Using Data to Tell a Story - Part 1

Using Data to Tell a Story - Part 1
By Kirk Harrington

I recently was given really good advice about my resume.  I hadn't realized what was mentioned to me, till this advice was given.  I was told that my resume seemed to focus highly on technical skills, but not enough on storytelling, being able to explain difficult data topics to other co-workers, and my aptitude with data visualization.  Truly, I have all these aptitudes.  I became skilled at doing them through work, lots of practice, and in sharing my presentations with other associates and business partners I worked with.  Further, in one job, I was told that my presentations were a 'standard' for the team, and it was suggested that others in the team look to my presentations as good examples of how to present analytic concepts to internal partners.

So I updated my resume a bit, however I also wanted to write this article to share some of the things I have learned in my data visualization journeys.  They have been many and several and I've enjoy data visualization and the telling of stories with my data for several years.  Here are some tips and advice I can give...

Focus on metrics that matter and that tell a story that can be acted upon

It is critical that your visualization answers the main questions and reasons for why you did the visualization in the first place.  To help with this, I will typically meet with the partner I am preparing it for and ask them specific questions so I can understand exactly what they want to know.  You might ask questions  like...
'What are you trying to measure?'
'What will this be used for?'
'Who will see this and act on it (i.e. the audience)?'
'Do you have any ideas that could help me with my analysis?'
'What are important metrics that you think should be included?'
'How will knowing the answers to your questions help our customer base?'
'Have you done something like this in the past that I can look into for more insights?'
'Do you have any ideas that could help me with my analysis?'

With answers to these types of questions, you will be able to more easily provide a visualization that tells the story that wants to be heard.

Highlight and make stand out important findings

Depending on the type of visualization done, you can bold, underline, circle, highlight, use large text...something to make a key finding stand out.  What this does is it focuses the attention of the viewer of your visualization onto what they most want to know or understand about what is happening with the underlying data.

The tool you use and the fanciness of the chart or graph is less important than how clear the visualization makes your message come across

I have seen fancy charts and graphs from many an analyst over the years.  The ones that impress me the most though are those that answer the questions that need to be answered in a clear and simple way.  In my mind...if you can look at a chart or graph and within a few seconds understand it and come away with insight...it is a good chart or graph.  On the contrary, if it takes more than a few seconds to understand the chart or graph...perhaps it looks confusing or is missing key elements...then it needs more work to be considered 'good'.  When I say 'key elements', I once looked at a chart for several minutes and was a bit confused by it.  Then I realized it was missing context and a time frame for when the data was being presented.  Without those key elements of a context and time frame, it just wasn't readable.

Follow a guideline when putting together your visualizations for a presentation

Here are my guidelines for a presentation.  I typically follow this format as I put it together.  Having a format is great because it makes things more obvious and people over time will know what to expect from you.

Title Page:  This will include a title, your name, and title in the organization.  I also mention contributors.

Highlights:  This is at the beginning and is simply a set of statements which goes over the answers to the questions being answered in the rest of the presentation.  Plus, it gives the answers to decision makers right away so they don't have to way.

Charts and Graphs of Main Findings

Charts and Graphs of Additional Findings (if any):  These are other things you discovered that can provide more value to your main findings.  I also see these as 'innovative ideas' that may not be known to help push the company forward.

Summary:  With action steps that can be taken with the findings derived.  I believe this is very important.  I have seen many presentations that focus on high level learnings, but don't provide action steps that clients can take to create a given outcome.

Appendix:  This is where you put the 'nice to know' things, detailed stats, and extra information that could be useful to answer questions when you are giving your presentation.

Make sure you include 'key elements' for your given visualization

I made mention of this already, however wanted to provide more specifics.  Here are my suggestions of key elements to use for each type of visualization...

Time series:  Date range, date labels on the x axis, key seasonal events (if any), a trend line, a context for the chart (what is it about OR what is it trying to show you? - this can also be accomplished with a meaningful title), a source

Pie chart:  Percentages, contrasting colors (so they don't appear blended), a key (to tell you what each colored piece of the pie represents), a title/context

Bar and Line Charts:  Clearly defined x and y axis, Values at each bar or line (if it does not appear too busy), labels for each bar on the x axis (or a key if the bars or lines are colored), a title/context

Scatter plots:  Clearly defined x and y axis, trend line (if appropriate), a title/context, highlight of key points or clusters of points

If your visualization looks too busy or crammed, simplify it

Visualizations will be more powerful if they are simple and have less things going on at the same time for someone trying to interpret it.  Here are my suggestions for dealing with business for each type of visualization...

Time series:  Business with these can be caused by having an extra long time window and more than 1-2 metrics followed over time.  For extra long time windows, I suggest splitting up times into time windows that make sense and having separate charts (that can be shows next to or on top of each other).  I suggest if you have more than 2 metrics over time, that additional metrics are show in separate graphs.

Pie Chart:  These can easily become busy because you have many different groups with very small percentages.  I recommend grouping categories based on similar characteristics or even by how small their percentage is (if, for example you have many groups that are <1%, combine these together for your pie, but make sure your key is clear in defining the groups included that are in that range).

Bar and Line Charts:  Including too many bars or lines can make these hard to interpret.  I recommend when you do have this problem, that you group together in their own separate charts categories that are meaningful.  You might even consider not including groups that are not meaningful or where their behavior is already well-known.

Scatter Plots:  Outliers, while sometimes useful to include, can cause these plots to look confusing and can throw off your ranges.  I suggest taking out outliers altogether and not mentioning them, or take them out and talk about them separately (outside the plot).

Scatter plots can also become busy and less meaningful when you include too many populations within the same plot that have contradicting trends within your x and y axis variables.  Trying taking slivers of your populations and seeing if trends become more meaningful.

Examples of visualizations that worked well for me

Unfortunately, I cannot provide actual visualizations in this blog for the employers mentioned...they are proprietary to them.  I can talk about them though so you can get a sense of ones that worked well and how I put them together.  I recommend keeping a record of ones that you have done that were useful so you can talk about them if you have to interview with someone.

The benefit of new advertising to Lean Cuisine (Nestle)

For this visualization, I had done a time series model looking at contributions to volume over time.  Each key part of the volume was given its own color and what the colors represented was stated clearly in a key below.  Dates over time were clearly stated, and I also included a window from a year prior when old advertising was in play.  Further, I included enough of a window after the new advertising to show how the volume benefit was sustained.  So basically, this chart answered several things for the company...what did the new advertising contribute to volume vs. when it was not present the following year AND was that boost to volume sustained.  As an additional way to drive home what the chart was trying to say, I included the % of volume contributed by the new advertising directly on the chart.

Target vs. Walmart and how they reacted to the Affordable Care Act (ACA) (Innerscape Data)

With this one, I was asked to look at two companies and how they reacted to the ACA in terms of the number of employees covered in a plan.  I had a list of companies to choose from, and decided to choose two from the same industry that were competitors (where I suspected there was a difference in their behavior).  I chose Target and Walmart.  It turned out that this was a good choice, because their trends were very different.  Further, because the number of employees for each company was very different I decided to make separate trended charts for each of them and show them side by side when I put them in a presentation.  Because I had a long time horizon, I decided not to state each individual month...but instead just show years with points for each month.  My x and y axis were labeled with variable names, which I don't typically do but made sense to use in this case because the client was looking to purchase use of our variables.  As an additional way to strengthen and interpret the trends, I looked up news articles which spoke to the behavior of Target and Walmart after the Affordable Care Act was released so that I could show that our data was consistent with the commentary.