學門類別
哈佛
- General Management
- Marketing
- Entrepreneurship
- International Business
- Accounting
- Finance
- Operations Management
- Strategy
- Human Resource Management
- Social Enterprise
- Business Ethics
- Organizational Behavior
- Information Technology
- Negotiation
- Business & Government Relations
- Service Management
- Sales
- Economics
- Teaching & the Case Method
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
Why IT Fumbles Analytics
內容大綱
As managers seek to exploit the tremendous amounts of data now available from internal and external sources, they're likely to use the approach they use with all their IT projects--that is, they'll focus on building and deploying technology on time, to plan, and within budget. That works for projects designed to improve business processes and increase efficiency, but when it comes to extracting valuable insights from data and using information to make better decisions, managers need a different approach and mind-set. A big data or analytics project is likely to be smaller and shorter than a conventional IT initiative, such as installing an ERP system. It's also more like scientific research. Commissioned to address a problem or opportunity, such a project frames questions, develops hypotheses, and then experiments to gain knowledge and understanding. The authors have identified five guidelines for taking this voyage of discovery: 1) Place users--the people who will create meaning from the information--at the heart of the initiative; 2) Unlock value from IT by asking second-order questions and giving teams the freedom to reframe business problems; 3) Equip teams with cognitive and behavioral scientists, who understand how people perceive problems and analyze data; 4) Focus on learning by facilitating information sharing, examining assumptions, and striving to understand cause and effect; and 5) Worry more about solving business problems than about deploying technology.