Compared to a decade ago, the increase in devices and quality of connectivity have transformed how we consume media. Streaming services made it possible for us to consume content continuously without the need to upload or download an entire file. Additionally, the sudden boom in OTT applications during and post-pandemic has expanded the use of media streaming platforms worldwide.
This explosion in the volume of streaming content data fueled the need to understand customer consumption faster, resulting in the need for real-time analytics or streaming analytics. For example, providing recommendations in near real-time is now required, and the ability to analyze advertising data gives providers an advantage.
The Evolution of Streaming Analytics
In time-sensitive scenarios, real-time analytics uses newly generated data to make predictions, ask questions, and automate decision-making in the application. While previous analytical systems could run periodically (say every 24 hours), this was insufficient for time-sensitive data. In the case of streaming information, periodic analytics would be outdated by the time it is processed. Also, as data streams have no beginning or end, they cannot be broken into batches. This continuous flow of data also requires a different processing and data architecture.
Streaming Analytics Processes Data Differently
Streaming analytics is the processing and analysis of data flowing continuously, and it relies on real-time data. Real-time data can be streamed from transactional databases using change data capture (CDC) or from applications using an event streaming platform such as Amazon Kinesis and Kafka to data sinks.
Stream processing engines are runtime libraries that help developers write code to process streaming data without dealing with lower-level streaming mechanics. It uses event stream processing, which analyses large-scale real-time information and in-motion data.
Some of the most widely used stream processing engines are Apache Spark, Apache Flink, Apache Kafka, Apache Storm, Apache Samza, AWS Kinesis Streams, and Apache Flume.
Real-Time Analytics Made Real
Streaming analytics aims to offer up-to-date information and keep the state of data updated with very low latency. It provides real-time insights to enable more responsive decision-making.
With media and entertainment companies generating vast volumes of data with every click, analytical speed is crucial. Real-time analytics or streaming analytics can help the media industry gain an advantage over competitors in the following ways:
- 360-Degree Customer View
Streaming analytics enables businesses to measure data usage across multiple media platforms accurately. As a result, media providers can now aggregate data sets to develop a clear 360-degree customer view.
These analytical data points can even include user viewing and engagement for companies to know how long, when, and where their viewers consume their content.
Apache Flink is an open-source platform that can ingest massive amounts of continuous streaming data from multiple sources, which is processed in a distributed manner on multiple machines. Apache Flink is used by King (the creator of Candy Crush Saga) to analyze their 300 million monthly users who generate more than 30 billion events every day from different games and systems. Flink offers processing models for both streaming and batch data, enabling data scientists to access these massive data streams while retaining maximum flexibility.
- Anticipating Viewer Churn
According to Interpret’s Video Churn Today in 2021 report, SVOD subscribers increased by 14% in the second half of 2020. During the same time period, the cancelation rate increased from 15% to 20%, and nearly 20% of subscribers switched services to gain access to exclusive content. In such a volatile and highly competitive market, streaming analytics provides operators with more accurate churn prediction models. Streaming analytics brings together both real-time and historical users (including user behavior and engagement) to identify subscriber clusters with a high churn risk.
- Impacting Customer Experience
Media companies must be able to introduce user activation, reactivation, and engagement campaigns that get their users to continue consuming content on their platforms.
Streaming analytics uses click records from various source platforms and enriches the data with demographic information to serve more relevant content to the targeted audience. Europe’s leading media and communications company, Sky, provides TV, streaming, mobile TV, broadband, talk, and line rental services to millions of customers in seven countries, and relies on the Google Cloud Streaming analytics services to deliver customer service at scale. Sky collects diagnostic data from its millions of TV boxes. By combining this set-top box diagnostic and viewing data with streamed and batched information from reference feeds, Google Cloud Streaming analytics created a data warehouse on BigQuery, to help ensure the best possible user experience.
- Real-time Recommendations
Today’s media consumers demand personalized, relevant, and contextual content. But with an increase in streaming services, competition for viewership is intense. Recommendation engines driven by streaming analytics can offer more customization and personalization to keep viewers coming back for more. Based on the real-time analysis of this big data, media companies can make better decisions on content dissemination.
- Content Usage Insights
Deep big data streaming analytics is also giving media companies deeper content insights. It helps uncover which genres are in high demand, what content is preferred at which time of the day, when they pause, or what they skip.
By analyzing this live data in real-time, businesses can detect and act on strategic content opportunities. Apache Spark is an example of a streaming analytics tool that makes use of a big data processing engine to provide scalable, high-throughput, and fault-tolerant live data stream processing.
Online news provider Yahoo uses Apache Spark for personalizing its news. It uses Apache Spark’s streaming analytics processing to find out what kind of news users are interested in and the kind of users who would be interested in reading each news category.
- Troubleshooting apps, devices, and more
According to video analytics solution provider NPAW, 4.9% of video-on-demand views experience some error; for live views, the number is 7.6%.
While media houses offer the same service across different devices, the understanding is that the approach cannot be the same. Netflix uses the Amazon Kinesis streaming analytics solution to monitor the communications between its applications so it can detect and fix issues quickly, ensuring high service uptime and availability to its customers.
The Real-time Future – Speed and Scale
The growth of global streaming analytics is driven by the need for accurate forecasts and the rising usage of technologies such as big data, IoT, AI, and automation.
IDC has predicted that almost 30% of all data generated by 2025 will be in real-time. This indicates a significant shift towards analytics that can handle continuous streaming analysis. In turn, this will drive demand for cloud-based streaming analytics software.
The first significant step toward enabling a real-time analytical solution will be to integrate data streams into a company’s data platform. Once this is accomplished, businesses will be able to receive data and perform calculations for analysis on a large scale and at high speed.
