Author: Lynton Mack
Slam dunks and 3 pointers…
This is about cloud-scale analytics and business transformation so try to stick with the “Sportsball” analogy if you’re a data peep… It’ll make sense…
In 1891, a physical education teacher in Springfield, Massachusetts, was dealing with students stuck indoors on rainy days. The kids were going nuts, they were bored and starting fights in the hallways. Cabin fever had set in. So, James Naismith nailed up a couple of peach baskets at either end of the school gym, 10 feet off the ground so they were out of the way of the running track. He split his students into 2 teams of 9, gave them an old soccer ball and told the kids to try and score as many points as they could.
This was the birth of Basketball.
Basketball player George Mikan in 1945 via The Sporting News ArchivesAt first, there were not enough rules. Kids were tackling each other to the ground and it was more like rugby or a gladiatorial battle than the game we know now. James introduced more rules to control the game. You weren’t allowed to run with the ball, it had to be bounced (dribbled), tackling was cut out and the game quickly gained popularity.
The design decision to hang the basket at 10” defined the game, taking shape around taller players. Time passed and as the game (sport?) became more popular, super tall players came to dominate the sport (game?).
In the 1940’s and 50’s, Basketball was getting boring. 6’10” players, like George Mikan, would just stand at the basket, bat away shots or just drop balls in. It was too easy to score and dominated by giant players nicknamed the “Goons”. So, specifically due to the Goons, the rules changed to make it harder to score and more interesting. The act of goal tending, batting away a descending ball, was banned in 1945 but the Goons still dominated the game.
In the 1960’s dunking, slam dunks, entered the game. In the 1966, NCAA (College) championship game, the all-black Texas Western defeated the (last) all-white Kentucky team. At the start of the game, Texas Western’s, David Lattin, gets the ball and makes a move towards the hoop. The defender in front of him is Pat Riley who went on to win 9 NBA titles as a player, coach and exec. Lattin leapt up and flew over the top of Riley and SLAMMED the ball through the hoop. Lattin’s dominant athleticism made the defender look completely powerless. Lattin’s dunk and Texas’s victory were seen as direct challenges to the establishment. The game was changing again.
In 1967 the NCAA banned the dunk. The NBA didn’t but faced criticism about the slowness of play. That’s because the game was jammed up around the net. Play revolved around getting the ball to the “big man” to score. A lot of design fixes were suggested. No backboard, a curved backboard, a smaller basket, a bigger ball, a no scoring zone and even a height cap.
A rival organisation to the NBA, the ABA (American Basketball Association), tried a host of ideas to improve the game and found one that stuck; the 3-point shot. Any shots made from 22’ or more away was now worth 3 points instead of 2.
The 3 pointer was nothing like the dunk. The dunk required the size, strength and athleticism that is limited to only the best athletes in the world like Michael Jordan. But anyone could go home and practice their 3-point shots like the Pros. Physical dominance was not required to hit a 3-point basket.
The 3 pointer changed the game. It was an impressive to watch, it won games and, most importantly, it changed court dynamics by rewarding attempts from further out and breaking up the cluster around the net. It gave a chance for other players to shine. Suddenly, the game got interesting.
In 1976, the ABA and NBA merged and 2 years later they added the 3-point shot. Importantly, they did NOT design or rule the slam dunk out of the game. They allowed it to continue but they designed a counterbalance to these players’ dominance to create the game we know today.
Now, Basketball is more inclusive than ever before. Players have evolved as Basketball has evolved.
How does this history of Basketball relate to the ever-evolving game of data?
I’m not going to do a deep dive on the history of data and analytics, that’s for another day where you can look forward to fun things like Napoleon’s march on Russia, Dr John Snow’s spatial data and epidemiology, sigh… so good… but, I digress…
Basketball has stayed relevant and successful, and become a “sport” instead of a game, because it has undergone many revolutions, changes in rules and evolved over time. Obviously, change is the norm in “Data Sports”. Not one day goes past that some change in approach or technology is introduced and businesses and people need to continually evolve to be competitive in the game (sport?).
Until recently, data (generally) was the providence of specialists, the Michael Jordan’s of the data domain. It has been the playground of Analysts, Programmers, Engineers and Architects. I know, that’s been my life for over 20 years. I could slam dunk the life out of that data and make people go “wow”.
At the start, we were such superstars of the game that we didn’t need to worry about the rest of the team at all. We could write any schema, code, procedure, or process that we liked and didn’t have to think about anybody else. There was no version control, no support to consider, nothing. We didn’t document things because it was more important to land that next shot than talk about the last one.
