In between sets of a professional singles tennis match, a court maintenance crew comes out to tend to the needs of the court. Among the checklist of validations performed is measuring the height of the net in select and vulnerable locations. In addition, there are also sometimes vertical support poles that stand against the net on both sides about a third of the way between the singles and doubles out lines. The crew measures and validates the distance from the outside line to the base of the vertical post to ensure it is exactly where it needs to be to maintain and support the height of the net during any let cords or disturbances that may come during gameplay (IE: players running into the net!)
This measurement of precision and accuracy is not only critical to the integrity of the net during gameplay, but directly impacts the athlete’s ability to maintain consistency in their performance, despite the location of the court. If these measurements are off—even by 1 centimeter—angles and judgment can easily become skewed, more balls will be hit directly into the net, and even serve percentages can plummet.
In short, without a high level of accuracy in setting up and maintaining the court, the tennis player will not perform at their highest level.
Now, with this backdrop set and you fully informed on the duties and responsibilities of the court crew—this is the part where I start talking about those two dreaded words: data quality.
(STAY WITH ME!)
Continuous improvement and improving continuously
Let us start by stating the obvious: there will always be data issues to work through. There can be periods of smooth integrations and automated processes, but regardless, seasons to validate, map, review, amend, or connect your data and/or data sources will always come again. In fact, this should really be a continued, non-stop workstream. So, if you think you’ve completely resolved all data issues, your operations and analytics run without fail, and you have no need to validate anything because AI does it for you—lets connect, because this is rare.
Regardless of this dreadful and looming shadow of The Data Monster, there are several things we can do to make sure our net heights are measured, and support posts are distanced accurately.
1. Walk out on the court.
It can be very easy to look at your data flows, workstreams, and operations team and assume everything is fine – based on carefully curated dashboards and fancy charts. But instead of getting lost in the sights of star athletes, giant crowds, blinding lights, and the smell of fresh tennis balls, we have to take the first step of walking out onto the court. We must question methodologies, we must look at the raw data inputs and outputs, and we must be okay with disrupting the flow of smooth operations for the sake of accuracy.
That moment of realizing your data is misaligned, inaccurate, mapped incorrectly, or not connected to the correct data sources can be a heavy feeling—one that can be time-consuming (and sometimes expensive) to resolve. However, the alternative of assuming good data quality can result in the entire foundation of business intelligence being established on faulty logic and fated to fail.
2. The right skillset in the right roles using the right tools & resources.
There are a lot of bodies on a tennis court during a match: a minimum of two players, one chair umpire, six different ball-persons every set, and up to ten additional officials; and yet, it’s an additional crew member that comes out on to the court to measure the net. Additionally, while it’s surely possible for the court crew to measure net height with a ruler, yardstick, or even protractor (I’d love to see this!), the most convenient tool is also the most obvious: a tape measure. In a sport where the athletes only take up roughly 10% of the bodies on court, why is an additional crew member needed to ensure the court is a flat constant in all the variables of the match?
The answer is simple: it’s all about the roles. Each body on the court has a specific duty and responsibility to ensure the match is fair, accurate, and documented properly. Supplementing the role of maintaining court conditions to a line judge would be about as effective as assigning Human Resources to increase the subscription consumption of your product offering. The right skillset in the right role using the right tools is critical to delivering actionable insights based on accurate and thorough analysis. People who are charged with organizing, modeling, and analyzing complex data from a variety of sources not only have to have the hard skillset required to run the analysis but the soft skills required to always be asking the “why” of any process and be ready to defend the accuracy of any output.
Lastly—as a plug for my fellow CS Analysts—having Customer Success focused analysts as part of your CS organization (or better yet, CS Center of Excellence) is a must. Company-wide collaboration with other business units analysts is important to ensure alignment, but as part of the Customer Success business unit, the CS analysts will be naturally customer-centric and focused on the crucial leading indicators that will enable CSMs to thrive, and in turn, enable your customers.
Seemingly ordinary moments lead to greatness
Tennis is a sport where the most seemingly ordinary point, called in by millimeters, can lead to a profound impact on the game, the set, the match, the tournament, the season, the career. Greatness and legacy in the sport comes from a collection of these moments, supported by countless hours of on- and off-court preparation. The seemingly ordinary days we go through as Customer Success Analysts – mapping data, analyzing customer behaviors, automating reports, evaluating data sources, building predictive models, working with finance, sales ops, sol ops, dev ops, marketing—these moments are shaping the foundation that can change the direction of a brand. This, in turn, can lead to customers achieving their objectives using your product, as they are enabled by the results and insights that come from the Customer Success organization’s ability to lead the charge with consistency, tenacity, and accuracy.