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From Good to Great
Andy Kriebel - Head Coach @ The Information Lab
dataschool.co.uk vizwiz.blogspot.com datavizdoneright.com
Where do you stand amongst the competition?
Is what students are learning applicable in the real world?
What do the best
universities teach?
To Win!
A Good Data Analyst is NOT a Report Generator
A Good Data Analyst is NOT a Yes Man (or Woman)
Good Data Analysts Know the Basics
Good Data Analysts Understand Design Principles
Good Data Analysts Know Basic Coding
Good Data Analysts Have Good Business Acumen
Good Data Analysts Understand Basic Stats
What Separates a Great Data Analyst from a Good One?
Great Data Analysts Understand the Story Behind the Data
Great Data Analysts are Interested
Great Data Analysts
are Curious
Great Data Analysts
Understand Context
Great Data Analysts Have an Appetite for Learning
Great Data Analysts Have an Appetite for Learning
http://bit.ly/DataVizBlogs
Great Data Analysts
are Imaginative
Great Data Analysts
Can Decipher the Message
Great Data Analysts
are Methodical
Great Data Analysts Can Spot Trends and Themes
Great Data Analysts Can Spot Trends and Themes
Great Data Analysts Can Spot Trends and Themes
Great Data Analysts
are Storytellers
18 Weeks 18 Projects
Some Unexpected Benefits
Their Work Gets Noticed
Certification to Show They’re Ready
From Good to Great – Tips for Becoming a Great Data Analyst
From Good to Great – Tips for Becoming a Great Data Analyst

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From Good to Great – Tips for Becoming a Great Data Analyst

Editor's Notes

  1. Hi everyone! My name is Andy Kriebel and I’m the Head Coach of The Information Lab’s Data School. Thank you for having me here today and thank you to Larry for asking me to speak. I’m here today to talk to you about a big problem I’m seeing in the word of data analytics. I’m going to ask questions along the way to confirm my thinking. Are you ready?
  2. There is an impending talent shortage in the Data Analytics world. Headlines from are clearly saying data science is the most required job in the world.
  3. Yet our universities are failing to teach students the proper, applicable skills they need to succeed in this sea of data.
  4. Too many companies are failing their employees by not investing enough to develop these skills. Why is that? What are some of the reasons (or really we should call them excuses) that you hear?
  5. Companies are clearly afraid that they will train their employees and then they’ll quit and take the knowledge with them. But think about the alternative: what if you don’t train people and they stay? Can companies afford to NOT take this chance?
  6. What I’m seeing is companies that do nothing instead. Inaction is a risk that I don’t feel is worth taking.
  7. This talent shortage is going to affect you. If you’re a student, then you have the opportunity to put yourself at the top of the talent pool. If you’re a company, then you in competition for this talent. I’m going to talk to you about some strategies for developing and attracting this talent. But first, let’s step back and see how we got here.
  8. Discussion: Why do we go to university?
  9. We all like to think we’re there to learn. But if you think about it, isn’t university really about building a strong network? College is about meeting people you can call upon to help you have a great career. We don’t go to college to learn. We go to college to get a great job. Are universities helping us develop the skills to be successful? I think not.
  10. Discussion: What are our universities teaching us that gets us excited? What’s the last course you took that you couldn’t wait to wake up for? Why were your favourite professors your favourite?
  11. Discussion: How much of what we learn can we apply? Generally very LITTLE
  12. Discussion: What do the best universities teach? A way to think.
  13. The best universities teach students how to turn problems into solutions.
  14. Discussion: What is the purpose of data analysis? Why do we do data analysis?
  15. We do it to WIN! To have an advantage. To be the best. To make more money for our shareholders.
  16. To make an IMPACT.
  17. To find patterns.
  18. To find something that might have otherwise been unknown. To predict the future. To gain an advantage on the competition.
  19. Because, oftentimes, the worst 3 words an analyst can say are “I don’t know.”
  20. Would the best snipers in the world ever be sent into battle without proper training? Of course not.
  21. Discussion: Then why do organisations not invest in a similar manner? Why don’t they train their people to be the best?
  22. Are they afraid their employees will quit and take the knowledge with them? Let me repeat. Can companies afford to NOT take this chance? Why do you think companies operate under this fear?
  23. If companies are afraid of losing talent, maybe they should begin to understand why those people are leaving.
  24. Most companies don’t let people pursue their passions, despite studies showing that people who are able to pursue their passions at work experience flow, a euphoric state of mind that is five times more productive than the norm.
