Stage 1: Business Monitoring
Organizations leverage data warehousing and Business Intelligence to monitor the organization's performance
Stage 2: Business Insights
Leverage predictive analytics to uncover customer, product, and operational insights from the massive internal and external data sources. Organizations aggressively expand their data acquisition efforts by combining all of their detailed transactional and operational data with internal data such as consumer comments, e-mail conversations as well as external and publicly available data such as social media, weather, traffic, economic, demographics, home values, and local events data
Stage 3: Business Optimization
Organizations apply prescriptive analytics to the customer, product, and operational insights to the Business insights obtained, to deliver actionable insights or recommendations to frontline employees, business managers, as well as customers. Enables all stake holders to optimize their key decisions
Stage 4: Data Monetization
Organizations leverage the customer, product, and operational insights to create new sources of revenue (Selling Insights), integrating analytics into products and services to create “smart” products, or re-packaging customer, product, and operational insights to create new products and services, to enter new markets, and/or to reach new audiences.
Stage 5: Business Metamorphosis
Organization transitions its business model from selling products to selling “business-as-a-service.” Create a platform enabling third-party developers to build and market solutions on top of the organization's business-as-a-service business model
Big data only matters if it helps organizations make more money and improve operational effectiveness (increasing customer acquisition, reducing customer churn, reducing operational and maintenance costs, optimizing prices and yield, reducing risks and errors, improving compliance, improving the customer experience etc.)
Organizations leverage data warehousing and Business Intelligence to monitor the organization's performance
Stage 2: Business Insights
Leverage predictive analytics to uncover customer, product, and operational insights from the massive internal and external data sources. Organizations aggressively expand their data acquisition efforts by combining all of their detailed transactional and operational data with internal data such as consumer comments, e-mail conversations as well as external and publicly available data such as social media, weather, traffic, economic, demographics, home values, and local events data
Stage 3: Business Optimization
Organizations apply prescriptive analytics to the customer, product, and operational insights to the Business insights obtained, to deliver actionable insights or recommendations to frontline employees, business managers, as well as customers. Enables all stake holders to optimize their key decisions
Stage 4: Data Monetization
Organizations leverage the customer, product, and operational insights to create new sources of revenue (Selling Insights), integrating analytics into products and services to create “smart” products, or re-packaging customer, product, and operational insights to create new products and services, to enter new markets, and/or to reach new audiences.
Stage 5: Business Metamorphosis
Organization transitions its business model from selling products to selling “business-as-a-service.” Create a platform enabling third-party developers to build and market solutions on top of the organization's business-as-a-service business model
Big data only matters if it helps organizations make more money and improve operational effectiveness (increasing customer acquisition, reducing customer churn, reducing operational and maintenance costs, optimizing prices and yield, reducing risks and errors, improving compliance, improving the customer experience etc.)
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