Council Post: Relevance of applying design thinking principles in AI and Analytics
For more than a decade, tech giants like Apple, Netflix, Microsoft, IBM, Google and LinkedIn have been tapping design thinking principles– backed by analytics– to build a solid ecosystem to stay ahead of the competition and scale exponentially. For those unaware, design thinking is a customer-centric approach to build innovative solutions and profitable and sustainable products. Design thinking leans on empathetic observation of how people communicate/respond with their environments, and follows an iterative, hands-on approach to create innovative products and solutions. The design principles consist of five stages – empathise, define, ideate, prototype and test. Design thinking principles (Source: Interaction Design Foundation) Today, design thinking is being taught at top universities, including MIT, Harvard and Stanford. The tech is used across manufacturing, retail, financial services, and telecom. Many companies have also started to realise the importance of design thinking combined with big data analytics to facilitate a customer-centric approach. For instance, Apple Watch uses big data analytics to keep track of people’s health, lifestyle and fitness. Netflix uses design thinking principles combined with advanced AI and data analytics to provide personalised movies and TV series recommendations. Analytics has played a critical role in shaping design thinking. It utilises a large amount of customer data to create personalised solutions–one of the core aspects of design thinking. AI and analytics Thanks to data analytics, the world of design thinking has evolved significantly. But what about the flip side? Do we adequately use design thinking principles (empathise, define, ideate, prototype and test) in analytics and AI engagements? Currently, design thinking strategy is being used by teams to solve business problems, develop customer-centric products, and scale business. Design thinking teams are open-minded, curious, collaborative, always ready for change, and adaptable. This, overall, creates a great work culture, and increases productivity. AI and data analytics teams that leverage design thinking principles to deploy machine learning models and algorithms can drive higher value from analytics engagements. The benefits of design thinking, when applied to AI and analytics, include: Elevates the thought process from being only transactional to a strategic viewHelps in developing an innovative team culture, which embraces inclusiveness, collaboration, and co-creation Allows for a diverse set of perspectives to influence the process (set up AI council of experts)Brings better AI governance into the processDevelop bias-free algorithms and models Ensures the right person is in charge Leads to faster deployment of models Increases the accuracy of the modelHelps in building AI at scale Encourages the team to use new algorithms, models and analytics tools In conclusion We need to acknowledge that applying design thinking principles will foster better throughput for AI & analytics engagement, and help increase the uptake of analytics engagements. The scope for design thinking can be varied, from building visibility on simple reporting metrics to solving a complex real-world drug discovery engagement or identifying fraud waste & abuse in multiple industries. As they say, the “days of the lone inventor are over”. With design thinking at play, organisations can focus on being “significantly outside in, highly collaborative & truly embedded. Hopefully, we should see some amazing advancement and innovation in the coming months by implementing design thinking principles in the AI and analytics space — similar to the product and service ecosystem. This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the form here.
Applying design thinking principles will foster better throughput for analytics engagements, drive higher value and help increase the uptake of analytics engagements.
Applying design thinking principles will foster better throughput for analytics engagements and drive higher value.
[]
["big data", "data science", "Go", "API", "machine learning", "AI", "RAG", "Aim", "analytics", "R"]
["AI", "machine learning", "data science", "analytics", "Aim", "RAG", "R", "Go", "API", "big data"]
Hari is an executive leader with a 25-year track record working for successful, high-growth companies in the fields of Analytics and Data Science. With a strong focus on client imperatives, Hari has inspired capable teams to push beyond their comfort zones and deliver meaningful business outcomes powered by state-of-the-art Analytics and Data Science practices.