I’ve been spending a lot of time recently working with data. For some clients I’m helping to assemble data from multiple sources across their enterprise to answer business questions like how does clickstream behavior impact revenue. For other clients, I’m strategizing about using aggregate data to create new opportunities that provide added insights and actionable steps toward increasing profitability. And for fun, I’m slicing through data to gain greater understanding of events I’ve missed or simply things that I’m curious about.
This last effort is what got me typing today. As I sorted through Tweets and scoured the web for information about the recent DAA Symposium in San Francisco, I was heads down looking at data. I wanted to accomplish two very specific objectives: 1) to validate a new calculated metric that I’m working on, and 2) to simply find out how the event was and what type of knowledge was being shared.
So I turned to five different tools to try to find the answers that would satisfy my curiosity.
My research quickly yielded data that showed how many Tweets with @DAAorg and #SanFranDAA were flying; who the top contributors were; and in some cases how many impressions were created by these messages across the Web. As I researched more, I became more and more focused on the numbers and sought to find the story within the data that would tell me more. As I dug deeper, my tracking spreadsheet started to grow and I began to see that across the five tools, each had significant gaps in the data that they provided. While most were able to reveal the total volume of mentions for my specific keywords, there was a great deal of variation in what they found. Further, the data produced by these tools was often lacking metrics that I wanted to perform my calculations. But what really struck me was the fact that amid all this data I was looking at, very few of these tools told me anything about the content of what was being said. Sure, I could scroll through the individual Tweets and see the content, there were also lists of top keywords showing me what was mentioned most, and even in a few cases there were word clouds that highlighted commonly mentioned terms and their relationship to my search query. But through all of this data I still didn’t know what really happened at the DAA Symposium in San Francisco. I needed someone who was there to fill in this essential piece of information.
But I was still determined to produce something from my exercise in curiosity, so I sent out a Tweet with a quantitative perspective on what I had discovered. Almost immediately, I received a response that asked… “@johnlovett @DAAorg so what’s the qualitative story?” I too had this question in my mind and with the help of this one innocuous Tweet; I realized that every data exercise can benefit from both the quantitative and qualitative sides of the story. Either one alone is woefully insufficient. By digging into the data, there were things that I could see that helped me to understand what happened at the event, and I was even able to gain a better understanding of the awareness created by the event using my calculated metric. However, what I failed to capture in looking solely at the data alone was the qualitative message. Through all the Tweets and data I analyzed, I learned some very interesting things, but the results of my analysis were hollow without a first hand narrative to accompany them.
While this may be painfully obvious to many, all too often I see organizations lose sight of this fact. They expect digital analysts to amass data and crunch numbers to uncover revelations about the business. But in many cases, these analysts don’t have the benefit of understanding the strategy behind the numbers or the context of a story that they data can support. This makes their jobs incredibly more difficult and ultimately it leaves their analysis with a hollow void that is begging for a narrative. In my experience, I’ve found that this narrative comes from collaboration between analysts and business stakeholders who take both sides (the quantitative and the qualitative) to showcase results in a manner that is not only meaningful, but also leaves a lasting impression.
So the next time you’re itching to deliver that beautiful analysis you just created…or if you’re listening to an eager analyst share new data…ask yourself if the perspective you’re hearing considers both the quantitative and qualitative sides of the story. If not, ask for more.
What do you think?
John Lovett DAA President