Profitable businesses rely on their own data to grow, make smart choices, and delight their customers. Easier said than done! While a batched approach can provided limited success, real-time data analytics often give better ROI.

Introduction

All happy families are alike; each unhappy family is unhappy in its own way. - Leo Tolstoy (Anna Karenina)

For the last 5-10 years, most organizations have believed that amassing petabytes of data from various sources and storing them in databases was a characteristic of a good data-driven organization. Only later did they realize that this leads to data silos. Unless datasets are served to their stakeholders in a proper format, without timely decision making, investments in collecting data are largely wasted. The worst part of data silos is that each organization’s data silos are complicated and bureaucratic in their own way.

By contrast, companies that have used data to their advantage have one thing in common: they understand the timeliness of the data to generate real-time insights and solutions. They use this understanding to put systems in place and data strategies for accessing, storing, and serving datasets for various downstream analysis like analytics or an AI/ML application.

All data is inherently streaming in nature. The only real difference is how we decide to conceptualize that data — as what it really is (a stream of changes) or as what it looks like right now (a snapshot). While most analytics frameworks and machine learning libraries assume the latter, the former can enable us to make faster and more relevant decisions (Housley & Reis, 2022).

Characteristics of Streaming Data

The term “streaming data” describes information that is produced and transmitted in a constant and rapid stream in real time. For example, sensors, social media, website interactions, monetary transactions, and many other applications and systems all contribute to the mountain of data that exists today. These data streams arrive continuously and without limits, and may have high throughput.

Kolajo et al (2019) propose these basic characteristics of streaming data:

• Real-time or Near-Real-Time (NRT) in nature: This means that the data is accessible as and when it is generated in real time. When discussing real-time data that ensures immediate processing and action, it is important to note that near-real time is still very fast, processing data streams in seconds or minutes, if not milliseconds. For instance, an analytics dashboard build with Grafana and Prometheus may require a few seconds to reload.
• High Velocity: Information in a stream is continuously produced and delivered, and is well-suited for applications that require constant monitoring and analysis. The data are frequently updated and reflect the most recent state of the monitored phenomenon. The majority of the time, real-time data also includes a temporal format, i.e., the information is updated within a constant time interval.
• High Volume: The size and quantity of the data that is streaming can be highly variable. Data processing can be both dynamic and scalable because it is not constrained by fixed batch sizes or data sets.
• Event-Driven: Real-time data are triggered by specific, time-sensitive events, and applications that require them demand immediate attention. In many instances, events are what initiate data streams. User actions, sensor readings, log entries, and transactions are examples of events that can be monitored and recorded. Events trigger the creation and transmission of stream data.

Streaming Vs Batch Processing

Traditionally ML systems have utilized data in batches to train a ML model for future prediction. However, as technology has advanced, there has been a growing need for services that can operate in real time, thanks to the availability of real-time data from various applications and devices.

Incorporating streaming data into ML models, for example, enables a more comprehensive analysis that combines real-time insights with historical context. Batch processing is still suitable for non-time-sensitive applications — there are currently many more types of machine learning algorithms geared towards batch training compared to online modeling. Nevetheless, there is an increasing demand for processing high-speed data to provide actionable insights on the go. This demand has given rise to platforms, tools and frameworks that simplify the process of working with real-time data.

In addition to this, streaming data can be instrumental in MLOps systems to continuously update machine learning models in real-time as new data arrives. Since streaming data is continuous in nature it can enable Data Scientists to monitor the statistical properties of incoming data and ensure that the properties of incoming data matches with the data that is used to train the ML model; this will avoid phenomena like data drift and concept drift in Machine Learning pipelines (Shahraki et al., 2022).

Examples of Real-Time Data Applications and Benefits

A few examples where processing real-time data wins over batch processing data are:

• Finance where demand for time sensitive applications are non-negotiable
• Stock market trading where decision needs to be made in split second
• Customer Experience (CX) where meeting the ever evolving customer expectations and leveraging short customer interest span is key to gaining advantages over competitors.

These examples highlight how real-time processing has become crucial in various industries. Moreover, advancements in data processing technologies and infrastructure, coupled with innovative approaches to analytics and artificial intelligence, have made it increasingly convenient to employ real-time processing for analytics across all sectors.

Looking to Break New Ground?

Here are some of the most important real-world use cases that you can expect to see that thrive on real-time data:

