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Big Ways Data Can Play a Role in Your Relocation Program

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Uncover the true meaning behind our favorite buzzword: big data. Here, we'll break down this term and give you ways to turn it into something manageable. (Learn even more in our eBook: http://resources.urbanbound.com/reasons-data-should-play-a-role-in-your-relocation-program)

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Big Ways Data Can Play a Role in Your Relocation Program

  1. 1. DATA CAN PLAY A ROLE IN YOUR RELOCATION PROGRAM POWERED BY BIG WAYS slideshare
  2. 2. BIG DATA.
  3. 3. we’ve all heard this phrase tossed around.
  4. 4. we’ve all heard this phrase tossed around. ...a lot.
  5. 5. the problem is:
  6. 6. we toss it around so much...
  7. 7. ...it’s hard to know what it really even means
  8. 8. WE’RE GOING TO CLEAR THAT UP
  9. 9. and show you how to use data properly.
  10. 10. and show you how to use data effectively.
  11. 11. and show you how to use data strategically.
  12. 12. and show you how to use data internally.
  13. 13. and show you how to use data externally.
  14. 14. and show you how to use data cross-departmentally.
  15. 15. ready to dive in?
  16. 16. the evolution of visual data
  17. 17. your first experience with probably looked something like this... visual data
  18. 18. this is a fine way to represent data.
  19. 19. we get the overall idea.
  20. 20. BUT.
  21. 21. BUT. what if.
  22. 22. BUT. what if. we made something like this instead...
  23. 23. gets the point across a little faster, doesn’t it?
  24. 24. it’s important not to get carried away here
  25. 25. it’s important not to get carried away here otherwise, you’ll end up with a chart or graph that looks like this:
  26. 26. this, as we can see, is not an effective way to display information
  27. 27. Don’t let this deter you from using visual representations to show data, though!
  28. 28. Don’t let this deter you from using visual representations to show data, though! Just make sure you choose the method that makes the most sense and conveys your message the clearest
  29. 29. MAKE IT EASY TO UNDERSTAND
  30. 30. MAKE IT EASY TO UNDERSTAND what we mean: it’s important to pick a chart that gets your point across in the quickest way possible—take these two charts for example...
  31. 31. Source: HubSpot
  32. 32. For the sake of understanding the information quickly, the first chart (while it may look nice) takes far too long to comprehend. By using an approach like the second graph instead, you’re able to quickly grasp on to the message and spend more time analyzing the information instead of decoding it.
  33. 33. USE A GOAL LINE
  34. 34. USE A GOAL LINE what we mean: a goal line gives a point of reference for the story you’ re trying to tell—see how much easier it is to understand the second graph in the following image?
  35. 35. Source: HubSpot
  36. 36. It’s hard to even see what you’re looking at in the first graph. Is the data indicating something above average? Below average? By using a goal line like in the second graph, you can quickly identify what the goal was and how it compares to the data. In this case, the data is climbing at a pace above average.
  37. 37. all organizations should be using these types of methods.
  38. 38. in fact...
  39. 39. 117 49.5 “top performing organizations are to use data to make decisions” 5x MORE LIKELY 17 49.5 - Harvard Business Review 17 49.5
  40. 40. see?
  41. 41. see? it’s important!
  42. 42. You don’t have to figure it out all on your own, though.
  43. 43. You don’t have to figure it out all on your own, though. In fact, roles that specialize in data analytics are becoming more and more popular.
  44. 44. 1,736 OPENINGS $116,840 MEDIAN SALARY DATA SCIENTIST + Source: Glassdoor
  45. 45. Historically, HR teams used data in a surface level way. With the uptick in data scientist and statistician roles, we are light years ahead of that now.
  46. 46. HR & Recruiting teams recognized an opportunity to expand their wheelhouse, growing the distance of how far their data can reach and the different teams it can affect. They want to use data to help businesses grow, and in order to do that, they need a strong comprehension of what data makes that possible and how to extract and present it.
  47. 47. which brings us to our next point...
  48. 48. strategies for using data contextually
