Tableau Conference 2014 Presentation

Editor's Notes

• #3: - But before I dive into that topic, I’d like to tell you a bit about the company I work for How many of you have heard of MC or even purchased from our site? (Good!) ModCloth is the leading social shopping e-tailer of women's fashion and décor. Co-founder Susan Gregg Koger found an early love of vintage fashions, and that passion has grown into a company of over 500 employees with 3 office locations (site) MC strives to cultivate a unique catalog of products as well as a strong commitment to customer care, leading to customers who are more than shoppers, but really part of our community!
• #4: As leaders of this social retail community, we hope to be more than a retailer, we want to inspire personal style and help customers feel like the best versions of themselves as well as to disrupt the fashion industry by democratizing fashion and décor around the world.
• #5: To do this, we allow our data and customer involement to inspire us. We allow data we collect from our customer help us influence the direction of our catalog through several means, including interactive programs that provide us data about our consumers and their style: 2 of these programs are Make the cut and Be the Buyer … So who at modcloth actually uses this data?... Make the Cut, our crowdsourced design program where our community is invited to submit clothing sketches we may produce and sell on our site. You can see here an example. In this case we received the sketch from a member of the ModCommunity, and the design received enough support to go into production. Be The Buyer, our pick it/skip it program that allows our customers to tell us why they do or don’t love our new or upcoming styles In this example, you can see that the ModCommunity was not a huge fan of the original design, community members submitted reviews like this one from Morgan… and the modified style was much more popular These are just 2 of the many ways ModCloth has decided to use data to direct fashion and customer experience…
• #6: To do this, we allow our data and customer involement to inspire us. We allow data we collect from our customer help us influence the direction of our catalog through several means, including interactive programs that provide us data about our consumers and their style: 2 of these programs are Make the cut and Be the Buyer … So who at modcloth actually uses this data?... Make the Cut, our crowdsourced design program where our community is invited to submit clothing sketches we may produce and sell on our site. You can see here an example. In this case we received the sketch from a member of the ModCommunity, and the design received enough support to go into production. Be The Buyer, our pick it/skip it program that allows our customers to tell us why they do or don’t love our new or upcoming styles In this example, you can see that the ModCommunity was not a huge fan of the original design, community members submitted reviews like this one from Morgan… and the modified style was much more popular These are just 2 of the many ways ModCloth has decided to use data to direct fashion and customer experience…
• #7: Our current data team is split into 4 major factions that allow us to take on a large breadth and depth of data projects: Our BI analyst is our go-between for data warehousing and analytics Our Data Scientist engineers data products in collaboration with both analytics and engineering teams at MC Experience research focuses on consumer insights and trying to gather direct from consumer data via surveys and face-to-face interaction Analytics focuses on analyzing data gathered through interactions with the MC site: (next slide)
• #8: None of the analysts are truly embedded with any given team, although we do have specific spheres or liaisonships that we take responsibility for (for example I work closely with our Social, Merchandising, and Operations teams) From here on in, I will use the word stakeholder multiple times. By stakeholder, I mean the intra-company partners that the analysts are working with or for. Now, before I get into the main structure of my talk, I’d like to know a couple of things about the audience… In doing this, our day-to-day can include any of the following:… Some of these tasks can be heavily aided and made simpler by the use of data visualization tools such as Pentaho, Omniture, Tableau and others… From here forward, I will talk about my experiences in using Tableau, but I wanted to make it clear that the principles I will discuss should be applicable for any good data vis gui (examples on next slides)
