I've had the great fortune to attend my 8th domestic and 9th overall Tableau Conference (TC) and one of thoughts that top of mind, is wow! I feel as energized and in love with the Tableau community this conference as I did in 2013. For those you doing math, my 2011 an 2012 conferences (Vegas and San Diego) felt overwhelming; 2013 felt new since I started to meet people in the community that year.
I am trying to make this saying go viral, because I think that's how we should learn. It should be fun to learn and it actually is at TC! My learning opportunities this year came in the form of podcast discussions, keynotes, and a couple of sessions. But before we get to that, the conference kick-off was amazing!! I marched in a parade to celebrate the beginning of conference! Matt Francis and I hosted The Vizzies. These are awards that we give out to Tableau community members that are voted on my the community. We held them during the welcome reception and it was so fun...one might say it was informative fun!
This is the first year I attended the US conference with podcasting as my number one job, so podcast we did. We recorded a record 12 episodes with informative and fun conversations with people like the Makeover Monday team, Andy Kriebel and Eva Murray, to Keisha Rose (developer at Tableau) feat. Paul Banoub to Elissa Fink (CMO of Tableau) and Adam Selipsky (Pres/CEO of Tableau).
New to conference this year was Brain Dates mentoring. It was in a fun and relaxed atmosphere where people could sign up to learn more and have engaging conversations about almost anything. What I love is that people got connected that might have otherwise not been and learned along the way! I was able to participate in one about Data+Women, which also renewed some of my energy for helping others get started with organizing one.
I had the opportunity to sit in on Paul Banoub's session about building a Center of Excellence (COE) and it was excellent!! What I loved most is that Paul took us through this COE journey that firms of any size could relate to. I think it helped frame up COE goals for a lot of people in the room. And what's even more amazing is that Paul's team is quite small and yet, is best in class.
I also sat in on part of the data+women session. I thought it was well-done and I really enjoyed the thought-provoking content presented by Anya A'Hearn. Especially in podcasting, where communicating information is the point, it made me think about how I might have some implicit bias (everyone tends to) and how I can make sure I self-correct. The easiest example is one when introducing guests on the show. Do we only give men the intro of accomplishments? I know there are cases where it's happened, but hopefully it's not the norm for me. However, the whole point (to me) of Anya's presentation was to raise awareness and self-monitor.
Finally, in the informative fun category, we have Fanalytics. Last year was the first year where it was less about vizzing a data set and more about discussions that impact the Tableau Public community. This year, attendees discussed topics such as improving Iron Viz feeders, mentoring, and data culture (just to name a few).
I sat in on the data culture discussion because that's part of how I want to help organizations. The observation that stood out to me is that about half the table did not feel empowered to help create a good data culture in their organization. How sad!! Ideas like viz games (Iron Viz, for example) was met with hesitation. As a result, I put a call to action out there in my LinkedIn weekly leadership in data tip to help employees feel empowered to take action. I strongly believe in individuals taking actions and can usually find a way to work within or around the environment. Your organization isn't keen on a viz contest? Do it at lunch! No one can help organize a proper internal Tableau user group meeting? Meet up during a Friday at lunch or after work for a happy hour to chat all things data viz and Tableau. Before going too far down the rabbit hole, the data culture discussion, which was facilitated at our table by Fi Gordon (and topic leader, Sarah Burnett), really lit me up. I could have stayed for another hour just helping people dig in, get lit up, and ready to action.
I'll be the first to admit that I wasn't sure what we were going to get with Adam Grant as a keynote speaker on the last day (after Data Night Out). For those who missed his talk, wow! You really missed something. Adam's talk was so good! He is an organizational change psychologist and Professor at the Wharton School of Business.
1. Weed out the takers in a community/group/organization.
2. Create psychological safety.
3. Encourage help-seeking.
4. Make the unfamiliar, familiar (and master repetition).
5. Put your worst foot forward (address the concerns first).
6. Set boundaries on time. Chunk out time to avoid distractions.
Adam shared such valuable information, but then he shared the results of a Tableau community survey on who the top givers in the community are...and my name was on the slide. I had this feeling of immense gratitude, that people recognized and saw value in my contributions to the community. As one of my friends put it, "Now you know what it's like winning a Vizzie."
