S3 | Ep 33 | Design Thinking: Creating Value-Driven Data Products & Solutions with Micheline Casey, CDAO at Siemens Energy
Jun 20, 2023
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Micheline Casey, Chief Data & Analytics Officer at Siemens Energy, discusses using design thinking to create value-driven data products. They explore the challenges of the energy sector, applying design thinking to data analytics, and understanding customer needs for innovative data products.
Design thinking enables a customer-centric and agile approach to delivering value in data products and AI solutions.
The organizational structure plays a crucial role in facilitating effective design thinking by aligning stakeholders, defining roles, and fostering collaboration.
Deep dives
Applying Design Thinking in Data Products and AI Solutions
Design thinking is a problem-solving approach that involves understanding customer pain points and iteratively developing solutions that meet their needs. This approach can also be applied to data products and AI solutions, allowing for a more customer-centric and agile way of delivering value. By focusing on desirability (what customers actually want), viability (access to the necessary data), and feasibility (building and iterating on the solution), organizations can ensure that they are effectively solving the right problems and delivering meaningful outcomes. Design thinking provides a framework for businesses to bridge the gap between problem-solving and technical implementation, creating innovative solutions that drive value.
The Role of Organizational Structure in Design Thinking
Organizational structure plays a key role in enabling effective design thinking. It is important to have clear roles and responsibilities, ensuring that accountability and authority are assigned to the right people. This involves aligning business stakeholders with technical teams, defining interfaces, and establishing a shared understanding of roles and execution speed. While the specific organizational structure may vary across different companies and industries, the focus should be on bringing together the right people, fostering collaboration, and ensuring a common understanding of goals and objectives. Ultimately, organizational design should facilitate seamless integration between business and data/tech teams, allowing for effective execution of design thinking principles.
Modeling for Valuation in Data Analytics and AI
Economic modeling is crucial in quantifying the value and potential benefits of data analytics and AI initiatives. When creating a business case, it is important to consider both hard benefits and soft benefits. Hard benefits can be quantified and tied directly to financial outcomes, while soft benefits are more intangible, such as improved productivity or customer satisfaction. Additionally, organizations should explore the reusability of data and technological components across different use cases and business units. By estimating the potential demand and cost allocation for reusable assets, businesses can optimize their investments and maximize cost-effectiveness. Economic modeling provides insights into the financial impact and feasibility of data analytics and AI initiatives, helping organizations prioritize and allocate resources effectively.
The Importance of Aligning Design Thinking with Business Strategy
Design thinking should be closely aligned with the overall business strategy. This involves identifying the goals and objectives of the organization and ensuring that data analytics and AI initiatives are directly tied to those strategic priorities. By integrating the data strategy as part of the wider business strategy, organizations can ensure a shared vision and direction. This alignment allows for the effective allocation of resources, the prioritization of initiatives, and the development of a roadmap that leads to the desired outcomes. Design thinking should be woven into the fabric of the organization, guiding the decision-making process and driving value realization.
In Episode 33 of Season 3, of Driven by Data: The Podcast, Kyle Winterbottom is joined by Micheline Casey, Chief Data & Analytics Officer at Siemens Energy, where they discuss how to create value-driven data products using design-thinking, which includes;
Being the first CDO at the Federal Reserve
Working in Senior Data Leadership at household names such as Ford, Maersk and now Siemens Energy
The spin-off of Siemens Energy from Siemens and the challenges they’re tackling
How there are close to a billion people worldwide who are under-served or don’t have energy
How Data/AI is creating commercial value to the energy sector
The sustainable energy transition movement
The concept of design-thinking
How to use design thinking to create tangible business value
How to apply design thinking to create data products and artificial intelligence solutions
Why it allows us to speak the right language to business stakeholders
Why it allows you to get value iteratively and quickly
Some real-life case studies in creating innovative data products that create business value
The importance of slowing down to speed up
Connecting design thinking to Data Strategy
How design-thinking fits into the organisational design conversation
Economic modelling for valuation - hard benefits and soft benefits
The importance of designing for component reusability to allocate value
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