How do you leverage data and analytics to inform your design thinking decisions?
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The first stage of design thinking is to empathize with your users and understand their needs, motivations, challenges, and behaviors. Data and analytics can help you to gain a deeper and more objective understanding of your users by providing quantitative and qualitative insights. For example, you can use data from surveys, interviews, observations, analytics tools, social media, or customer feedback to identify user segments, personas, pain points, goals, and emotions. You can also use data visualization techniques to communicate and share your findings with your team and stakeholders.
The second stage of design thinking is to define your problem statement and scope based on your user empathy. Data and analytics can help you to narrow down and prioritize your problem by providing evidence and validation. For example, you can use data to measure the impact, urgency, and feasibility of your problem, as well as to identify the root causes, assumptions, and constraints. You can also use data to benchmark your current state and set your desired outcomes and success criteria.
The third stage of design thinking is to generate as many ideas as possible to solve your problem and meet your user needs. Data and analytics can help you to inspire and evaluate your ideas by providing insights and feedback. For example, you can use data to discover existing or emerging trends, best practices, gaps, or opportunities in your domain or industry. You can also use data to test and refine your ideas by applying analytical methods such as SWOT analysis, cost-benefit analysis, or scenario analysis.
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ChatGPT is a great tool to help with ideation! Entering basic requirements/needs/goals into generative AI tools can unleash an entire perspective you never thought of!
The fourth stage of design thinking is to turn your ideas into tangible and testable prototypes that can elicit user feedback. Data and analytics can help you to design and improve your prototypes by providing guidance and validation. For example, you can use data to inform your design choices and assumptions, such as the features, functionality, layout, or aesthetics of your prototype. You can also use data to measure and optimize the performance, usability, or desirability of your prototype.
The final stage of design thinking is to test your prototypes with real users and collect feedback and data that can inform your next iteration. Data and analytics can help you to learn and iterate from your testing by providing evidence and insights. For example, you can use data to evaluate the effectiveness, efficiency, and satisfaction of your solution, as well as to identify the strengths, weaknesses, opportunities, or threats. You can also use data to communicate and share your results and recommendations with your team and stakeholders.
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New ideas and ways are great and should become one of the tools people can chose from not a replacement for something that worked well.
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Most of these articles feel like generic word salad that the internet can do without. A better approach for these would be bullet points or at least something that has references to other authoritative bodies of knowledge otherwise we're just making way to inauthentic AI content that benefits no one.
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