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.

If You Are Not Innovating as a Data Organization, You Are Spinning Your Wheels

By Kirk Harrington

The idea for this article came from a dream I had this morning, where I was interviewing with a company and I asked one very important question...."Does your organization take time to do research, find data gaps (and seek to fill those gaps), look for opportunities to innovate with the data they have, while working on regular projects?"  After I had asked this question to the first person I was interviewing with, I then was passed from one data analyst to another.  Two analysts had given me some sort of excuse as to why they couldn't answer the question and the third said something like "Well, I don't have time to answer your question...I am very busy...and then he went on to describe an internal political matter that caused him grief."  Basically, the answer was that this company did not take the time to really work with their data...but instead became paralyzed with what they already had and the workloads and inner politics surrounding it.

If this company were real, and not a figment of my imagination created in my dream...I would suggest this to them.  To be competitive as an organization and to make work more interesting and vibrant, there needs to be dedicated time made for data.  In the various organizations that I have worked in...I have seen several practices that have helped a company be successful with this...for example:

• Engage in projects that identify data 'gaps'.  Reach out to your various organizations...find out their needs.  Find out what the data answers for them and what it doesn't.  Find out what they'd really like to know that their data is not answering for them now...and seek ways to fill these needs.
• Look for data overlaps and data 'excess'.  Sometimes, whether its through reporting or even data sets that are sitting in various repositories, overlaps and data 'excess' can be created which can cause needless slowdowns,workflow inefficiencies, time waste and data space waste.  A project to identify overlaps and data 'excess' can be useful to deal with this.  One project I did, for example, was to identify all the reports created at an organization and determine which reports were being used and which were not.  This project helped because we were able to identify which reports were not being used so they could be eliminated and it created an understanding of all the different reports being created that could possibly be used across the various teams at the organization.
• Take time to delve into the data and find innovative ways to stretch it.  One project I did once at an organization did this very thing.  I was tasked to understand all of their data from the various sources and how it could be used for analytics and modeling.  I went the extra step to teach them how to create variables from what they already had so it would enhance what they could learn from their customer base.  Further, I identified third party variables that were being paid for and found a suitable public source that they didn't have to pay for.  This effort really helped with the analytics and modeling that I was tasked to do after this project.
• Take time to understand the third party data you have purchased.  I once worked at an organization that had purchased various different third party data and when I asked what people knew about it, I didn't get straight answers.  What that told me is that they had purchased data, but didn't really know if it was useful or not.  I proceeded to test a given set of this third party data for a project I was working on and found it extremely useful to identify target populations for mailing (I was a marketing analyst).  Because of the success of the test, these variables were used and saved the campaign I was tasked to save.  And all it took was a little research time and trial.
• Look at improving processes that access and read in data that is being used for reporting and other processes.  I once did an interesting job at an insurance company where I was tasked to improve coding for various reports being created at the organization.  I was asked to look for coding inefficiencies as well as ways to improve the coding.  What this did, when done successfully, was improve processing time and create increased automation for the work they were doing.  I guess you could call this 'streamlining' and I found it to be a very useful practice in making things better.

Besides opportunities, I also found certain things held back organizations from doing the above.  And this speaks to the dream I had.  For example...

• Heavily siloed analysts that work on very specific things...that may not get the chance to work with other parts of the organization.  While this sometimes can't be helped, I found it useful to be engaged in cross-team projects with various data analysts across the organization...to work on projects that affected the entire organization.
• Company politics that focus on short term vs. long term goals.  This can be very stifling to an organization.  If all your focus is on short term financial goals and 'putting out fires', you are neglecting long term growth, competitiveness and steadiness/stability.  Recognizing that you have politics that hold you back can be very challenging as well...especially when people's reputations can be affected.
• Having an overabundance of work and overburdening your analysts can also be a source of stifling to innovation.  In an age where many companies are trying to cut costs and divide workloads among fewer and fewer analysts, while this approach can help with short term cost benefit, it will not help with long term growth and being competitive.  A balance should be found where workloads become manageable and that analysts are given some free room to innovate.  It will make their jobs more enjoyable and lead them to be more productive with the work they are already doing.

In my mind, if you don't take time to innovate and improve your data...as a company you become just like hamsters running in a wheel.  It looks like you are working, but you are not really getting anywhere.  And, you tire out the hamsters (your analysts) who probably want to do more with their careers than just round around in circles for you.

Claiming Success When your Project is Killed

By Kirk Harrington

Have you ever had an amazing project that would have enhanced the company killed by office politics?  And then your work just kicked to the wayside and you are asked to bury it?  That happened to me once.  Not the most pleasant experience.  The thing is though...what they couldn't take away from me was the success of the project and that it made an impact when it was in place.

What happened was this...I was tasked to create a householding methodology for the entire organization (a small bank I used to work for).  I was given leeway to create a project team, lead that team, and give them an end product that would benefit the specific group we were doing the project for (in this case it was Asset and Liability Management).  

Everything went well.  I was able to bring in a good team, given everyone manageable projects that would help with the end product, and we delivered the householding methodology (which the Asset and Liability team saw as miraculous) within a reasonable amount of time.

What happened though is that once the methodology was in place, was working well, and the client using it was happy with it....someone in the organization decided to take an ego trip (for lack of a better word) and decided that householding would be better served by having a third party do it (which by the way...that third party was notorious for not being the best to work with).  So basically, the organization spurned the advance I did...one that was done internally, that could be easily done and controlled...to a solution that was done by a third party that was less efficient with less ability to control it.  I think too the person that killed it felt embarrassed that someone like me would come in and do such a grand thing.  Again...why I call it an ego trip.  What's more...my manager felt inclined (because of office politics) to support the person taking the ego trip, so my project was basically doomed.  I was also never asked to provide project notes, coding, anything to anyone that showed that I had done the project.  In effect, it was buried.

Was I upset?  Of course I was.  Especially when I was given all the resources to do the project and it was meaningful to me because it helped the entire organization.  Further, the team I had was great and it was a very positive experience for me to work with them to create such a useful end product.

What I have determined though is this...while my project was killed ...it did do the following:

• Lit a fire under someone who put through a householding methodology for the group asking for it (even if it wasn't the best solution)
• Gave me experience managing a project team on an important project that effected the entire organization
• Gave me experience creating a householding methodology that was innovative for the company I was creating it for

So, instead of getting angry and upset because of office politics killing my project, I have decided to make it a positive.  I have included this project on my resume and its something I enjoy talking about when I speak with potential employers.  I have in effect claimed success when the project itself was killed.