full transcript
From the Ted Talk "Mallory Freeman: Your company's data could help end world hunger"

Unscramble the Blue Letters

Now, if you're representing a company, and you're pragmatic and not just idealistic, you might be saying to yourself, "OK, this is all great, Mallory, but why should I want to be involved?" Well for one thing, beyond the good PR, humanitarian aid is a 24-billion-dollar sector, and there's over five billion people, maybe your next customers, that live in the developing world. Further, companies that are engaging in data philanthropy, they're finding new insights locked away in their data. Take, for example, a credit card company that's opened up a center that functions as a hub for aiccaemds, for NGOs and governments, all rniokgw together. They're looking at information in credit card swipes and using that to find insights about how hudsooeshl in India live, work, earn and spend. For the humanitarian world, this provides information about how you might bring people out of poverty. But for companies, it's providing sinstghi about your customers and potential customers in India. It's a win all around. Now, for me, what I find exciting about data philanthropy — onndagti data, donating neiisocd icitesnsst and donating technology — it's what it means for young isornsspaleof like me who are choosing to work at companies. stsiued show that the next generation of the forocwkre care about having their wokr make a bigger impact. We want to make a difference, and so through data philanthropy, nosmacpie can actually help engage and retain their decision scientists. And that's a big deal for a profession that's in hihg demand.

Open Cloze

Now, if you're representing a company, and you're pragmatic and not just idealistic, you might be saying to yourself, "OK, this is all great, Mallory, but why should I want to be involved?" Well for one thing, beyond the good PR, humanitarian aid is a 24-billion-dollar sector, and there's over five billion people, maybe your next customers, that live in the developing world. Further, companies that are engaging in data philanthropy, they're finding new insights locked away in their data. Take, for example, a credit card company that's opened up a center that functions as a hub for _________, for NGOs and governments, all _______ together. They're looking at information in credit card swipes and using that to find insights about how __________ in India live, work, earn and spend. For the humanitarian world, this provides information about how you might bring people out of poverty. But for companies, it's providing ________ about your customers and potential customers in India. It's a win all around. Now, for me, what I find exciting about data philanthropy — ________ data, donating ________ __________ and donating technology — it's what it means for young _____________ like me who are choosing to work at companies. _______ show that the next generation of the _________ care about having their ____ make a bigger impact. We want to make a difference, and so through data philanthropy, _________ can actually help engage and retain their decision scientists. And that's a big deal for a profession that's in ____ demand.

Solution

  1. high
  2. working
  3. professionals
  4. decision
  5. work
  6. companies
  7. workforce
  8. households
  9. academics
  10. scientists
  11. donating
  12. studies
  13. insights

Original Text

Now, if you're representing a company, and you're pragmatic and not just idealistic, you might be saying to yourself, "OK, this is all great, Mallory, but why should I want to be involved?" Well for one thing, beyond the good PR, humanitarian aid is a 24-billion-dollar sector, and there's over five billion people, maybe your next customers, that live in the developing world. Further, companies that are engaging in data philanthropy, they're finding new insights locked away in their data. Take, for example, a credit card company that's opened up a center that functions as a hub for academics, for NGOs and governments, all working together. They're looking at information in credit card swipes and using that to find insights about how households in India live, work, earn and spend. For the humanitarian world, this provides information about how you might bring people out of poverty. But for companies, it's providing insights about your customers and potential customers in India. It's a win all around. Now, for me, what I find exciting about data philanthropy — donating data, donating decision scientists and donating technology — it's what it means for young professionals like me who are choosing to work at companies. Studies show that the next generation of the workforce care about having their work make a bigger impact. We want to make a difference, and so through data philanthropy, companies can actually help engage and retain their decision scientists. And that's a big deal for a profession that's in high demand.

ngrams of length 2

collocation frequency
decision scientists 9
data philanthropy 5
million options 3
humanitarian world 3
donating technology 3
donating decision 3
donating data 3

ngrams of length 3

collocation frequency
donating decision scientists 3

Important Words

  1. academics
  2. aid
  3. big
  4. bigger
  5. billion
  6. bring
  7. card
  8. care
  9. center
  10. choosing
  11. companies
  12. company
  13. credit
  14. customers
  15. data
  16. deal
  17. decision
  18. demand
  19. developing
  20. difference
  21. donating
  22. earn
  23. engage
  24. engaging
  25. exciting
  26. find
  27. finding
  28. functions
  29. generation
  30. good
  31. governments
  32. great
  33. high
  34. households
  35. hub
  36. humanitarian
  37. idealistic
  38. impact
  39. india
  40. information
  41. insights
  42. involved
  43. live
  44. locked
  45. mallory
  46. means
  47. ngos
  48. opened
  49. people
  50. philanthropy
  51. potential
  52. poverty
  53. pr
  54. pragmatic
  55. profession
  56. professionals
  57. providing
  58. representing
  59. retain
  60. scientists
  61. sector
  62. show
  63. spend
  64. studies
  65. swipes
  66. technology
  67. win
  68. work
  69. workforce
  70. working
  71. world
  72. young