Recently, we welcomed Ken Jee on the Insights x Design podcast. Ken is a fascinating guy for myriad reasons. One is that he trained to go pro in golf during his younger years. We asked Ken about that unique experience, exploring takeaways for #datafam, and he said this.
I have continued to reflect on the interview since then. Unlike Ken, I never made it far in the gentleman’s game. I probably spent more time in the sand and water than I did on the green. Still, I may offer a complementary perspective because I went pro myself. Not as a golfer, mind you. But as a caddy. I worked at four top-ranked golf courses in the intermountain west.
Let me suggest a few additional ways that golfing can inform data analytics.
Find the Flag
Golfers hit the ball in the hole with as few hits (“strokes”) as possible, and all 18 holes on a golf course are marked by flags. Why? Because flags allow them to see the objective from a distance. Without flags, most shots would be little more than guesswork.
It makes sense, then, that golfers first find the flag before lining up a shot. In particular, the flag enables them to approximate three key bits of information.
First, how far away is the hole? Some holes are 800 meters from the tee box, where golfers launch their first shot. Some are only 80. The distance matters because the purpose is not to blast the ball into orbit. Again, it’s to drop the ball into the hole, and distance largely dictates club selection. More on that in a moment.
Second, is the hole straight ahead, or does it lay to the right or left? Few fairways are perfectly straight. Many “dog leg,” creating a curve that requires strategy.
Third, what hazards are near the hole? Most hazards are ponds, streams, and sand bunkers. There can also be bushes, tall grasses, trees, and wind. Obstacles may be a better term because hazards are rarely hazardous in the literal sense of the term. There are exceptions, however. The “most dangerous golf course in the world,” located near the Demilitarized Zone (DMZ) between the Koreas, is surrounded by active landmines.
These three questions enable a kind of SWOT analysis that will ring familiar to anyone who does project management: Where should we end up, how do we get there, and where do we need to be cautious? It’s a SWOT analysis that occurs more than once. It occurs before every shot the golfer makes.
Similarly, data analysts need to know their objectives well and the roadmap to achieve them. At each stage of a project, they need to “find the flag.” Otherwise, they are just doing guesswork, which, as in golf, can lose the game.
Pick the Right Club
Different clubs make the ball fly and spin in different ways. Clubs with low numbers (e.g., the “woods” labeled 1-3) hit the ball low and far, ideal when the hole is far in the distance. Clubs with higher numbers (e.g., the “irons” labeled 7-9) do not hit the ball very far, but they do hit the ball high, which can clear hazards and make the ball stick to the green. There are also specialized clubs like sand wedges, pitch wedges, and gap wedges, which can hit the ball even higher and shorter, and they are rarely used with a full swing. Perhaps the most specialized club of all is the putter, which rolls the ball across a green and (if the gods of the highlands will it) into the hole.
The assortment of clubs required in a game is what kept me in business as caddy. They can be heavy to carry, and golf is already hard enough. But imagine how much harder it would be with only a putter. Golfers would basically be playing field hockey, wracking up hundreds of strokes above par.
Like golfers, data analysts need to master a range of tools and, no less important, learn to use the right tool for the right task. Andrew Ojeda at Google emphasized that point when he chatted with us the other day. To paraphrase Andrew, Tableau is not really a platform for calculations, so don’t do calculations there. Use Tableau for what it does best: data visualization. In other words, don’t be driving with a putter, or putting with a driver.
That goes for AI tools as well.
Surmount Setbacks
Golf is full of surprises, some of which are fortuitous. I have seen holes in one, or shots that have come breathtakingly close to it. I have seen a ball take a bad turn, clang off a metal stanchion, and ricochet into the middle of the fairway. I have seen putts drop in the hole from basically the state of Nebraska. Yet, even at professional levels, there are always other surprises—setbacks. A golfer can momentarily forget their mechanics and hit a “fat” shot. They can hit a promising shot that somehow ends up directly behind a tree. They can struggle in the mental game and fall into a multi-round slump. The possibilities for setbacks are endless. But good golfers find ways out, around, over, or through.