• #9: None of the analysts are truly embedded with any given team, although we do have specific spheres or liaisonships that we take responsibility for (for example I work closely with our Social, Merchandising, and Operations teams) From here on in, I will use the word stakeholder multiple times. By stakeholder, I mean the intra-company partners that the analysts are working with or for. Now, before I get into the main structure of my talk, I’d like to know a couple of things about the audience… In doing this, our day-to-day can include any of the following:… Some of these tasks can be heavily aided and made simpler by the use of data visualization tools such as Pentaho, Omniture, Tableau and others… From here forward, I will talk about my experiences in using Tableau, but I wanted to make it clear that the principles I will discuss should be applicable for any good data vis gui (examples on next slides)
• #10: None of the analysts are truly embedded with any given team, although we do have specific spheres or liaisonships that we take responsibility for (for example I work closely with our Social, Merchandising, and Operations teams) From here on in, I will use the word stakeholder multiple times. By stakeholder, I mean the intra-company partners that the analysts are working with or for. Now, before I get into the main structure of my talk, I’d like to know a couple of things about the audience… In doing this, our day-to-day can include any of the following:… Some of these tasks can be heavily aided and made simpler by the use of data visualization tools such as Pentaho, Omniture, Tableau and others… From here forward, I will talk about my experiences in using Tableau, but I wanted to make it clear that the principles I will discuss should be applicable for any good data vis gui (examples on next slides)
• #11: But the ModCloth analytics team has been able to extract the most power out of these data tools by teaching non-analyst stakeholders at our company (people to whom we often deliver data and recommendations) how to perform quick analyses themselves {Animate the point-by-point order and maybe set up photo shoot to replace this stock art} Subscribing by the old adage of teaching a man how to fish - This is important because our teams current backlog has over 100 requests, and not every request can be prioritized immediately
• #14: Rather than focusing the rest of my talk on the benefits of data visualization in teaching others, I thought I would go through the benefit of teaching others in creating data visualizations To do this, I will discuss some of my own personal insights I have gathered in training non-technical individuals how to use a data visualization GUI
• #15: Teaching non-technical individuals how to use a data visualization GUI is not as simple as showing them the functionality and letting them loose. As a teacher of these tools, I have identified my top hurdles to overcome: (as listed on slide) The following set of slides will detail my experiences in trying to overcome these hurdles
• #16: Teaching non-technical individuals how to use a data visualization GUI is not as simple as showing them the functionality and letting them loose. As a teacher of these tools, I have identified my top hurdles to overcome: (as listed on slide) The following set of slides will detail my experiences in trying to overcome these hurdles
• #17: Data is clean – We expect stakeholders to be able to use them with minimal input from the analytics team How many data sources do we have – how many are truly optimized??
• #18: Data is clean – We expect stakeholders to be able to use them with minimal input from the analytics team
• #19: Data is clean – We expect stakeholders to be able to use them with minimal input from the analytics team How many data sources do we have – how many are truly optimized??
• #20: For a stakeholder to create a powerful visualization, it must be meaningful, and often the language used by the analytics team may not be the most straightforward or intuitive To make sure the stakeholder has the best chance at creating a meaningful vis, our team goes to lengths to make sure data is stored and presented with non-technical individuals in mind (anecdote about stakeholders using the wrong data and being confused about why the bar chart looked so weird) {Look into tableau comments bubbles and maybe show labels in dashboards themselves as examples?}
• #21: Reviews data source
• #22: Teaching non-technical individuals how to use a data visualization GUI is not as simple as showing them the functionality and letting them loose. As a teacher of these tools, I have identified my top hurdles to overcome: (as listed on slide) The following set of slides will detail my experiences in trying to overcome these hurdles
• #23: Teaching non-technical individuals how to use a data visualization GUI is not as simple as showing them the functionality and letting them loose. As a teacher of these tools, I have identified my top hurdles to overcome: (as listed on slide) The following set of slides will detail my experiences in trying to overcome these hurdles
• #24: Since these tools can be very far-reaching with many, many functionalities, it is a logistically sound strategy to entice and intrigue stakeholders by making trainings team or topic specific rather than general Anecdote about how trainings began as general, but we found we were often repeating ourselves during out-of-training hours to people who were interested but didn’t gain the appropriate knowledge during training. Now, trainings are much more useful for stakeholders as attested to by the stakeholders themselves (slide)