  25. They fail to challenge people intellectually. When talented and intelligent people find themselves doing things that are too easy or boring, they seek other jobs that will challenge their intellects.
  26. Make work fun. If work feels like work, then you’re in the wrong job.
  27. Let’s turn back to the student and the employee and talk about what it means to be an analyst. But first let’s start by talking about two traits for what a good analyst is NOT.
  28. Show of hands, who has people in their companies that have the title analyst that do not real analytical work? A good analyst is NOT a report generator. Too often I see people with the title “Analyst” that do absolutely zero analytical work.
  29. A good analyst is NOT a Yes Man or woman. A good analyst should be confident enough to question the opinions of others. Just taking orders doesn’t make someone an analyst.
  30. So what then does make a good data analyst?
  31. Good Data Analysts Know the Basics
  32. Good data analysts know the basics of effective design. They have a combination of skills that allow them to take complex data and communicate it simply and effectively.
  33. A good data analyst knows basic coding. Languages like python and SQL help develop structured, logical thinking.
  34. A good data analyst can speak to upward, downward and sideways. They can talk the IT language as well as the language of the business.
  35. A good data analyst will know the basics of stats. They will know enough to be able to call people out for lying with stats.
  36. We now have a baseline for what makes a good analyst. But I’m here to talk to you about what makes a great data analyst.
  37. Great data analysts not only know the basics, but they also can find, understand and communicate the story behind the data.
  38. The most important trait for any job is to be interested in the work. If you dread going to work, if work feels like a job, then you’re in the wrong the job. You absolutely need to love the work that you do. If you don’t, quit.  Being interested is the single characteristic that differentiates a job from a career. This cannot be cultivated. You either have it or you don’t. In a few minutes, I’ll talk about how we, at The Information Lab, determine if people are interested.
  39. Although you may be interested in a topic, you need to have the curiosity to dig deeper into data. Don’t take data at face value. Figure out why. For example, if you look at this map of crimes on NYC subways, you can clearly see the stations that have the most crime. But a great data analyst will see beyond this.
  40. What is much more relevant is how many crimes happen per person, because that provides a much closer estimate of each individual’s risk of becoming a victim. Great data analysts can tease things like this out of the data.
  41. Including context doesn’t have to mean making it overly complex. Consider this example from Emily Chen of The Data School. With simple annotations, and an excellent title, she’s provided all the context needed to emphasise the magnitude of the AIDS epidemic.
  42. Or maybe context is added through colour and sheer volume of information, like this visualisation about mass shootings in America. In this case the context is seen on the map with the dots representing the locations of shootings, by the calendar which shows the frequency of mass shootings, and by the reverse bar chart which adds emotion.
  43. A great data analyst is a great storyteller. A great analyst might animate this visualisation and turn it into something even more powerful. Telling the story this way reinforces the magnitude of the problem; it makes the problem more real. It clearly demonstrates the horrifying fact that there are over 300 mass shootings per year in America.
  44. Great data analysts are always looking to learn and improve. But their learning should be broad. Great data analysts are not only experts in data visualisation and design, but they also understand how the brain works and how people perceive messages. This is a sample of great books that should be in any great data analysts library.
  45. In addition to the books, there are tons and tons of great blogs to read that will absolutely help you learn and grow. A great place to start learning are all of the wonderful blogs by the Tableau Zen Masters.
  46. Great data analysts are ‘imaginative’ not only in displaying the data but also in analysing it. They always think of a next step of how to slice and dice the data set: What if I did this? What if I break this dimension down by this factor? And so on.
  47. Great data analysts have the ability to decipher what’s important and what’s not. They can quickly understand the critical objectives and focus on those.
  48. A great data analyst is systematic in their analysis approach.  They can create a step-by-step approach and run through an internal checklist of what has to be done on a data set.
  49. Great data analysts have a unique eye for spotting data patterns.  Oftentimes, this doesn’t materialise until you can see it in graphical format. For example, in this map of US drought by month and year we can see some overall patterns. What patterns are obvious to you?
  50. A great data analyst will see additional patterns. Like California and the cyclical nature of its drought. Yet somehow the California government doesn’t see the droughts coming. Maybe their analysts are only good analysts and not great analysts.
  51. Here’s another great example of someone that took great care in exposing the trends, themes and patterns in the data.
  52. Great data analysts take all of the pieces of different data points, patterns, and themes and are able to compose them into a story. Let’s look at an example from Pablo Saenz de Tejada of the Data School. He built this for Tableau’s music IronViz contest. Pablo starts off with a beautiful, engaging opening visual.
  53. He then sets the stage with some background information. Any great story has a beginning, a middle and an end. Great data analysis is the same.
