AI Value Creation and Data Monetization Strategy Masterclass

Explore the differences between data, artificial intelligence, and large language models, and understand why data value creation matters for business, as illustrated by ChatGPT's rapid adoption.

Data is the new fuel, and its rapidly expanding landscape drives business, but value comes from managing the data lifecycle: acquisition, storage, processing, usage, and disposal.

Explore data value creation and personalization marketing to boost revenue and reduce acquisition costs, guided by McKinsey research; learn the strategy and framework to apply these use cases.

Define value by engaging stakeholders to capture their priorities across product, customers, employees, processes, branding, and ESG, then identify and measure value levers and prioritize actions.

Explore data value creation strategy and a four-quadrant framework mapping internal and external data quality to business drivers, with operations optimization and use cases such as visual analytics and chatbots.

Drive organizational transformation with data quality by applying three use cases: product offerings, customer lens, and decision making, using data insights and AI to optimize pricing, incentives, hyper-personalization, and engagement.

Sell raw data to third parties, often with low value due to poor quality, and pursue aggregated, anonymized data for advertisers and data brokers after starting with internal monetization.

Explore incremental data monetization through insights as a service, data marketplaces, and ad tech, leveraging high data quality external data to deliver business-to-business 360 data insights and targeted campaigns.

Demonstrate how external and internal datamatization, along with internal operation and business transformation, drives AI value creation while ensuring data quality, governance, and cross‑team collaboration to enable compliant data monetization.

Learn how the digital core, the operating model, and the value creation and realization layer drive transformation, with cloud-based applications and data as the backbone.

Apply the data value creation business driver framework to map internal and external data monetization, raw/aggregated and incremental, into four drivers: cost reduction, compliance, revenue uplift, and new revenue streams.

Align data value creation with business strategy. Assess the state using a maturity framework; apply fit-gap analysis and a roadmap to mature data and AI capabilities for sustainable value.

Explore a data value creation maturity framework that maps as-is state to a roadmap, from descriptive insights to preventive actions across five levels and two dimensions.

Master the six critical stakeholder interview questions to assess data quality, usage, technology, talent, and future use cases, shaping a clear as-is to to-be data value roadmap.

prioritize data value creation use cases with a one-pager backlog template that ties to business objectives and stakeholders, detailing why, what, how, and who, and presenting the business case.

Apply a data value prioritization mechanism that scores use cases on business strategy and impact, converts ratings to points, ranks them, and plans workshops with senior management.

Develop a consolidated data value creation business case by compiling market research and stakeholder input into a storyline, outlining cost, benefits, return on investment, and transparent assumptions for financial modeling.

Craft a data value creation storyline by outlining a four-stage roadmap—visual analytics, product analytics, product rationalization, and customer reactivation—aligned with maturity levels from descriptive to prescriptive, enabling proof-of-concept and scale-up.

Develop a data value creation roadmap with a proof-of-concept and industrialization, outlining workshops, budgeting, timelines, and parallel data use cases from concept to rationalization and customer reactivation.

Review the data value creation framework and use cases for artificial intelligence value creation and data monetization, linking internal/external dimensions, data quality, and a roadmap to a compelling business case.

Conclude your course by starting a data value creation journey for your organization and cultivating a data value creation mindset in your work, especially in the context of AI evolution.