  49. 49. VISUALS WE PROCESS60,000 TIMES FASTER THAN TEXT
  50. 50. TIME INFORMATION PROCESSED visuals text
  51. 51. But what good are visuals if the message doesn’t make sense?
  52. 52. ENTER: CONTEXT
  53. 53. CONTEXTUAL DATA what we mean:
  54. 54. CONTEXTUAL DATA what we mean: we can collect piles upon piles of data, but if we don’t analyze it and use it in the right context of what we are trying to understand or for whom we intend the data to impact, the data will just sit there collecting, ultimately becoming overwhelming instead of useful.
  55. 55. let’s take relocation for example.
  56. 56. Say you want to use data to help your transferees figure out where they should live and how they should get to work.
  57. 57. Say you want to use data to help your transferees figure out where they should live and how they should get to work. You survey your employees, analyze the data and then compile it into a report.
  58. 58. 17 49.5 17 49.5 like this!
  59. 59. After surveying your employees, you learn that 88.2% of employees live in chicago, and the rest are spread out between 4 other suburbs.
  60. 60. After surveying your employees, you learn that 88.2% of your employees live in Chicago, then a smaller percent are spread out between 4 other suburbs. That’s helpful information, but now let’ s add some context.
  61. 61. who actually took this survey?
  62. 62. 17 49.5 17 49.5 department, age, and salary, will have an impact on where you decide to live
  63. 63. Let’s take the other example now— using data to show how employees get to work.
  64. 64. 17 49.5 17 49.5 it’s pretty clear that most people walk to work, and right behind that is public transportation.
  65. 65. Sure, this is great information to know— most people walk or take some form of public transportation to work. But, how useful is it to know that most people walk to work, if all we know is that they live somewhere in Chicago?
  66. 66. chicago is a pretty big place, after all...
  67. 67. Let’s go back and add some context to this information, and find out exactly where in Chicago people live
  68. 68. 17 49.5 17 49.5 now we know most people live in lakeview or gold coast, meaning : If you want to walk or take public transportation, one of these neighborhoods is the place to live.
  69. 69. Let’s segment this information even further and find out more about the commute.
  70. 70. Let’s segment this information even further and find out more about the commute. for example...
  71. 71. how much time does it take you to get to work?
  72. 72. how much time does it take you to get to work? (because no one wants to spend 2 hours walking to work everyday)
  73. 73. 17 49.5 17 49.5 from this data segmentation, we know the average commute takes about 16-30 minutes
  74. 74. Now let’s take a step back and make some assumptions about what we learned.
  75. 75. Now let’s take a step back and make some assumptions about what we learned. There’s a pretty good chance that: If an employee lives in Lakeview or Gold Coast, they’ll take either public transportation or walk to work, and that commute will take somewhere 16-30 minutes.
  76. 76. Now let’s take a step back and make some assumptions about what we learned. There’s a pretty good chance that: If an employee lives in Lakeview or Gold Coast, they’ll take either public transportation or walk to work, and that commute will take somewhere 16-30 minutes. By looking at the neighborhoods on a map in comparison to the office, it’s easy to start connecting the dots between which method of commute aligns with which timeframe
  77. 77. see how helpful a few additional data points can be?
  78. 78. ONE LAST THING TO REMEMBER:
  79. 79. WE’VE EVOLVED TO CONSUME bite-sized PIECES OF DATA
  80. 80. WE’VE EVOLVED TO CONSUME bite-sized PIECES OF DATA because we are constantly switching between tasks
  81. 81. LET THE NUMBERS SPEAK FOR THEMSELVES:
  82. 82. 40% of people abandon a website that takes more than 3 seconds to load 17% of internet page views last less than 4 seconds Only 4% of page views last longer than 10 minutes On a page with 111 words or less, less than half of the words get read
  83. 83. Keep it concise
  84. 84. Keep it concise Keep it visually appealing
  85. 85. Keep it concise Keep it visually appealing Keep it seg men ted
  86. 86. data is one of our most powerful tools.
  87. 87. USE IT.
  88. 88. POWERED BY:

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