• #25: Since these tools can be very far-reaching with many, many functionalities, it is a logistically sound strategy to entice and intrigue stakeholders by making trainings team or topic specific rather than general Anecdote about how trainings began as general, but we found we were often repeating ourselves during out-of-training hours to people who were interested but didn’t gain the appropriate knowledge during training. Now, trainings are much more useful for stakeholders as attested to by the stakeholders themselves (slide)
• #26: Teaching non-technical individuals how to use a data visualization GUI is not as simple as showing them the functionality and letting them loose. As a teacher of these tools, I have identified my top hurdles to overcome: (as listed on slide) The following set of slides will detail my experiences in trying to overcome these hurdles
• #27: -
• #28: (slide)
• #29: - Write logic to find dates when order_num = 1 and dates when order_num = 2 Write logic to find the date diff between order_date_1 and order_date_2 Plot date difference (days between) vs Sum of New Customers (variable we provide) Implement a quick calc to produce running total
• #30: I’m not going to act like there are no downsides to teaching others how to become basic data vis productionists using a data vis GUI, but I hope to convince you in the next couple of slides that the trade-offs are well worth it - Before I go into some of data showing the success MC has experienced by using this model of empowering “non-technical analysts”, I’d like to go through a quick pros/cons list
• #31: (slide)
• #32: (slide)
• #33: - Write logic to find dates when order_num = 1 and dates when order_num = 2 Write logic to find the date diff between order_date_1 and order_date_2 Plot date difference (days between) vs Sum of New Customers (variable we provide) Implement a quick calc to produce running total
• #34: I’m not going to act like there are no downsides to teaching others how to become basic data vis productionists using a data vis GUI, but I hope to convince you in the next couple of slides that the trade-offs are well worth it - Before I go into some of data showing the success MC has experienced by using this model of empowering “non-technical analysts”, I’d like to go through a quick pros/cons list
• #35: First, a quick pros/cons list
• #36: {MC analytics completed ~75 requests in ~943 hours of working time (~12 working hours per request)} By teaching non-technical individuals at our company how to use data vis GUIs in an effective manner, we have been able to add only a few hours of additional work per week to increase our “request” delivery by nearly 2x In addition, the majority of data pulls/simple data delivery can now be done by the stakeholders themselves, giving the analysts on the team more latitude to pursue topics they deem more interesting such as data modeling and prediction Even more “requests” have been filled through use of additional tools such as Omniture and even google analytics (which has many of the data housing and visualization functions)
• #37: None of the analysts are truly embedded with any given team, although we do have specific spheres or liaisonships that we take responsibility for (for example I work closely with our Social, Merchandising, and Operations teams) From here on in, I will use the word stakeholder multiple times. By stakeholder, I mean the intra-company partners that the analysts are working with or for. Now, before I get into the main structure of my talk, I’d like to know a couple of things about the audience… In doing this, our day-to-day can include any of the following:… Some of these tasks can be heavily aided and made simpler by the use of data visualization tools such as Pentaho, Omniture, Tableau and others… From here forward, I will talk about my experiences in using Tableau, but I wanted to make it clear that the principles I will discuss should be applicable for any good data vis gui (examples on next slides)
• #38: None of the analysts are truly embedded with any given team, although we do have specific spheres or liaisonships that we take responsibility for (for example I work closely with our Social, Merchandising, and Operations teams) From here on in, I will use the word stakeholder multiple times. By stakeholder, I mean the intra-company partners that the analysts are working with or for. Now, before I get into the main structure of my talk, I’d like to know a couple of things about the audience… In doing this, our day-to-day can include any of the following:… Some of these tasks can be heavily aided and made simpler by the use of data visualization tools such as Pentaho, Omniture, Tableau and others… From here forward, I will talk about my experiences in using Tableau, but I wanted to make it clear that the principles I will discuss should be applicable for any good data vis gui (examples on next slides)