Streaming data is data that flows in continuously from highly distributed sources. These data streams are constantly feeding data that data platforms can use or act upon dynamically and in real time, without the need to download or batch it first.
Data streams come from a wide variety of sources and in many different formats, ranging from server log files and network device data to website activity and customer transaction data.
Stream processing technology allows you to process, store, analyze, and most importantly, act on these data streams as soon as they are generated.
Table Of Contents
- Streaming Data vs. Batch Processing
- Benefits of Streaming Data
- Challenges of Streaming Data
- Streaming Data Use Cases and Examples
- Getting Started with Streaming Data
- Watch our new architecture overview video to see how Volt Active Data works with your existing technologies to power real-time business opportunities and instantly derive value from events captured across multiple streams of fast data.
Streaming Data vs. Batch Processing
In comparison to streaming data, legacy batch data processing methods require data to be collected in batch form before it can be processed, stored, or analyzed. Because streaming data flows in continuously it can be handled immediately, enabling dynamic, contextual, real-time decisions that empower enterprises to unlock the full value and potential of their data, and hence, their applications.
Benefits of Streaming Data
The explosive growth of both 5G and the Internet of Things (IoT) is generating more data, faster, than ever before. As a result, today’s business operations are incredibly fast-paced. Many processes now depend on the ability to act on data within an extremely short window of time to produce optimal or even standard results, such as keeping a customer’s attention, completing a transaction, and preventing fraud.
Even a half-second of latency can be too long: mere milliseconds lost can lead to missed revenue, data leaks, and other negative outcomes. In fact, the new standard for processing data and responding to an event is now under 250 milliseconds. Legacy batch data processing systems simply cannot meet these modern demands. Because decisions must now be made instantly, there just isn’t enough time for data to be downloaded, batched, and then processed.
However, the real value of streaming data is achieved when stream processing technology is applied to enable sub 10-millisecond actions in response to events and transactions, as the events are happening. This type of fast data architecture is what powers seamless user experiences, from shopping across multiple devices to predictive recommendations tailored to the individual user in real time, leading to zero or near zero downtime and increased customer loyalty.
Challenges of Streaming Data
While there are many benefits to streaming data, there are also some important considerations to ensure you are prepared to manage streaming data effectively. For example, it is critical to consider scalability, fault tolerance, and data consistency and durability when implementing data stream processing. Partnering with the right solution provider can help you navigate these challenges effectively and design a powerful stream processing engine that scales with your business.
Streaming Data Use Cases and Examples
Streaming data opens up a world of new use cases because it enables more dynamic ways of leveraging data that weren’t possible with the latency of batch processing. Here are just a few examples and use cases:
Prevent fraudulent transactions and lost revenue
Real time streaming data makes it possible to prevent fraud by identifying and blocking fraudulent transactions before they can be completed. For example, by applying complex rules and algorithms to check against fraud identification patterns in real time, Volt Active Data can identify anomalies and instantly block fraudulent transactions.
The Volt Active Data Platform ingests millions of individual events per second and then updates an in-memory contextual model of user activity. In the case of fraudulent calls, this means the phones on both sides of the call can be checked for numerous forms of suspicious behavior, during the call setup process, thus preventing fraudulent calls from ever being connected. And, as every call being made on the network is being absorbed by the model in real time, behavior algorithms are being continuously updated and increasing the success rate of fraud prevention.
Maximize revenue with dynamic, hyper-personalized customer experiences
With so many stimuli competing for our attention, there is an ever shrinking window of opportunity to capture a customer’s focus and compel them to act. Customers also now expect highly personalized experiences. This means enterprises need to be able to power applications that can capitalize on the few moments when customers are motivated to act, for example, by presenting well-timed, personalized offers and incentives that prompt end users to spend more money.
The timing for these offers is critical: the window of opportunity for making the best possible offer for any given subscriber in a hyper-personalized manner is now within 250 milliseconds. However, this requires the identification of the offer to be done within 10 milliseconds from when the moment presents itself. A powerful customer management solution increases ARPU (average revenue per user) while presenting offers of value to customers (i.e. subscribers) when they are most open to an offer.
Getting Started with Streaming Data
It’s important to partner with the right solution provider that can deliver the streaming processing capabilities and sub 10-millisecond speed to power real-time applications that both drive revenue and prevent revenue loss. Volt Active Data enables global organizations to power real-time business opportunities and instantly derive value from anomalous events captured across multiple streams of fast data. Precise decisions, made in less than 10 milliseconds, can directly influence in-the-moment monetization, prevent digital fraud, and power digital transformation initiatives.
