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Forecast trading is the next big technological revolution

 


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In recent years, data has become the most popular product in the world. Money is drawn to the companies that collect it, the companies that analyze it, and the data infrastructure companies that provide the digital plumbing that makes it all possible.

Over the past five years, data infrastructure start-ups alone have raised over $ 8 billion in venture capital, bringing the total to $ 35 billion.

We know the names of the largest companies in this area. They include Databricks, Snowflake, Confluent, MongoDB, Segment, Looker, and Oracle.

But what are they really for?

Most investors, in theory, talk about how to use data to drive trends. Others may talk about how data changes the world, without filling in the blanks.

I disagree. I have been working and investing in a data company for a long time.

But I think they are missing something big. There is a great deal of confusion. Probably the most powerful computerized transaction processing since it was invented in 1964. Predictive transaction processing is overturning the computing model of the last 57 years and changing the way we live, work, shop and entertain.

To stay relevant and competitive, companies need to not only predict customer behavior and preferences, but also rely on predictive transactions to automate most business interactions. This means that you need to take automated actions while selling or servicing your customers. ..

Innovative new model

Since the dawn of computing, transaction processing has been performed in much the same way. When a user makes a request, the request is processed, and if you’re lucky, the user’s choices are then analyzed.

This is what is happening on many platforms today.

When you purchase a product from Amazon, you can use machine learning to make recommendations. But the purchase decision is basically what I, the customer, have to do. When you browse Netflix, the algorithm suggests what you want to see, but you have to choose whether to play it again.

We call this artificial intelligence, but I don’t think it’s smart enough. The actual transformation happens when you move to a predictive computing model.

Imagine this. You’ve just returned home from work, and an Amazon delivery truck arrives at your door, carrying 25 household items, from dry groceries to cleaning supplies. The week will be notified based on a detailed customer profile. Items that you don’t need (which happens rarely due to enhanced machine learning) can easily return information that is added to the database. This continually improves the ability of the engine to learn and predict its behavior.

The use case is clear when a transaction moves from decision-making enhancement (that is, recommended bundle items) to purchasing decision prediction. Consumers can let Amazon handle their daily purchases and regain time in their busy lives. In terms of logistics, last mile delivery technology ensures that people get what they need, when they need it, caused by uncertain timeframes and delivery trucks that are currently hampered by unavailable customers. Traffic congestion is alleviated.

Given Amazon’s sophisticated logistics and data assets, it’s not hard to imagine this scenario. Amazon has data about your shopping habits from the lifetime of your purchase. It has your credit card details. And it has an unparalleled ability to ship goods quickly on a large scale.

The same applies to other entertainment platforms like Netflix and Spotify. They know our habits, but why wait to tell them what they already know before they entertain us?

As Benedict Evans says, computers shouldn’t ask questions that know the answer.

But this is just the beginning. Predictive transaction processing models are more than just opportunities to improve our lives, existing systems, and business models. It will be important to unleash the innovative technology of the future.

Take an autonomous vehicle as an example. If your car only has reliable sensors built in, you will never reach level 5 autonomy. To use the data collected by all self-driving cars to calculate the risk of the road ahead, you need all cars, from human-driven cars to cloud-learning cars. And this calculation is predictive, and you need to steer your vehicle in anticipation of the dangers ahead. By acting with data-based predictive models, car accidents can be a thing of the past.

Predictive transactions are critical to the industry, from DTC commerce and entertainment to transportation, logistics and even healthcare. Each is in a position to benefit from this incredibly keen insight into the customer / client base and its habits.

Place building blocks

There are already companies that are taking tentative steps towards the future of forecasting.

Most notable is the ByteDancesTikTok. With $ 34 billion in revenue in 2020, it’s the most profitable forecast transaction processing app ever created. When you open the app, you’ll see an endless stream of short-format autoplay video. As you look, the algorithm learns what you like based on the preferences you reveal, not the preferences you stated.

In other words, if you spend more time watching your pet’s video than someone who sings or stunts, the app doesn’t require you to press play or type a word in the search box. , Show more pets.

Enterprises built today need to invest and build key technologies to move to a predictive transaction processing model, following the ByteDance example.

The entire technology stack needs to be rebuilt and redesigned as part of the transition from user-measured interactions to learning systems and data decisions.

For example, cascading through a logistics chain makes a small difference, so you need an improved machine learning model with more accurate predictions. You also need a learning system that can look back and correct previous mistakes so that the errors don’t get worse.

We also need to replace the long-held sacred cows, such as the J2EE standard, which has been fixing e-commerce for generations. Applications that are based on learning from data are very different from applications that are based on traditional relational databases. You will also need new development and debugging tools, such as new low-level programming languages, to enable you to explore your data more effectively.

Application integration is also complicated because apps are completely driven by data, not design.

And finally, you need to gradually change the reliability of your real-time transaction processing application. If your forecast data is mission-critical, you need a platform and product that reduces downtime, enables immediate recovery, and has automatic failover capabilities.

Real opportunity

A revolution in predictive transaction processing is imminent. This may be the most exciting innovation that enterprise computing has ever seen. Once the technical components are in place and the app is finally on the market, the impact is immediately noticeable.

The number of transactions on the forecasting platform will skyrocket. There is a great opportunity to improve the efficiency of existing systems and it has a beneficial role for the corporate ecosystem that creates middleware that enables it. And today’s dominant SaaS enterprise platform runs the risk of becoming obsolete.

Therefore, it is time to adopt predictive transaction processing. Wise investors will learn lessons from this new paradigm. It’s time to look forward to knowing what’s coming and deciding where to put the money right now.

Alfred Chuang is a general partner of Race Capital (Databricks, FTX, Solana, Opaque) and has a significant investment in data infrastructure. Prior to that, he was co-founder, former chairman and chief executive officer of BEA Systems, leading Oracle’s $ 8.6 billion acquisition.

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2/ https://venturebeat.com/2021/09/26/predictive-transactions-are-the-next-big-tech-revolution/

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