And, like Basketball, the data game was jammed up around the net. To win, those key players took over the game, because of the way it was designed, and this created a bottleneck. The data game has always been slow because of this. Only the best (yes, we are the best) can make those shots and the rest of the team just has to hope they can get the ball to them. Access was limited to specific systems, databases and tools.
How is the game of data changing now and how do we continue to evolve with it?
This little diatribe isn’t about what makes a good cloud-scale analytics platform or how to leverage it to its best advantage. I’m not talking about vendors, software or configurations.
It’s about understanding that the game has now changed and what you need to do to win, or at least keep playing.
Cloud-scale data analytics is the “3 point shot” of Data Sports. It is changing the core dynamics of how we play in the data domain. Cloud-scale analytics does not design the slam dunkers out of the game either. They still have a pivotal role within the team. The tools and platforms we now have to bring in counterbalance to the impact of the Pros, allowing others in the team to not only contribute to wins but lead the team to victory. It removes the jamming up around the net and opens the game up.
Damn, it’s so freaking exciting now!
Just as a hoop can be set up in the driveway and kids can take practice shots like the pros, people can now practice their data analytics at home for free using cloud-hosted platforms. It is bringing in team members to a game that they could never have hoped to play before.
Cloud-scale analytics is a broad term for, well, moving data analytics services to the cloud. “Scale” is the important part of this. Unlike on-premises servers with finite resources, cloud services can scale to demand. And if demand is zero, it can be scaled down to zero – often a lot more important and overlooked than being able to scale up.
This new world doesn’t mean that we can now do fancy things with data that we couldn’t before. Instead, it means we can do them quicker and cheaper than before. We don’t need to have a commitment to hardware and support and so on. Cloud services can bubble up and then blow away.
The buzzword – “Data Democratisation”
It means to make all the data available “democratically” to all for decision-making. There are even Liberal and Conservative viewpoints on the approach to keep the political semantics going.
Here are 2 of my issues with this:
“Democracy” means voting, elections and such. The popular candidate wins. That’s not what is happening here. Resources are equally available to all of our population. Sounds like Communism to me. “Data Communism” wouldn’t really get the attention it should in a capitalistic organisation though.
Secondly, and political semantics aside, OK, you’ve got all the data. Now what? You don’t have the capability to do anything useful with it.
So, we need to consider the tools, training and data literacy using cloud-scale analytics to now provide the capability to people to act on their democratically hosted data. Once our team have the capability then they can have the ball AND do something with it.
Build a team
You now need to build a team to play a game that’s going through massive changes with the boundaries, balls, nets and rules changing on a brand spanking new playing field.
I wish I could charge for the number of times I heard “Where can we get another one of you?” over my career. While my ego really (really) appreciates it, I’m good at what I do but there are a lot better than me. But we are a limited pool of data dunking pros. There’s just not that many of us out there. Demand has been beyond our capacity for decades and is growing exponentially every day.
Now, instead of trying to get more professional data-dunkers (I’m hash-tagging that…), we need to build our team up around other players. Add more dynamics to the field. Break the bottleneck. Get more people taking shots.
You don’t need to find these new data players. You probably already have them within your organisation but they aren’t known because they haven’t been given the capability, training or told to “get in the game”.
Setting rules
You’ve delivered new capabilities, and training and picked out some new players but there is going to be a melee in the middle of the court if you don’t have some rules to control the flow of the game.
Without some rules, there’s going to be tears. IT is not the only team playing now. Someone in Accounts is taking long shots at Cost Control using ML. And so is someone over in Asset Management. They will butt heads and end up in a scrap. Penalties will be awarded and the whole game will slow down while we sort out whose foul it was.
Often, (OK, every single day) people in our client’s organisations still say “Data? That’s IT’s job.”
No, it’s not. In the history of data, rarely has it belonged to the IT department. They don’t produce it, they don’t consume it, they don’t value-add to it. They just manage it on behalf of the business “because data’s not our job”.
Don’t start playing unless there are some rules set to the game and that everyone playing knows them. This is where Data Governance comes in and why it is more important than ever before. Like the data, IT doesn’t own Data Governance; the business does. They just don’t know that yet.
The game has changed
Our role now, as Data Architects, is to design teams to win on this new playing field. This dynamic and exciting space.
It is to help our clients on their journey from a bunch of Goons jamming the basket to everyone taking shots from 22’ away.
We need to work with the players, let them know how the rules work, give them the capabilities and training and lead them to victory.
Go Team!
References:
The Yin and Yang of Basketball – 99% Invisible (99percentinvisible.org)
What Is Data Democratization? A Super Simple Explanation And The Key Pros And Cons (forbes.com)