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The Data Science Maturity Curve is a model that describes the stages through which an organization progresses as it adopts data science and analytics. It provides a roadmap for organizations to understand where they currently stand and what steps they need to take to fully leverage data science capabilities. The stages of the curve typically include:

  1. Ad Hoc: At this stage, an organization is just starting to explore data science. Data management and analysis are often manual and inconsistent, with no formal strategy or governance.

  2. Preparatory: The organization begins to develop a data strategy, including data collection, storage, and management protocols. There may be some initial data projects, but they are often isolated and not integrated into broader business processes.

  3. Formative: The organization starts to implement data science projects more consistently, often with a dedicated data team. Data-driven decision making starts to take root in some parts of the organization.

  4. Operational: Data science is integrated into daily operations and decision-making processes. The organization has a clear data strategy, governance policies, and a dedicated data science team. Data-driven insights are used regularly to drive business decisions.

  5. Mature: At this stage, data science is a core part of the organization's strategy and operations. The organization has a mature data infrastructure, strong data governance, and a culture of data-driven decision making. Advanced analytics, machine learning, and AI may be used to generate insights and automate processes.

  6. Transformative: In the final stage, the organization is fully data-driven, with data science providing strategic direction. Data is used to drive innovation, and the organization is able to quickly adapt to changes based on data-driven insights.

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