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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
Analytics 3.0
內容大綱
Those who study "data smart" companies believe that we've already lived through two eras in the use of analytics--we might think of them as "before big data" and "after big data"--and are entering a third. It represents a far-reaching resolve to apply powerful data gathering and analysis not just to a company's operations but also to its customer services and products. This strategic change in focus means a new role for analytics. Companies will need to recognize a host of related challenges and respond with new capabilities, positions, and priorities. Requirements will include: multiple types of data, often combined; a new set of management options; faster technologies and methods of analysis; embedded analytics; data discovery; cross-disciplinary data teams; chief analytics officers; prescriptive analytics; analytics on an industrial scale; and new ways of deciding and managing. These new capabilities can't be developed using old models for how analytics supported the business. The big data model was a huge step forward, but it will not provide advantage for much longer. Companies must once again fundamentally rethink how the analysis of data can create value for themselves and their customers.