In today’s dynamic global marketplace, events outside of our control are constantly making the data we’ve collected erroneous and outdated. There are instances in which real-time decision-making isn’t particularly critical (such as demand forecasting, customer segmentation, and multi-touch attribution). In those cases, relying on batch data might be preferable. However, when you need real-time automated decision-making – utilizing a machine learning (ML) model with complex datasets – streaming data is the superior choice. For example, real-time data is better suited for use cases such as:

• Algorithmic trading in high-frequency trading
• E-commerce conversion/retention models
• Fraudulent financial activity/usage
• Intrusion detection in cybersecurity 
• Anomaly detection for medical devices 

Real-time data is useful for training ML models as part of a data transformation strategy, providing benefits in terms of lower data storage requirements and the ability to adapt rapidly to market changes. While it can be a powerful tool for businesses that use ML models to generate business value, real-time data can also present some unique challenges. 

5 Common Obstacles When Using Real-Time Data

Lacking a Strict “Real-Time Data” Definition

One of the biggest challenges starts with the definition of “real-time data” itself. For some, “real-time” means getting results immediately. Others are fine with waiting several minutes for data to be collected and then processed. The lack of a static definition can cause issues across the organization.

For example, if the C-level executives of a company, following their data governance framework, want to adopt real-time data analysis but its management team thinks differently, it can lead to a disconnect over the expectations for a project. In such cases, the project is more than likely to fail. As a result, it is essential to make sure your team has a consistent understanding of what real-time means.

Unpredictable Data Volumes and Speed

The volume or speed of real-time data doesn’t follow a steady pace and can be very difficult to predict. And unlike working with batch data, it’s impractical to continuously restart a task to find a flaw in a pipeline. The disorganized phases of real-time data processing further hampers standard troubleshooting processes.

For instance, if your operations require real-time data from various sources, then these times should be consistent down to the same milliseconds and use the same format as well. Ensuring you receive or convert all your data into the same format might not allow you to catch every error but adding this to your data management plan is crucial to eliminating this issue in your troubleshooting processes.

Inferior Data Quality

Gartner correlates poor data quality to revenue losses in businesses. Low-quality data can negatively affect your entire pipeline’s performance in ways similar to bad data collection. There is nothing more unpleasant for your business than forming conclusions drawn from false data.

It’s also possible for one data stream to be behind the others. For example, in the case of a periodic neural network that uses a series of credit card transactions for predicting the possibility of fraud, if consecutive data streams don’t have the same level of quality then it will result in unexpected errors. 

A high level of attention should be given to the data architecture principles, or the integrity, comprehensiveness, and correctness of the data, followed by data governance best practices. One approach is to apply a quality policy that uses automated procedures to save time and guarantee the use of dependable data sources.

Separate Data Formats from Separate Sources

In any analytics pipeline, the first step is data collection. And with the use of different data formats and a rapidly growing number of data sources, real-time data processing pipelines are going to face big challenges.

Let’s take a manufacturing plant that uses a variety of IoT devices for collecting performance data from production equipment, where each has its data format and specifications. Here, if there are any changes in the data schema, the sensor firmware will update, or the API (Application Programming Interface) specification will change, leading to the interruption of the data collection pipeline. Because of this, cases where the readings are missing should be accounted for to prevent potential malfunctions and any inaccurate data from the analytics tools in the pipeline.

Inefficient and Outdated Techniques

The increasing popularity of real-time data analytics is leading to the rise of newer information sources, posing a problem for businesses. Moreover, using real-time data requires a completely different approach from traditional batch data processing. Instead of insights being provided to the organization once a week, real-time data analytics is expected to deliver them every second. It also requires engineers and designers to continuously update their skills as the data continues to evolve.

As the changes data in sources continues to increase, so do the chances of consuming incorrect data into your model. This makes staying up to date with any changes to the data you consume a valuable practice. For example, if you train a predictive model (whose data source has changed), but you’re still using techniques that are no longer compatible, when you deploy your model the predictions will no longer be consistent with the actual data.

Working with real-time data means constantly facing unique challenges, so companies need to focus on tools that will speed up the process of managing and deploying ML models effectively. Ideally, you would want a user-friendly and simple interface allowing your team to enforce real-time analytics and data quality metrics for measuring, tracking, and ultimately improving your ML model’s performance. To identify the core reason for obstacles, real-time data audit trails during production can be helpful for your team. Getting meaningful insights from real-time data – the kind that can make a business more competitive – depends on optimizing the data processing pipeline for large data volumes while also allowing model performance visibility.