I’m a major baseball fan and love to analyze all the statistics the game offers. Pioneers at the front of the statistical analysis trend in baseball include Bill James (“The New Bill James Historical Baseball Abstract”) and the late Chuck Waseleski. When I was a kid I couldn’t wait for my dad to be finished with the Sunday Sports section of the newspaper so I could study the Baseball Notes column. My favorite part of the report were Waseleski’s obscure tidbits, which I gobbled up. As I fondly recall, observations such as how many dents Wade Boggs put in the wall at Fenway Park in1984 or how many batters Roger Clemens struck out from the seventh inning on were waiting to be consumed. As a teenager growing up in the 1980s, these were very important details to me, and nobody had these vital statistics at the time except for Waseleski.
Sabermetrics have, over the past 15 years or so, brought a new perspective on how baseball’s numbers are analyzed. “Insightful” sabermetric data has become statistical doctrine for fans everywhere. A variety of secretive complex algorithms are used by teams to build a strong relief staff, assemble a murderer’s row type of lineup against a left-handed starting pitcher, or recruit a talented stockpile of prospects. The vast amount of statistical records creates endless research possibilities. How teams interpret the information may allow them to make more effective organizational transactions, but those decisions have to be done wisely by knowing what data provides the most value.
You may ask, “What does all of this have to do with someone who works in Cisco IT?” For the past decade, Big Data has been a popular term in the IT industry. Trying to make heads or tails out of all that data has led to the relatively new term “Insightful Data.” To me, sabermetrics and Insightful Data are very similar. IT teams and businesses are scrambling to find out how to interpret all the Big Data and cull the most value from it.
For example, in the contact center technology domain, agents traditionally have often been measured by how many calls per shift they answer. While this is applicable data, how insightful or actionable is it? While one agent may answer more calls than another agent, this data doesn’t measure the agent’s overall effectiveness and, thus, isn’t very insightful. These days contact center agents may have to handle calls, chats, emails, video calls, SMS, respond to social media, etc. So, what if there was a new algorithm that could measure their overall effectiveness, or value?
Baseball sabermetricians have come up with the relevant algorithm OPS (On-Base Plus Slugging) to measure a hitter’s performance. OPS combines a player’s on-base percentage and slugging percentage to measure that player’s ability. Previously if a player had a high batting average, that measurement was the outlier of an all-star performance. OPS (a recent metric) provides greater awareness into the hitter’s value using a combination of two significant statistics.
The same could be said for a contact center agent. How can an agent’s performance be more effectively measured with something similar to OPS? Possibly, a more decisive algorithm for measurement could use some combination of “number of calls answered,” “average handle time,” “number of chats answered,” “emails answered,” and “social media interactions” along with customer satisfaction scores or sales placed with that particular agent. Digging deeper with a new measurement may indeed help business executives understand where the all-star agents exist, not just the ones answering more calls.
Corporations gather infinite amounts of critical data every day. Business systems that create Insightful Data, which lead to highly valued results, are constantly being explored. Capturing relevant statistical information, to analyze the health of your data center infrastructure or network efficiency or identify the hidden needs of your best customers, is advancing at a rapid pace. Much of this constructive information should be examined by appropriate teams across IT organizations to make corporate infrastructures more secure, networks more efficient, data centers more green, customers happier, and the list goes on and on.
What pertinent data do you think could be beneficial or insightful in your area of expertise? How do you see this area developing in the next 18 to 24 months? What are your thoughts around Insightful Data and how it can help IT departments as well as provide overall business value to your organization?
Great questions! Working in a contact center environment I never noticed the importance of metrics in data. But as you previously mentioned what information is actually pertinent to customer and employee success?
In the next 18-24 months improved reporting and data mining to pull useful information from metrics would be valuable. Instead of digging through static information and canned reporting, it would be nice to find other innovative ways to gather information.
Perhaps, you can also do the analysis using combinatorial testing. You can state the parameters and thier coresponding values. then make 1-way interation test suite, 2-way, 3-way, 4-way,5-way, six way.
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