-the-dots-how-data-strategy-fuels-ai-strategy/
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