Quick story about a tree. I was caddying for a gentleman whose ball landed snug against a trunk, blocking the green. He could have taken a mulligan: pick up the ball, drop it for a better position, and add a stroke to the score. But this man was inventive. Though using right-handed clubs, he reversed his position, smacked the ball with the back of an iron left-handed, and plopped the ball in the fairway. The hole was now within easy reach, as we knew from the flag.
Setbacks will require data analysts to be inventive as well. Analysts may need to try unconventional solutions, or at least something outside of their comfort zones. Of course, not every setback vanishes with a single stroke of creativity. Good analysts, like experienced golfers, know when to take the mulligan: pull the plug, deal with the consequences, and try again.
Setbacks are also a call to courtesy. After a shot, courtesy demands that golfers not leave irregularities for others, a task that typically falls to the caddy. Caddies will fix divots, pitch marks, footprints in the sand bunker, and more. They give the golfers who follow an opportunity to score their best, without compromising the course.
We can apply this principle to data analytics. Fix your mistakes. Remove irregularities in the processes and systems you work with, if you can. Help your colleagues to succeed, without expecting any praise, favor, or credit in return. When you move to a new role, do not leave damage behind you.
Consult on Critical Decisions
Golfers and caddies are a team. True, caddies do not shoot the shots. But they do help call the shots. They will often research the course, estimate distances to the flag, advise on club selection, and lend a second pair of eyes when the golfer needs to “read” the green, determining where to aim the putt. (For you non-golfers who are still reading, greens are not pancakes. They contain bumps and curves, so golfers usually need to putt away from the hole, then hope geography will roll the ball into the hole.)
Reading greens is how I really pocketed tips as a caddy. My courses butted up against the mountains, which can affect how a ball rolls: the ball would “want” to break ever so slightly to the east. This often came as a surprise to out-of-towners, who appreciated opportunities to consult on critical decisions like putting. “Golf is a game of inches,” they say, because scores make or break on the green. But inches might be too generous. Putts can come down to mere millimeters.
Now, data analysts should not burden their colleagues. They must be value-added professionals. Still, as a general principle, it’s totally fine to involve competent peers in critical decisions. Do they see the same trends? Do they agree on the strategy? Do they have on-the-ground experience that can help achieve objectives? Competent peers may offer helpful insights. But just as a putt depends on the golfer, not the caddy, take responsibility for project execution.
Be Patient and Persistent
Golf has attracted some mind-blowing youngsters. Tiger Woods became the youngest Masters champion ever, donning the green jacket when he was just 21. Lydia Ko was just 17 when she topped the rankings for women—making her the youngest player ever to hold that spot. She had won her first tour event two years earlier. These achievements are inspiring, yet viewers do not see the intense practice it took to get there. Everyone who excels at the game has logged hundreds, if not thousands of hours on the putting green, driving range, fairway, fringe, and sand bunker. It takes patience and persistence, under a range of conditions, to develop the wide-ranging skillset for golf.
So too with data analytics. Here is how Google broke it down.
The skillset will vary somewhat by industry, company, role, and personal interests. Even so, data analysts need to know a lot of stuff. On the technical side, they need to know SQL, Python, machine learning, and statistics. Determining what to apply when (that is, picking the right club) takes critical thinking and problem solving. Then there is the challenge of communicating what they know through reports, dashboards, and verbal presentations, skills that our podcast guests have hammered on repeatedly. Every deliverable has an audience, and the (in)effectiveness of that deliverable depends on audience perceptions, values, and needs. That’s just the way it is.
As in golf, building a well-rounded skillset takes time. Some skills in data analytics will be more enjoyable than others, just as driving is much more fun (in my opinion) than putting, and many mistakes will be made along the way. But approach your career with a growth mindset, and there is nothing in the visual above that you cannot master, no matter your age or experience.
Wrap Up
These are just a few examples of how golf can inform data analytics. You may not like golf. You may not have swung a club yourself. Nevertheless, I would encourage you to find the flag, pick the right club, surmount setbacks, consult on critical decisions, and be patient and persistent in your analytics career. These principles from the caddy shack can take you to the next level of professional growth.
Do you see additional connections between golf and data analytics? Or data analytics and another sport you play? Let us know in the comments.