Key challenges in using real-time data

Analyzing real-time data has always presented a challenge to those working with ML models as they look to improve the accuracy of their inferences using the latest data.

Only AI and ML can make sense of large volumes of streaming data because real-time data is delivered too fast for manual analysis or traditional software for data organization. But while working with real-time data is one of the most valuable applications of ML models, it raises several questions for those looking to leverage the tool for data analysis.

Next, we’ll discuss some of the key challenges faced by those trying to use real-time data and potential ways to overcome them

In what use cases do enterprises need to use streaming data instead of Batch data? Overall, data streams can be used for real-time automated decision-making, which may involve leveraging machine learning models in a production environment on complex data sets. Examples of this include algorithmic trading in high-frequency trading, anomaly detection in medical devices, intrusion detection in cybersecurity, or e-commerce conversion/retention models. Therefore, working with batch data falls under "everything else," where real-time decision-making and context are not as important as having large amounts of data to analyze. Therefore, working with batch data falls into the "everything else" category, where real-time decisions and context are not important, but rather large amounts of data are analyzed. Examples of this include demand forecasting, customer segmentation and multi-touch attribution.

Challenges of using real-time data

While using real-time data to train ML models on continuous data streams has the advantages of quickly adapting to changes and being able to save data storage space, there are also challenges. Converting the model to real-time data may incur additional overhead and may not provide ideal results if these challenges are not properly considered.

Definition of real-time

Working with real-time data presents several challenges, starting with the concept of real-time data itself. Different people have different understandings of the word "real-time". In an analytics environment, some may think real-time means getting answers immediately, while others don't mind waiting a few minutes from the moment data is collected until the analytics system responds.

These different definitions of real-time may lead to unclear results. Consider a scenario in which the management team’s expectations and understanding of real-time analytics differ from those implementing it. Unclear definitions lead to uncertainty about potential use cases and business activities (current and future) that can be addressed.

Constant Data Speed ​​and Volume Variation

Generally speaking, real-time data does not flow at a consistent speed or volume, and it is difficult to Predict how it will behave. Unlike processing batch data, it is impractical to constantly restart tasks until a defect is discovered in the pipeline. Since data is constantly flowing, any errors in processing it can have a domino effect on the results.

The limited nature of the real-time data processing stage further hinders standard troubleshooting procedures. So while testing may not catch every unexpected error, newer testing platforms can better regulate and mitigate problems.

Data Quality

Getting useful insights from real-time data also depends on the quality of the data. A lack of data quality can impact the entire analytics workflow, just as poor data collection can impact the performance of the entire pipeline. There’s nothing worse than drawing business conclusions from wrong data.

By sharing responsibility and democratizing access to data, a strong focus on data correctness, comprehensiveness and completeness can be achieved. An effective solution will ensure that everyone in every function recognizes the value of accurate data and encourages them to take responsibility for maintaining data quality. Additionally, to ensure that only trustworthy data sources are used, automated procedures must be used to apply similar quality policies to real-time data, as this reduces unnecessary analysis efforts.

Various Data Sources and Formats

Real-time data processing pipelines can face difficulties due to the diversity of data formats and the increasing number of data sources. For example, in e-commerce, activity monitoring tools, electronic activity trackers, and consumer behavior models all track web activity in the online world. Likewise, in manufacturing, a wide variety of IoT devices are used to collect performance data from various devices. All of these use cases have different data collection methods and often different data formats as well.

Due to these changes in data, API specification changes or sensor firmware updates may cause interruptions in real-time data flow. To avoid erroneous analysis and potential future problems, real-time data must account for situations where events cannot be recorded.

Outdated Technology

Various new sources of information create problems for businesses. The scale of current processes for analyzing incoming data has grown significantly. Gathering and preparing information using an information lake on-premises or in the cloud may require more testing than expected.

The problem stems primarily from the use of legacy systems and technologies, which require an ever-expanding army of skilled information designers and engineers to acquire and synchronize information and create the inspection pipelines needed to communicate information to applications.

Given the unique challenges of processing real-time data, organizations need to consider which tools will help them deploy and manage AI and ML models in the most effective way. An easy-to-use interface that allows anyone on the team to leverage real-time metrics and analytics to track, measure, and help improve ML performance would be ideal.

Basic observability features, such as real-time audit trails of data used in production, can help teams easily identify the root causes of snags. Ultimately, an enterprise's competitiveness may depend on its ability to derive actionable business insights from real-time data with data processing pipelines optimized for large volumes of data while still providing visibility into model performance.

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