How Analytics can help Startups Build Products – A 101 guide
Amongst the hype of GenAI, I wanted to stress that you don’t need to bring in complexity when there is no need for an LLM.
So what is the role of data science in building products?
While it is easy for folks to conflate AI and data science in terms of model building, in essence, DS ‘reduces uncertainty’ and allows a company to compete through understanding the vast unstructured data across multiple data sources
Sequoia did a great write-up on Data informed Product building highlighting the below 4 outcomes
#1Evaluating the health of the business : Defining, monitoring, measuring and understanding product health, drivers and variations
#2Shipping the right products and features : DS helps drive multiple experiments to guide product teams on optimising for the product based on insights and ensuring the right features get built
#3Forecast Outcomes with Prototypes/ Production ML models : Support forecasting future expectations and trends with AI/ML. Ex – think identifying fraudulent activity using production systems
#4Drive Product Roadmap and Strategy : Understand user behavior and journey to help set effective product roadmap and strategy
Data informed decision making for startups in building products can be a huge competitive advantage and a differentiator, if set up correctly.
Are you effectively leveraging data science in your startup for product building?
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