What is the meaning of “real time?” For most of us, the definition is simple. It’s the time at which something is taking place “right now.”
But there’s more to real time than meets the eye. I’m a golfer, so I think of it like this: to take the best possible swing, golfers need to understand the direction of the wind, the distance to the green, and the position of water and sand traps. And they need to process all of this information quickly and simultaneously. Taking the best aim requires considering all necessary information, putting it in the right context – the specific context – and analyzing the possibilities within a matter of seconds.
In this scenario, the swing is occurring “right now.” But the availability of that data – and the golfer’s instantaneous processing of that data – that is “real time.”
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I emphasize this distinction because there’s never been a more important time to understand the concept of real time. With the development of artificial intelligence, LLMs have become a commodity as standardization, reduced costs and increased versatility have made them widely accessible. But the truth is that without accurate data information, delivered within the right context at millisecond speeds, LLMs are useless. Data is to LLMs what the incoming information about the wind, sand traps and distance to the green is to the golfer.
Why AI needs real time data
Real time data allows AI applications to access the most current and accurate information–providing the context the AI needs to generate more relevant answers. Without real time data, AI systems can’t do what they do best. Without context and relevancy, AI won’t make the profitable trades on Wall Street, alert manufacturing plants of operational malfunctions, or provide customers with chatbots capable of solving problems and answering questions.
This is not an overstatement. Plenty of applications have flopped because they could not access data in real time. Google Flu Trends aimed to predict outbreaks by analyzing search query data, but neglected to contextualize the searches. It could not figure out why people were searching for a certain number of words – and therefore it repeatedly overestimated flu predictions and was discontinued in 2015.
More recently, Rabbit R1, an AI gadget designed to perform tasks similar to a smartphone with simple voice commands (think of ordering an Uber), faced intense criticism because it couldn’t even provide quick and accurate weather or traffic updates. It also became infamous for hallucinating, even when using its camera, confusing Doritos for tacos and failing to read basic text. All of this explains why AI’s capacity to access real time data –and process it– has evolved from being a luxury to an absolute necessity.
Challenges with accessing real time data
With AI, every millisecond counts. But retrieving precise information from amounts of data sets is no easy task. Businesses and organizations typically store their accumulated data from multiple sources and in different formats. And the amount of data they have is vast. So vast, in fact, that in our industry, we break down types of data into three categories – hot, warm, and frozen, based on how often it is used by AI. Hot data is the most often used, and therefore, traditionally, the data that’s most easily accessible. By contrast, frozen data is rarely used and doesn’t need immediate processing. Organizations typically have much more frozen data than the hot or warm variety, and that frozen data is usually stored in “lakes,” or cheaper and lower performing storage tiers that are very good at storing vast amounts of data.
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Retrieving frozen data from a lake can be both time consuming and costly. Data lakes are plagued with latency issues and are not meant to handle multiple simultaneous data access requests, which leads to data retrieval delays. Meanwhile, LLMs demand that frozen data be thawed immediately and be accessible for contextualization with AI conversations that are happening in real-time. Regardless of where it is stored, data curation and blending needs to happen in milliseconds, indefinitely, otherwise we will continue to see more failures like Rabbit R1.
How to access data real time, for real
The most important step to solving these challenges is to reduce complexity–and that means storing all data in a single place. Keeping frozen data in one platform and hot data in another makes it much more difficult to integrate and mix data from multiple sources when needed to power an LLM. Cloud-based data platforms are a good solution; they can provide unlimited storage capacity that can scale up or down based on demand.
Once you have all of your data stored in one place, your developers should focus on building the capability to access frozen data as easily as hot data. Techniques such as indexing and partitioning can help by enabling quick searches and parallel processing.
Fast retrieval from data lakes is an essential capability for AI. But there are other capabilities that speak to the importance of having an organized data structure that consolidates all data in one space. Take vector data, or multidimensional data points, which are key to helping LLMs learn patterns and better consider context before generating responses. Specialized Vector Databases (SVDBs) were developed to handle vector data and feed it to LLMs. But because they only handle vectors, they are not integrated into your overall data architecture. This results in excessive data movement and redundant data, which increases labor and licensing costs as well as limits on query power.
Not addressing the disconnect between your data sources will lead to costly and time consuming data movement, which is a terrible mix for AI. If you have different data sources storing massive amounts of data, you need a system that can retrieve data in different formats from all your sources with incredible speed and flow.
And there’s so much at stake. There’s an AI arms war occuring in Silicon Valley – and around the world – as companies of all sizes race to develop their own LLMs and AI applications. The competition is fierce, and companies that do not develop the most efficient methods for storing, retrieving, and processing data will have a very difficult time keeping up. If we return to our sports analogy, not investing in this capability is like dramatically reducing the golfer’s capacity to listen, feel and see. Give your LLM the capabilities to ingest the most high-quality and accurate information at the fastest possible speed, and it will swing at that ball with the strength and precision you need to land directly on the green.
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