• IoT: The data produced by IoT devices in real time is massive. Data from sensors, smart watches, and machines can be gathered and analyzed in real time by applications in this space. In this way, businesses can track metrics, look for outliers, automate tasks, and fine-tune procedures to boost output while cutting costs (Levy & Tian, 2022).
• Financial Fraud Detection: This is by far one of the most important use cases of utilizing real-time data to make decisions to avoid financial fraud from happening. Financial institutions such as banks and credit card companies use stream data applications to track customer activity in real time, spot anomalies, and stop fraud before it happens (Gauder, n.d.).
• Weather Monitoring & Forecasting: Stream data applications are used by meteorological agencies to process data from weather sensors, satellites, and radars in near real time which makes it easier to make accurate weather predictions and send out timely warnings when bad weather is coming (Levy & Tian, 2022).
• Supply Chain Management: Companies in the supply chain industries are increasingly relying on near-real-time insights to track the movement of goods and materials and optimize inventory levels and delivery schedules (Mitchell, 2022).
• Fleet Management: Logistics companies track their vehicles in near real-time and monitor factors such as location, fuel consumption, and driver behavior to optimize routes, improve fuel efficiency, and ensure timely deliveries (Doukas & Christos, 2011).
• Social Media Analytics: Social media platforms use real-time data applications to analyze user interactions, trends, and sentiments. This facilitates personalized content recommendations, targeted advertising, and real-time responses to customer inquiries or complaints. Furthermore, these apps allow businesses to understand customer preferences and adjust marketing strategies in real time (Kumari & Babu, 2017).

Real-Time Data as a Competitive Advantage

So how to evaluate when a real-time data product is beyond experimentation and heading towards competitive advantage? Here are the key themes to target in your real-time product KPIs:

• “Project X reduces decision-making time from __ to ___.” With real-time data, businesses can make quick, well-informed decisions, and agility enables swift responses to market trends, customer demands, and new opportunities.
• “Product Z improves Customer Experience by reducing churn ___%.” By understanding customer behavior and preferences in real-time, businesses are able to personalize their interactions with customers, thereby increasing customer satisfaction and loyalty.
• "___% of issues are now resolved proactively by Service ABC." By monitoring and analyzing data in real time, businesses can reduce the damage caused by problems or anomalies. Businesses can safeguard their reputation by proactively addressing issues such as service quality, downtime, and customer complaints.
• “Our engineering team completed the MVP in only ___ sprints.” By understanding real-time demand and operational dynamics, real-time data analytics can optimize supply chain, inventory, and workforce resource allocation, thereby reducing waste, streamlining operations, and increasing efficiency.
• “Since adding Product EDA, we’ve pre-empted ___ risk events.” Monitoring real-time data streams for fraud, cybersecurity threats, and operational vulnerabilities will aid in identifying and mitigating risks, allowing businesses to respond more effectively and reduce losses.

Conclusion

Datasets are the foundation of analytics, machine learning, and AI applications, for which we require high-quality, comprehensive, and pertinent information to generate insights, make predictions, and even train robots.

Utilization of real-time data offers many advantages for data stakeholders. Organizations can leverage the advanced analytics from their data and data professionals can improve their experimentation and iteration processes in analytics and machine learning pipelines to provide better services to their customers.

However, it is essential to note that utilizing real-time data comes with its own sets of challenges. To manage the volume and velocity of real-time data streams, businesses must invest in a solid data infrastructure, secure data transmission, and scalable analytics platforms. Data professionals must have the necessary skills to effectively collect, analyze, and interpret real-time data so that they can effectively harness the power of real-time data in this data-driven era if they want to experience sustainable growth and success as technology advances.

If you’re looking to get into real-time data science, check out Ensign, a platform and community for data scientists building event-driven analytics together.

References

• Doukas, E., & Christos, S. (2011, January). Advanced Technologies for Fleet Management Systems. IHU Repository. https://repository.ihu.edu.gr/xmlui/bitstream/handle/11544/172/Efstathiadis_Doukas_Silkoglou_Christos.pdf?sequence=3
• Gauder, M. (n.d.). Fight Fraud With Real-Time, Product-Level Data. Mastercard Data & Services. Retrieved May 21, 2023, from https://www.mastercardservices.com/en/reports-insights/fight-fraud-real-time-product-level-data
• Housley, M., & Reis, J. (2022). Fundamentals of Data Engineering: Plan and Build Robust Data Systems. O’Reilly Media, Incorporated.
• Kolajo, T., Daramola, O., & Adebiyi, A. (2019, June 6). Big data stream analysis: a systematic literature review - Journal of Big Data. Journal of Big Data. Retrieved May 19, 2023, from https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0210-7
• Kumari, S., & Babu, C. N. (2017, December 14). Real time analysis of social media data to understand people’s emotions towards national parties. International Conference on Computing and Networking Technology (ICCNT), 8th. 10.1109/ICCCNT.2017.8204059
• Levy, D. C., & Tian, Y.-C. (Eds.). (2022). Handbook of Real-Time Computing. Springer Nature Singapore.
• Mitchell, K. (2022, June 2). Integrating Real-Time Data into the Supply Chain. Industry Today. Retrieved May 21, 2023, from https://industrytoday.com/integrating-real-time-data-into-the-supply-chain/
• Shahraki, A., Abbasi, M., Taherkordi, A., & Jurcut, A. D. (2022, April 22). A comparative study on online machine learning techniques for network traffic streams analysis. Sciencedirect, 207(108836). https://doi.org/10.1016/j.comnet.2022.108836

Faraz Rahman is a Data & Machine Learning Product Manager and professional Data Scientist. She is currently pursuing a graduate degree at Carnegie Mellon University in Silicon Valley, where she works on software products that benefit society and use ML/AI as their core technology.

Photo by Joshua Sortino on Unsplash.