Unicorns Are People, Too: Re-Thinking Soft and Hard Skills

3d65a0bc911de24fde5e58d84b0276af?s=47 Liz
August 22, 2014

Unicorns Are People, Too: Re-Thinking Soft and Hard Skills

As developers, we tend to value hard skills that can be quantified or measured objectively. Job postings search for unicorns, but we are people first and foremost and being human isn’t as easy as programming. While the code comes easily, the soft skills that make us human are complicated and difficult to get right. This talk will explore the danger of neglecting so-called “soft” skills, what we stand to lose by overvaluing technical skills, and alternatives to the hard and soft dichotomy.

3d65a0bc911de24fde5e58d84b0276af?s=128

Liz

August 22, 2014
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Transcript

  1. Hello !

  2. Unicorns ARE PEOPLE TOO. Re-Thinking Soft and Hard Skills Liz

    Abinante • @feministy
  3. SOFT VS. HARD WHAT ARE SOFT AND HARD SKILLS? Cheese

    by Consuelo Elo Graziola, Pillow by Yazmin Alanis, Hard Hat by André Renault, and Mountains by Cris Dobbins from The Noun Project
  4. SOFT VS. HARD HIDDEN MEANINGS

  5. SOFT VS. HARD A PROBLEMATIC DICHOTOMY

  6. POTENTIAL ALTERNATIVE TERMS Robot by William Hollowell and Talking by

    Ed Gray from The Noun Project
  7. REAL PEOPLE TIPS FOR TEACHERS, INTERVIEWERS & MANAGERS

  8. SOFT & HARD A problematic dichotomy.

  9. WHAT ARE SOFT SKILLS?

  10. communication friendliness team building influence persuasion conflict resolution

  11. PROJECT, TEAM, AND TASK MANAGEMENT. communication friendliness team building influence

    persuasion conflict resolution
  12. MANY EMPLOYERS ACTIVELY SEEK OUT SOFT SKILLS IN PROSPECTIVE EMPLOYEES,

    MAKING THEM NEAR REQUIREMENTS FOR SUCCESS IN THE WORKFORCE.
  13. None
  14. None
  15. BUT IF SOMETHING IS A NEAR REQUIREMENT, WHY ARE WE

    DESCRIBING IT AS “SOFT”?
  16. SOFT ! EASY TO MOLD, CUT, COMPRESS, OR FOLD; NOT

    HARD OR FIRM TO THE TOUCH
  17. SOFT ! HAVING A PLEASING QUALITY INVOLVING A SUBTLE EFFECT

    OR CONTRAST RATHER THAN SHARP DEFINITION
  18. mushy dim lenient easy goingcompassionate subdued silky gentle forgiving tolerant

  19. mushy dim lenient easy goingcompassionate subdued silky gentle forgiving tolerant

  20. “SOFT” IMPLIES THAT IT IS LESS IMPORTANT BY USING A

    WORD THAT ALSO MEANS THINGS LIKE “GENTLE”, “DIM”, AND “PLEASING.”
  21. I AM NOT A STUFFED ANIMAL. ! I AM NOT

    A CHEESE OR A FLUFFY PILLOW
  22. I am a person who likes working with other people.

  23. WHAT ARE HARD SKILLS?

  24. programming computation physical labor statistical analysis

  25. HARD ! SOLID, FIRM, AND RESISTANT TO PRESSURE; NOT EASILY

    BROKEN, BENT, OR PIERCED.
  26. HARD ! REQUIRING A GREAT DEAL OF ENDURANCE OR EFFORT.

  27. resistant solid unyielding arduous grueling strict severe violentaustere reliable rigid

  28. resistant solid unyielding arduous grueling strict severe violentaustere reliable rigid

    NOPE. No by Naomi Atkinson from The Noun Project
  29. THESE SYNONYMS SOUND MORE LIKE A LIST OF PERSONALITY TRAITS

    FROM A DATING SERVICE THAN A LIST OF JOB REQUIREMENTS.
  30. MY SKILLS ARE NOT VIOLENT, THEY ARE NOT STRICT OR

    SEVERE.
  31. It is not grueling to write code.

  32. WRITING CODE IS MENTALLY CHALLENGING AND REQUIRES TRAINING AND TOOLS.

  33. OUR TOOLS ARE WRONG. ! THEY DON’T MEAN WHAT WE

    THINK THEY DO. WE ARE FORCED TO INFER MEANING. Tools by Jeremy J Bristol from The Noun Project
  34. WE CAN INFER ALL WE WANT, BUT ! OUR TOOLS

    ARE BROKEN. Broken Light Bulb by Gregory Sujkowski from The Noun Project
  35. WE STRUGGLE WITH SKILLS THAT DON’T FIT INTO A PERFECT

    BOX.
  36. Code reviews are nebulous.

  37. A GOOD CODE REVIEW TAKES SOFT AND HARD SKILLS. !

    THEY REQUIRE TECHNICAL KNOW-HOW AND SUBJECTIVE, CONSTRUCTIVE ANALYSIS.
  38. SURPRISE!

