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In recent years, data has been the world’s hottest commodity. Money has gravitated towards companies that collect it, companies that analyse it, and the data infrastructure companies that provide the digital plumbing that makes it all possible.
In the last five years, data infrastructure startups alone have raised over $8 billion of venture capital, at an aggregate value of $35 billion.
But what are they actually for?
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Most investors will talk about how data can, in theory, be used to derive trends. Others may talk about how data will change the world, without filling in the blanks on how.
I don’t disagree. I’ve worked and invested in data companies for my entire career.
But I think they are missing something big. There is a powerful disruption coming; perhaps, the most powerful since computerized transaction processing was invented in 1964. Predictive transaction processing is about to upend the model of the last 57 years of computing and change the way we live, work, shop, and entertain.
For businesses to remain relevant and competitive, they not only need to be able to predict customer behavior and preferences, they also need to rely on predictive transactions to automate most of their business interactions, i.e., taking automated actions while selling to or servicing the customer.
A transformative new model
Since the dawn of computing, transaction processing has been performed in much the same way. The user makes a request, the request is processed, and if you’re lucky, afterwards the user’s choices are analysed.
This is what happens across many platforms today.
When I buy a product from Amazon machine learning may be used to make recommendations. But the decision to purchase is fundamentally something that I, the customer, must make. When I browse Netflix, it will algorithmically suggest content that I may like to watch, but once again I must make the choice to hit play.
We call this “artificial intelligence” but I think this is not smart enough. The real transformation will happen when we move to a predictive computing model.
Picture this: You’ve just got home from work, and an Amazon delivery truck arrives at your door, carrying the 25 household items, from dry groceries to cleaning supplies, you’ll need that week, informed by your in-depth customer profile. Any of the items you don’t need (an unlikely occasion given the enhanced machine learning) can easily be returned – information which adds to the database that continually improves the engine’s learning and ability to predict your behavior.
The use case is clear – when transactions move from enhancing decisions (i.e. recommended bundle items) to predicting purchase decisions, consumers will be able to let Amazon handle their daily purchases, giving them back time in their busy lives. In terms of logistics, last-mile delivery technology will ensure that people get what they want when they need, easing the traffic congestion caused by delivery trucks currently hindered by uncertain time frames and unavailable customers.
Given Amazon’s sophisticated logistics and data assets, this scenario isn’t hard to imagine. Amazon has data on your shopping habits from a lifetime of purchases. It has your credit card details. And it has the unrivalled ability to ship goods quickly at scale.
The same can be true for Netflix, and other entertainment platforms like Spotify. They know our habits, so why wait for us to tell them what they already know before they entertain us?
As Benedict Evans says, a computer should never ask a question it knows the answer to.
This, however, is only the beginning. The Predictive Transaction Processing model is not just an opportunity to improve our lives, existing systems and business models. It will be critical for unlocking the transformative technologies of the future.
Take autonomous vehicles, for example. We are not going to reach “Level 5” autonomy if the car only has its own built-in sensors to rely on. We need all the cars, from the human-driven ones to cloud learning vehicles, for the risks on the road ahead to be computed using data collected by every autonomous vehicle. And we need this computation to be predictive, to steer our vehicles in anticipation of the dangers that lie ahead. By acting using the predictive model, based on data, automotive accidents can be a thing of the past.
Predictive transactions will become crucial to industries from DTC commerce and entertainment to transportation, logistics, and even healthcare – as each stands to reap the benefits from this incredibly incisive insight into their customer/client base and their habits.
Putting the building blocks in place
There are already companies taking tentative steps towards the predictive future.
Most notably, there is ByteDance’s TikTok. With $34bn revenue in 2020 it is the most profitable predictive transaction processing app ever created. Open the app and you will be presented with an endless stream of autoplaying short form videos. As you watch, the algorithm will learn what you like based not on your stated preference, but on your revealed preference.
In other words, if you’re spending longer watching videos of pets than people singing or performing stunts, the app will show you more pets, without you ever needing to press play or type words into a search box.
Companies that are being built today need to follow ByteDance’s example and invest and build the key technologies that will move us towards the Predictive Transaction Processing model.
As part of the shift from user-instrumented interactions to decisions made by learning systems and data, we will need to retool and redesign the entire technology stack.
For example, we will need improved machine learning models that are more precise in their predictions, as marginal gains will make the difference when they are cascaded through a logistics chain. We will also need learning systems that can look backwards and correct for previous mistakes, so that errors are not compounded.
We will also need to replace long-held sacred cows, such as the J2EE standards that have unpinned ecommerce for a generation. Applications based on learning from data are very different to those based on the traditional relational database. We will also need new development and debugging tools, such as new lower-level programming languages to enable us to interrogate data more effectively.
Application integration will also increase in complexity as apps will be entirely driven by data rather than design.
And ultimately, there will need to be a step change in the reliability of real time transaction processing applications. If predictive data is to be mission critical, we need platforms and products that reduce downtime, enable instant recovery and have automatic failover capabilities.
The real opportunity
The Predictive Transaction Processing revolution is imminent. It may be the most exciting innovation that enterprise computing has ever seen. When the technological building blocks fall into place and apps finally come to market, the impact will be felt immediately.
The number of transactions on predictive platforms will skyrocket. There will be enormous opportunities to improve the efficiency of existing systems, and a lucrative role for the ecosystem of companies that create the middleware that make it possible. And the SaaS enterprise platforms that dominate today will risk becoming obsolete.
So it’s time to embrace Predictive Transaction Processing, and wise investors will take a lesson from this new paradigm: It’s time to look forward, and make decisions now about where to put your money knowing what is coming.
Alfred Chuang is General Partner at Race Capital (Databricks, FTX, Solana, Opaque), where he invests heavily in data infrastructure. Prior to this he was co-founder and former Chairman & CEO of BEA Systems and led its acquisition by Oracle for $8.6 billion.
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