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DDI can be defined as a series of innovation processes that apply techniques (such as big data analytics) and technologies (e.g., machine learning, deep learning, AI) to extract meaningful value from data to generate innovative results.
Data-driven innovation (DDI) is the use of data and analytics to develop or foster new products, processes, organizational methods and markets. Data and analytics can drive both the discovery and execution of innovation, achieving new business models, products and services with a confirmed business value.
The Data-Driven Innovation (DDI) Framework systematically addresses the challenges of identifying and exploring data-driven innovations. It guides start-ups, entrepreneurs and established companies alike in scoping promising data business opportunities by analyzing both the dynamics of supply and demand.
Risk Mitigation: A data-driven approach can help identify potential risks and threats before they escalate. Predictive analytics and data modeling can forecast market shifts, operational risks, or potential financial downturns giving your organization the opportunity to take proactive measures.
Data-driven innovation (DDI) is a strategic approach that leverages the power of data (whether its big data or small data) to facilitate better decision-making and drive advancements within organizations.
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