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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
Big data and talent management: Using hard data to make the soft stuff easy
內容大綱
Our increasing capacity to collect, store, and analyze large volumes of data has changed the way in which organizational decision makers approach their work. The ability to accurately quantify variables that previously had been assigned to the gut instinct of grizzled veterans or subject to the wisdom of sages for interpretation can now be more objectively understood. The implications for organizational performance are clear: better data and better decisions yield better performance. In many functions, like marketing, this capability has resulted in a true revolution in how companies come to understand and most profitably serve customers. Other areas, such as talent management, have lagged behind in this regard. This is largely due to the fact that many of the relevant variables (e.g., personality) are difficult to measure. It is also because the relationship between these variables and organizational performance is not entirely understood. Recent developments regarding how we understand and then link individual characteristics and performance are enabling a data revolution in the area of talent management. Herein, we offer three examples that illustrate how data can now be used to improve talent management decisions and, ultimately, organizational performance.