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最新個案
- A practical guide to SEC ï¬nancial reporting and disclosures for successful regulatory crowdfunding
- Quality shareholders versus transient investors: The alarming case of product recalls
- The Health Equity Accelerator at Boston Medical Center
- Monosha Biotech: Growth Challenges of a Social Enterprise Brand
- Assessing the Value of Unifying and De-duplicating Customer Data, Spreadsheet Supplement
- Building an AI First Snack Company: A Hands-on Generative AI Exercise, Data Supplement
- Building an AI First Snack Company: A Hands-on Generative AI Exercise
- Board Director Dilemmas: The Tradeoffs of Board Selection
- Barbie: Reviving a Cultural Icon at Mattel (Abridged)
- Happiness Capital: A Hundred-Year-Old Family Business's Quest to Create Happiness
An Art & A Science: How to Apply Design Thinking to Data Science Challenges
內容大綱
We hear it all the time as managers: "what is the data that backs up your decisions?" Even local mom-and-pop shops now have access to complex point-of-sale systems that can closely track sales and customer data. Social media influencers have turned into seven-figure solopreneurs from digital advertising analytics. In 2023, globally, we will create three times the data we did in 2019, and by 2025, it is estimated that 181 zettabytes of data will be generated (that is 181 followed by 21 zeros). Data is becoming a critical, arguably inextricable, part of business operations in our modern context. Data-driven decision-making (DDDM) uses data to inform decisions rather than relying on intuition. The digital era has given rise to the importance of data science for business applications. This technical note explores how different design thinking principles can assist the data-driven processes in a project.