Helping Your IT Clients — And You — Achieve Inbox Zero With Big Data Analytics
By Frances Angulo, Director of Support and Education, Datameer
More money, more problems. As you move up the workforce ladder, you can pretty much expect one thing to be true: your email inbox is going to explode. More than you thought was possible. With all of the talk around Big Data, I thought it would be an interesting experiment to see if I could use Big Data analytics to figure out how to combat (or at least better prepare myself for) the inevitable.
It was pretty easy to determine that between the hours of 9 a.m. and 6 p.m. EST, I receive an email on average every 13 minutes and 68 percent of them are considered “actionable.” For the purposes of this analysis, I defined actionable as “requiring a response.” The problem with being productivity obsessed is that email has never quite fit efficiently into my workflow. I’m constantly faced with the challenge to make a split second decision. Do I answer this now or do I defer, mark unread, ignore, flag, tag, label, color code, remind me later, turn off email, or throw my phone out the door?
Big Data?
Believe it or not, my email counts as Big Data. I face the challenge of decreasing time to decision-making whilst the complexity of my information is increasing. I have email coming in at a high velocity from various users at all levels of priority who are making requests, comments, and sending messages both overt and subliminal in nature.
Monitor Your Email “SLAs”
Analyzing “From:To” is a great place to start, but response performance is way better. I built some basic analytics on who is sending me the most email and what percentage of their emails require a response. You can do this to measure your “favorability” or even “VIP” list. I created almost a “service level” agreement for my emailers. What’s most interesting about that is not validating your priorities, but identifying areas in which you don’t realize should be priorities. I seem to give V/C level emailers almost instant responses or in less than one hour, but some of my top performing direct reports received responses in an average of 3 to 4 hours — that’s something for me to work on.
Look For Influx Trends
Taking a step back, historical trends in my email seemed to be pretty consistent over lengthy timeframes. I took a year of email data and compared month over month results but didn’t find any notable standard deviation. With that good set of information, I was able to identify sending and receiving trends to consider strategies to keep my inbox in a manageable place and even predict when things will get out of hand.
React To The Data
What I found in my time trends are some actionable insights, trends, and precursors to what will surely be email avalanches awaiting me. Here are some ideas to keep your inbox under control:
- Strategy 1: Plan your time accordingly
Consider putting blocks on your calendar proactively or reactively to prepare for predictable influx.
- Strategy 2: Set realistic expectations
Prepare “canned responses” that allow you to predict a realistic response time like, “I got your email, I should get back to you by EOD Thursday.”
- Strategy 3: Collaborate with your peers
Compare your email trend results to the results of others and choose your send times wisely and adjust your expectations of response time.
I was able to build this insight in a matter of minutes and I even shared the analytics with my colleagues. They’re able to plug in their email credentials (privately of course) and simply repurpose what I’ve already built to compare results. I’m looking forward to enabling a more stable email ecosystem!
For information on the Datameer Big Data insights information platform or its partner program, visit http://www.datameer.com/index.html.