  39. SURPRISE! COMMUNICATION PROBLEMS HAPPEN. A LOT.

  40. WE NEED TO MAKE NEW TOOLS. Broken Light Bulb by

    Gregory Sujkowski from The Noun Project
  41. Speaking of tools…

  42. None
  43. 483 JAVASCRIPT FRAMEWORKS

  44. wtf...?

  45. THIS IS BULL !

  46. We can build better.

  47. WE CAN’T THROW EVERYTHING AWAY, THOUGH. ! SORRY!

  48. HUMANS LIKE BLACK & WHITE.

  49. DICHOTOMIES MAKE THE WORLD EASIER TO PARSE. ! WELL, WE

    LIKE TO THINK THEY DO.
  50. DICHOTOMIES HELP US MENTALLY AUTOMATE INTERPERSONAL INTERACTIONS.

  51. AUTOMATION IS GREAT! WE LOVE AUTOMATION!

  52. BUT

  53. AUTOMATION SHOULD BE USED WISELY, NOT INDISCRIMINATELY.

  54. POTENTIAL ALTERNATIVES We can do better than this, folks.

  55. WHAT ARE WE ACTUALLY TRYING TO DESCRIBE?

  56. SOFT SKILLS ! INTERPERSONAL SKILLS PERSONALITY TRAITS TASK- & MANAGEMENT-RELATED

    SKILLS CUSTOMER-FACING SKILLS
  57. interpersonal human subjective psychological personal rational cognitive social life-learned

  58. HARD SKILLS ! CERTIFIED, TESTABLE, OR MEASURABLE KNOWLEDGE AND THE

    ABILITY TO USE THAT KNOWLEDGE
  59. quantifiable technical professional academic specialized computational robot learned mechanical book-learned

  60. PAIRED ALTERNATIVES

  61. QUANTIFIABLE INTERPERSONAL Calculator by Luboš Volkov from The Noun Project

    SKILLS ARE MEASURABLE OR IMMEASURABLE.
  62. TECHNICAL PSYCHOLOGICAL Brain by Martha Ormiston from The Noun Project

    MENTAL EFFORT IS REQUIRED FOR BOTH.
  63. BOOK-LEARNED LIFE-LEARNED GAINED THROUGH PRACTICE VS. TRAINING.

  64. ROBOT HUMAN HUMANS ARE INDIVIDUALS.

  65. TECHNICAL SOCIAL TOOLS REQUIRED FOR BOTH TYPES.

  66. REAL PEOPLE Adjusting expectations and action steps.

  67. T PS Alert by deadtype from The Noun Project

  68. TEACHERS & MENTORS

  69. 1 TEACH ETHICS MODEL SOCIAL SKILLS

  70. 2 REWARD PROGRESS CORRECT MISSTEPS

  71. 3 PROVIDE FEEDBACK & CONSTRUCTIVE CRITICISM DURING THEIR LEARNING PROCESS

  72. INTERVIEWERS & HIRING MANAGERS

  73. 1 ASK A LOT OF QUESTIONS ABOUT INTERPERSONAL SKILLS AND

    THEIR EXPECTATIONS FOR THE ROLE.
  74. SAMPLE QUESTIONS

  75. WHAT ROLE DO YOU PLAY BEST ON A TEAM? !

    DESCRIBE A GOOD CODE REVIEW. ! WHEN YOU PAIR, DO YOU PREFER TO DRIVE OR NAVIGATE?
  76. WHAT LEVEL OF FEEDBACK DO YOU EXPECT FROM YOUR MANAGER?

    YOUR PEERS? ! WHAT TYPE OF FEEDBACK IS MOST VALUABLE? ! HOW DO YOU LIKE TO WORK? ALONE? IN A PAIR?
  77. 2 INFORM THEM OF EXPECTATIONS AND WHY YOU VALUE INTERPERSONAL

    ABILITIES.
  78. 3 ADJUST EXPECTATIONS OF THEIR ROLE WITHIN THE COMPANY AND

    ON THEIR TEAM.
  79. MANAGERS & TEAM LEADS

  80. THIS IS A LOT OF WORK. BUT IT’S WORTH IT.

    PROMISE!
  81. 1 COLLABORATIVELY CHANGE JOB DESCRIPTIONS WITH HELP FROM DIRECT REPORTS,

    UPPER MANAGEMENT, AND HR. Chameleon by Radu Luchian from The Noun Project
  82. 2 PROVIDE TRAINING: PAIR PROGRAMMING, FEEDBACK, AND CODE REVIEW WORKSHOPS.

    Teacher by Jaclyne Ooi from The Noun Project
  83. 3 MAKE OPPORTUNITIES FOR NEW LEADERS ON SMALL PROJECTS AND

    FOR PAIR PROGRAMMING TO REINFORCE NEWLY LEARNED SKILLS.
  84. 4 GIVE FEEDBACK FREQUENTLY WITH REGULARITY AND SPONTANEITY.

  85. IT’S EASY TO BE A UNICORN. Unicorn by caba kosmotesto

    from The Noun Project
  86. IT’S HARD TO BE A PERSON.

  87. Bye ! Liz Abinante • @feministy • me@liz.codes This slide

    is tan at the request of my RailsGirls Summer of Code intern, who also picked out my outfit.