In a world focused on the value of Big Data, it’s important to realize that Small Data is meaningful, too, and worth analyzing to gain understanding. Let me show you with a personal example.
If you’re a regular reader of the OdinText blog, you probably know that our company President, Tom Anderson, writes about performing text analytics on large data sets. And yes, OdinText is ideal for understanding data after launching a rapid survey then collecting thousands of responses.
However for this blog post, I’m going to focus on the use of Text Analytics for smaller, nontraditional data set: emails.
SMALL Data (from email) Text Analytics
I recently joined OdinText as Vice President, working closely with Tom on all our corporate initiatives. I live in a small town in Connecticut with an approximate population of 60,000. Last year I was elected to serve our town government as an RTM member along with 40 other individuals. Presently, our town’s budget is $290M and the RTM is designing the budget for the next year.
Many citizens email elected members to let them know how they feel about the budget. To date, I have received 280 emails. (Before you go down a different path with this, please know that I respond personally to each one — people who take the time to write me deserve a personal response. I did not and will not include in this blog post how I intend to vote on the upcoming budget, nor will I include anything about party affiliations. And I certainly will not share names.)
As the emails were coming in, I started to wonder … what if I ran this the data I was receiving through OdinText? Would I be able to use the tool to identify, understand and quantify the themes in the people’s thoughts on how I should vote on the budget?
The Resulting Themes from Small Data Analytics
A note about the methodology: Each email that I received contained the citizen’s name, their email address and content in open text format. Without a key driver metric like OSAT, CSAT or NPS to analyze the text against, I chose to use overall sentiment. Here is what I learned
Emails about the town budget show that our citizens feel Joy but RTM members need to recognize their Sadness, Fear and Anger
Joy:
“I have been a homeowner in Fairfield for 37 years, raised 4 kids here and love the community.”
Sadness:
“I am writing you to tell you that I am so unhappy with the way you have managed our town.”
Fear:
“My greatest concern seems to be the inability of our elected members to cut spending and run the town like a business”
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Anger:
“We live in a very small house and still have to pay an absurd amount of money in taxes.”
Understanding the resulting themes in their own words
Reduce Taxes (90.16%)
“Fairfield taxes are much higher than surrounding communities.”
“Fairfield taxes are out of line with similar communities”
“The town has to stop raising taxes at such a feverish rate.”
Loading...“High taxes are slowly eroding the town of Fairfield.”
Moving if Taxes are Increased (25.13%)
“I am on a fixed income at 64, and cannot afford Fairfield’s taxes now. Please recognize that I cannot easily sell my house, due to the economy & the amount of homes on the market here”
“regret to say most of our colleagues and friends have an “exit strategy” to leave Fairfield”
“Our town is losing residents who are fed up and have moved or are moving to Westport and other towns with lower mil rates”
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Reduce Spending (33.33%)
“… bring spending under control”
“Stop the spending please”
“… needs to trim fat at the local level, cut services, stop spending money”
“We need to keep taxes down as much as possible – even if it means spending cuts.”
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Education ‘don’t cut’ (8.74%)
“… takes great pride in its education system”
“… promise of an excellent public education”
“… fiscal responsibility; however, not at the expense of the children and their right to an excellent education.”
Education ‘please cut’ (9.83%)
“Let’s shave funding from all programs including education”
Loading...“… deeply questioning our education budget”
“… reduce the Education budget”
“I have a cherished budgetary item that I want protected–the library. Cut that last, after you cut education, police, official salaries”
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Big Value from Small Data in Little Time
I performed this text analysis in 30 minutes. Ironically, it has taken me longer to write this blog post than it did to quantify the text from all those emails. Yet the information and understanding I have gleaned will empower me as I make decisions on this important topic. A small investment in small data has paid off in a BIG way.