  54. Pablo then takes us into a two part middle of the story. This is well done because it takes the previous part of the story and builds upon it, focusing on the locations and breadth of Bruce’s concerts.
  55. And then Pablo provides a summarised view, focusing on the length of Bruce’s career and his remarkable consistency.
  56. The close of the story focuses on the songs themselves and leaves you with a last impression of the emotion that is in Bruce’s songs.
  57. How can you learn these skills? How can you kick start data analytics at your company?
  58. Let me provide some tips by explaining how we’re finding and cultivating talent at the Data School and how we’re creating the next generation of great data analysts. Remember, the problem we’re trying to solve is the lack of data analytics talent in the market.
  59. All the training in the world won’t make a bit of difference unless those providing the training are the best in the world. At The Information Lab, we have 10 certified Tableau trainers, 3 Zen Masters and an Alteryx Ace. We each have different skill sets that, when you combine them, provide a level of expertise that’s unmatched as far as I’m aware. In addition we have the author of a data viz design book and one of the top football analysis experts in the world. Think about the different skill sets you have in your company. Can you leverage their combined experience to create a great training program?
  60. We recruited an amazingly diverse class from lots of different backgrounds. From economics to microbiology. From market research to physics. It’s the blend of talent that has brought out the best in each and every one of them. They were selected through a unique hiring process, part of which I did while I was working at Facebook.
  61. It’s a two step process with the ultimate goal of determining their passion. 1. First, anyone that is interested must submit a visualisation to Tableau Public with any data set of their choice. This has provided a great filter. If someone is willing to take the time to train themselves just for the chance at a job, then it’s obvious that they care. And those that don’t submit we simply never have to interview.
  62. 2. I personally review every submission and provide feedback. 3. I then do phone screens with anyone that has submitted something that shows potential.
  63. 4. I end up with a short list that we invite back for final interviews. The final interview has two 15-minute parts: 1. A more traditional interview, in which we try to tease out passion, potential and desire. Basically then things we can’t teach. 2. A demo of a visualisation created from a data set that was provided a week in advance. We evaluate them in four categories: insights, storytelling, best practice and if they are engaging. These are our criteria because these are the skills that make a great data analyst. 5. Lastly, we rank everyone, make offers and then we’re done.
  64. How are you hiring your data analysts? Are you finding the signal through the noise? I doubt you are unless you’re hiring people the same way we are.
  65. We’ve been able to leverage our network to bring in world class speakers. In their first week, the students had a private session with Christian Chabot. In their second week, Elissa Fink came to talk about how Tableau markets Tableau, and gave them advice on how to be a part of the Tableau community and what that means. Francois Ajenstat, VP of Product Management at Tableau and George Mathew, President and COO of Alteryx also came to visit.
  66. The consultants go through a 2-year program. It starts with an intensive 4-month training program where they become experts in Tableau and Alteryx. Their 4 months is likely equivalent to about 3 or more years of experience they’d get in a traditional job. The training is followed by three 6-month engagements. This gives the applicable experience that traditional organisation don’t provide. We then repeat the process over and over again. Are you as committed to developing the skills of your data analysts?
  67. The process repeats every 5 months, so at any point in time, we will have 32 consultants in the program
  68. While they’re in the Data School, they take on a new project every single week. Over half of these projects are for customers. And on these weeks, they’re not only working on their project; they’re going through training as well. This works because the projects give them real data to work with and real problems to solve. All of the training is the world won’t stick unless you reinforce the learning. This is how we do it. These projects help them learn time management, scope management, presentation skills, business analysis skills, and so on. I move them at this blistering pace so that when they go into their placements, the work will seem much slower and much more manageable. They might hate me now, but I’m fairly confident they’ll be thanking me later.
  69. Tell the Surrey County story
  70. And their work is getting noticed in the data visualisation community. In a little over 3 months at the Data School, they’ve had 4 Viz of the Day winners and this beautiful viz by Nicco Cirone about Edward Snowden was just chosen as Viz of the Week by Tableau.
  71. They also attain certification to prove that the things we’ve been teaching are what they really need.
  72. They have two choices at the end of the program.
  73. Nothing we’re doing is overly complicated and it’s working. It’s working because we all have a collective belief in our company mission: “Helping people make sense of data”. I know of less than 10 people in the world that are truly experts at both Tableau and Alteryx and our tiny company of 20 people has single handedly created 8 more in four months with many more to come over the next few years. We’re helping to fill the data analytics talent pipeline. If we can do it, why can’t you? Thank you!