How Businesses Can Create Data Frameworks for Real-world AI

How to make real-timeAI a reality?
How Businesses Can Create Data Frameworks for Real-world AI

The rapid pace at which companies are innovating has led them to gather increasingly large amounts of data on their operations. This data has become critical for improving outcomes through optimizing processes and improving decision-making, but these outcomes are dependent on the speed at which data can be gathered, transformed, and then analyzed.

The most significant challenge is using this data to build real-time AI applications. Businesses are evolving quickly and as a result, so are their datasets. In some industries like financial services, these inputs are updated every millisecond. However, most AI applications are currently only able to draw from data that is updated just once a day or even once a week.

This can result in inaccurate outputs which, in turn, bring the decisions made into question and make operational processes less effective overall. If the goal is real-time AI, then models simply cannot be based on batch data uploads that quickly become outdated.

This issue is widespread across sectors that use AI models, and the problem has gradually become clearer in recent years. In 2019, we saw this issue in the maritime industry despite heavy investments in digital and data transformation. AI projects in the sector struggled to meet their full intended potential because they were unable to make machine learning work with live and messy data streams.

Decreasing the use of batch data processing

One of the single biggest challenges in implementing real-time AI applications is that batch data provides a static snapshot that is based on a specific moment in time. Put simply, this can lead to its input being outdated within minutes, meaning it cannot power a live AI model.

That doesn’t mean that more advanced models can’t give accurate results based on these data snapshots, but slow data processing or superseding data batches can mean that the potential insight is delayed and at least not wholly accurate. After all, these models are only as smart as the data that feeds them. And without continuous data inputs, we cannot expect continuous intelligence.

Humans have the ability to think, solve problems, and constantly chop and change their existing knowledge all in real time. Why? Because we are constantly consuming new information. When you compare this to ‘real-time’ machines that are trained on batch data uploads, their limitations become clear.

They cannot continuously update their knowledge as they are given new information. For as long as this goes unresolved, real-time AI models will be unable to provide businesses with the ability to make accurate real-time decisions.

The challenge of cross-collaboration

It is extremely difficult to build data streaming workflows that underline real-time AI applications. It requires specialist skillsets, and as a result, you often see different teams working on different use cases for streaming and batch data.

The biggest challenge is unifying the efforts of these teams since they work on projects that code in completely different languages. And, adding innovations like generative AI only increases the demand for real-time data while adding to the complexity of the task for data teams.

Organizations must go back to square one to establish exactly how these data pipelines are designed. Take the example of AI applications leveraging IoT data, which are particularly challenging due to the data quality and processing requirements, but which depend on continuous updates to guarantee their accuracy.

Due to the multiple types of hardware, data needs to be aggregated from heterogeneous data sources and contains out-of-order data points that need to be handled appropriately. This presents a challenge to data quality and, if it is to be overcome, requires the AI model to be fed with a mix of real-time and batch processing that few data frameworks can support.

Democratizing AI with innovations

The disparate nature of batch, streaming, and LLM workflows is an issue, but innovations are bringing them together so businesses can experience genuine real-time AI for the first time. By using one platform to unify these data workflows, businesses can democratize all available data to scale, simplifying the process of implementing AI applications in a way that increases business impact.

By putting batch and streaming data together in one workflow, you unlock the ability to teach and update AI models in ways that were previously impossible. In addition, it takes less time as full batch data uploads aren’t required, meaning intelligence is available instantly, and with increased accuracy.

Data quality, as we already highlighted, is also key to the success of the AI model. That’s why it’s essential as part of the process to understand and validate it. One way of doing so is by assigning weight to each data point so that how a data point is taken into account by a model is directly related to its assumed quality.

Data processing can thus play a key role in helping to automatically clean data to ensure that it is pre-processed in a way that removes noise and drives better accuracy.

Realizing the potential of real-time AI

Organizations have become increasingly ambitious in exploring use cases that will allow them to seize the potential of real-time AI. The promise of helping them improve the speed and accuracy of their data allows them to create AI applications that find smarter ways of working, helping them address time-consuming challenges.

Therefore, the potential of unifying batch, streaming, and LLM data workflows will undoubtedly beckon new and greater innovations that will drive a new generation in using real-time AI to optimize business operations.

About the Author:
Zuzanna Stamirowska is the CEO of, a data processing engine enabling companies to power LLMs, MLMs, and enterprise data pipelines. She also authored the forecasting model for maritime trade published by the National Academy of Sciences (U.S.).

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