Vin Vashishta and Tiffany Perkins-Munn delve into monetizing data and AI, emphasizing aligning initiatives with core business objectives, quick wins, and balancing short-term fixes with long-term vision. They discuss strategies for selling data sets, subscription services, and empowering employees at all levels, highlighting the importance of transparent AI models for trust and seamless integration into various fields.
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Quick takeaways
Monetizing data requires clear strategies, from selling data sets to subscription-based access, and leveraging consumer data for revenue.
Success in data initiatives stems from prioritizing business outcomes, fostering data-driven culture, addressing technical and strategic challenges comprehensively.
Aligning technology decisions with business objectives through a technical strategy aids in articulating value to customers, driving competitiveness, and informed decision-making.
Deep dives
Re-framing the Approach to Data and AI Success
Success in data and AI initiatives relies on prioritizing business outcomes over technical complexity, as simple strategies can often deliver significant value efficiently. To achieve ROI and monetize data efforts, organizations must address technical, organizational, and strategic challenges comprehensively. This holistic approach involves tackling technical issues, fostering a data-driven culture, investing in talent, ensuring regulatory compliance, and developing clear monetization strategies.
Importance of a Strategic Technical Framework
Developing a technical strategy is essential for aligning technology decisions with business objectives. By establishing a framework for decision-making, businesses can articulate how technology adds value to customers and facilitates growth and competitiveness. This approach emphasizes understanding the unique strengths of different technologies, such as data analytics, machine learning, and AI, to inform informed decisions collectively and drive progress more effectively.
Assessing Data and AI Maturity Levels
To enhance data and AI capabilities, businesses must evaluate their maturity level across data practices, governance, strategy alignment, and IT infrastructure. Assessing data maturity involves categorizing practices from ad hoc to optimized processes, while evaluating data governance ensures data quality, security, and alignment with business objectives. Additionally, ensuring IT infrastructure supports efficient data collection and analysis is crucial for scalability and flexibility in future growth endeavors.
Monetizing Data: Strategies and Opportunities
Monetizing data involves various strategies, from selling or licensing data sets to offering subscription-based access to premium data. Targeted advertising and marketing, data-driven innovation, and utilizing consumer data for product development are key avenues for revenue generation through data. Packaging existing data for sharing and using technology for data management are highlighted as ways to explore monetization opportunities.
Skills and Exciting Trends in Data and AI
Key roles in the age of AI, like AI product managers and data strategists, require skills in opportunity discovery, technology translation, and aligning with business goals. Explorable AI and moving towards knowledge engineering excite professionals, along with advancements in causal methods and robotics. The potential of robotics and accessibility to IoT data are anticipated to revolutionize industries, emphasizing the importance of leveraging technology as a lever for business advantage.
Everything in the world has a price, including improving and scaling your data and AI functions. That means that at some point someone will question the ROI of your projects, and often, these projects will be looked at under the lens of monetization. But how do you ensure that what you’re working on is not only providing value to the business but also creating financial gain? What conditions need to be met to prove your project's success and turn value into cash?
Vin Vashishta is the author of ‘From Data to Profit’ (Wiley), the playbook for monetizing data and AI. He built V-Squared from client 1 to one of the oldest data and AI consulting firms. For the last eight years, he has been recognized as a data and AI thought leader. Vin is a LinkedIn Top Voice and Gartner Ambassador. His background spans over 25 years in strategy, leadership, software engineering, and applied machine learning.
Dr. Tiffany Perkins-Munn is on a mission to bring research, analytics, and data science to life. She earned her Ph.D. in Social-Personality Psychology with an interdisciplinary focus on Advanced Quantitative Methods. Her insights are the subject of countless lectures on psychology, statistics, and their real-world applications.
As the Head of Data and Analytics for the innovative CDAO organization at J.P. Morgan Chase, her knack involves unraveling complex business problems through operational enhancements, augmented financials, and intuitive recruiting. After over two decades in the industry, she consistently forges robust relationships across the corporate spectrum, becoming one of the Top 10 Finalists in the Merrill Lynch Global Markets Innovation Program.
In the episode, Richie, Vin, and Tiffany explore the challenges of monetizing data and AI projects, including how technical, organizational, and strategic factors affect your input, the importance of aligning technical and business objectives to keep outputs focused on core business goals, how to assess your organization's data and AI maturity, examples of high data maturity businesses, data security and compliance, quick wins in data transformation and infrastructure, why long-term vision and strategy matter, and much more.