While I had some informative fun, it would not have been as fun without my crew. These folks help make me better and are super supportive, especially as I have transitioned into entrepreneur life. I am so thankful that I have surrounded myself with such amazingly smart, funny, and caring people. A few people I want to call out specifically from the Tableau community:
I'm looking forward to continuing my contributions in the Tableau community and applying these learnings to grow myself and my skills to help others.
The best reason to use visual analytics is that the picture can tell data's story. It can uncover relationships between data and help show outliers that might otherwise be difficult to see. However, a visual may not be enough, depending on the reader. Adding a little text can help in understanding the message.
Take this visualization for example.
We clearly see there was a downturn in sales, which makes the reader curious about why it happened. The visualization though, does not give the answer.
However, if I add one sentence about a change in the pricing model, we can immediately understand why, at least at a surface level.
I think we've all been in business settings where we are handed a graph and asked, "What is this telling us?" And I'm sure we've all have had that momentary thought of "I don't have a clue."
Why make our readers work harder than what they need to?
Unless a visualization is exploratory, the real work should come in at the decision-making process.
To help the reader get to the conclusion/decision quicker, the following types of annotation can help.
Care should be taken to avoid over-annotating. The best way to avoid that is to ask, "Do we really need that additional information?" In the above example, there be opportunity to remove an annotation, which should discussed in the vetting session.
To read more about the making of this annotated line graph, click here.
Recently, I've been doing some practice with chart types I haven't used in awhile, so when I saw Cole Knaflic's Storytelling with Data Challenge this month, I decided to give it a go. The challenge for June was to create a slopegraph. While the data I have been working with recently hasn't been suited for a slopegraph, I do think they are a nice way to see the difference.
Here's my original table of data that I made up.
If my goal is to figure out which jewelry division increased the most or had a negative change, I need to do calculations in my head to get the answer I'm looking for. That takes precious time that could be spent making the decision or implementing actions.
This data is perfectly suited for the a slopegraph because I'm measuring two periods, so I should be able to see the change clearly.
Here are two versions of this data visualization, one with only the visualization and one with a small amount of text to inform the reader about a decision on where to focus.
I can now clearly see:
A little narrative can go a long way in facilitating or communicating decisions.
For this example, I used Tableau software and have provided the completely fictional data below if you would like to use it.
I recently had the great fortune to moderate a panel with some amazing people for the DC Data+Women meetup. If you are in the DC area and want to check out the meetup, you can learn more information here.
For this quarter's meetup, we held a recruitment panel discussion followed by speed mentoring and networking. Our panel included Ann K. Emery, Eric Duell, Executive Director of Applied Analytics, Comcast Communications; Emily Vose, Deloitte Consulting, and Meghan Marie Fowler-Finn, of DDOT. The focus of the panel was on recruitment, interviewing, and career transition advice. The panelists delivered great insight and was very generous with their time in talking with attendees afterwards.
There was such good interaction from the attendees and the panelists at this event. Though the panel lasted about one hour, it could have definitely gone on for another 30 or 60 minutes!
I hope these takeaways were useful. If you have any questions, are interested in participating in the next data+women meet ups, or would like a moderator or speaker for an internal workshop on recruitment in the data field, you can contact me at firstname.lastname@example.org.
Whether it's through talking with a friend or seeing trending items on social media, visual analytics is becoming a part of how we conduct business. But what is it exactly? My concise definition is as follows:
Visual analytics leverages automated analytical techniques to create interactive visualizations in order to make well-informed decisions.
Data visualization is a big part of the visual analytics process, but it's only one piece. A visualization isn't meaningful if it's not being used to inform or make decisions, which is the second part of the definition above.
People at all levels of business are using visual analytics to carry out their responsibilities. Data visualizations are helpful in measuring performance and identifying outliers. Because the data is shown visually, using patterns and pre-attentive attributes, the reader can quickly see the the information being displayed. Whether it's exploring the data or having a report available, quicker insights should translate into quicker decisions, which in turn facilitates timely business transactions.
At Analytics to Inform, we believe in empowering executives with information to make well-informed decisions. Explore our services page to see which service can help your business.