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."

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. 

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 emily@analytics2inform.com. 

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:

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.