Common Streaming Data Application Use Cases

Aditya Chidurala

Common Streaming Data Application Use Cases

Consumers want experiences and information faster—quicker fraud notifications, speedily delivered products and services, and in-the-moment personalized offers. Sometimes, they want responses faster than your current tech stack allows.

This demand has made digital transformation a top priority for business executives, with 60% listing it as their most critical growth driver in 2022. And while many businesses use and process real-time data – they’re not seeing the real-time results needed to execute the sophisticated business use cases that modern consumers demand.

On the other hand, streaming data applications with Nstream reduce the complexity of typical streaming data architectures so companies can deliver the business experiences that consumers expect and crave.

We’ll cover how streaming data applications with Nstream enable use cases such as asset tracking and management, anomaly detection, and customer 360.

How streaming data applications make modern business use cases a reality

Traditional data streaming architectures are highly complex, involving multiple data systems that make it challenging (and costly) to run the processing, analytics, and advanced automation needed to execute modern business use cases. As a result, many companies are stuck relying on stale data or predictive models to inform business decisions being made that impact the current state of their business.

Nstream allows organizations to build a complete, stateful model of their enterprise to identify noteworthy changes, understand what they mean, and take action accordingly. How does this happen? Nstream’s technology innovations – including stateful services, streaming APIs, and real-time UIs – reduce the complexity of traditional streaming architectures so companies can:

• Continuously perform stream-to-stream joins at scale. Streaming data applications with Nstream can join real-time streams at millions of events-per-second scale without incurring prohibitive costs.
• Isolate events and streams at the entity level. See your organization’s assets down to the customer, prospect, transaction, truck, server, device, or more.
• Reduce latency and maintain context. Streaming data applications push data (rather than polling data) to reduce latency and automatically update context.
• Establish real-time visibility of your business. See the real-time state of your business for better decision-making on the current state of operations – not decision-making based on your business one day, week, or year ago.
• Remove the need for multiple data systems. Avoid vendor lock-in and hiring niche subject matter experts for various data systems, and instead, use Nstream’s open-source platform.

Various enterprise customers have used the Nstream Platform to build streaming data applications that are running in production. Below are a few examples that showcase the scale at which these streaming data applications can operate:

• 10 GB/second of streaming data from 10+ data sources.
• 200M+ events/second, with millisecond latency.
• Real-time scoring of 100M+ users.
• Real-time interactive map view of 20M+ IoT devices.
• 10x faster time to value — 200M+ events/second in just six weeks.Over 70% lower total cost of ownership (TCO).

Common streaming application use cases

Streaming data applications can help companies across industries execute sophisticated use cases that improve customer satisfaction, drive efficiencies, save on costs, and even create a competitive edge. Here are some examples of where real-time streaming data applications are already enhancing existing concepts:

Asset tracking and inventory management

Employed Americans spend at least two hours each day – or 25% of their workweek – searching for the documents, information, or people they need to do their jobs. This inefficiency isn’t limited to the average worker, either – entire companies often struggle with visibility into the whereabouts of assets as they journey throughout the supply chain. This poor asset tracking and management can lead to operational inefficiencies, asset loss or theft, and poor asset maintenance and management, among other challenges.

Streaming data applications allow businesses to access and see their entire landscape of assets and immediately act on changes as they arise. For example:

• Retail: Isolate events or streams at the entity level to monitor inventory flow down to the individual t-shirt or baseball cap. This up-close, real-time asset tracking can tell companies what products currently are on the floor; which items are selling quickly at any given time and location; and how many hours until purchases are delivered to a customer’s mailbox, among other things. This real-time visibility into product availability can reduce customer points of friction to encourage customer loyalty and repeat purchases.
• Transportation and supply chain: Asset tracking for transportation or supply chain means knowing exactly what items each truck will transport, and tracking each specific piece of product, in addition to the truck as a whole. View every step of the journey, knowing the exact delivery time instead of a window. Identify early indicators of supply chain bottlenecks and automate remediation actions for proactive disruption resolution. A better understanding of your entire supply chain helps you find efficiencies to reduce costs associated with transportation and labor—all of which positively impact revenue.
• Finserv: Real-time asset and transaction data monitoring are critical for financial institutions that rely on consumer trust and confidence. For example, real-time asset tracking and transparency allow institutions and individuals to monitor transaction anomalies and suspicious activity to mitigate or prevent fraud or identity theft. By reducing losses and risk, organizations can better protect assets and maintain the trust of their customers, ultimately preserving revenue streams and safeguarding reputation. Access to real-time product or market trends or investment data can also empower investors to act quickly on market opportunities, thus generating additional income or saving them from more significant losses.

Anomaly detection

Often, the longer an anomaly goes undetected in your organization – whether that be a data breach or a missing shipment – the more damage it can cause. Since streaming data applications push data – rather than poll data – the state of a company’s business entities is updated at the speed of its fastest data source. That means that streaming data applications can help spot and begin to mitigate anomalies as soon as they appear – rather than minutes, days, or even months later.

Here are a few examples of how streaming data applications can support anomaly detection across verticals:

• Retail: This can look like monitoring for price discrepancies. Easily check to make sure all merchandise is priced appropriately.
• Finserv: Customers immediately want to know when a fraudulent charge hits their bank account. Streaming apps enable stream-to-stream joins at scale and isolate events at the entity level, allowing banks to compare large amounts of data and flag the moment a discrepancy is noted. Implement business logic to automatically trigger a fraud protocol that sends a notification and locks accounts before additional fraudulent transactions occur.
• Transportation: Identify when a shipment is running late – whether due to weather, road conditions, equipment failures, paperwork issues, or more – to mitigate and prevent further supply chain delays and bottlenecks as soon as possible.

Customer 360 and real-time personalization

With 80% of customers stating they are more likely to buy from a company that offers personalized experiences, more organizations have begun working toward creating individualized customer journeys.

Streaming data applications are ideal for executing customer 360 use cases as they allow companies to analyze and act on large amounts of data without relying on

prohibitively complex or expensive data processing architectures. With streaming data applications, companies can maintain a full view of their business entities in real-time and implement business logic to respond to customers quickly during events or opportunities that matter most.

• Retail: With streaming data applications, companies can build a holistic view of their customers in real-time through various sources, such as online interactions, geolocation data, sales/marketing, customer support, and more. Automate business logic to initiate key actions – such as texting a coupon code while browsing in-store or online – to build customer satisfaction and loyalty.
• Finserv: Customers appreciate when companies make them feel like individuals instead of cookie-cutter customers. Financial institutions can create and offer personalized financial plans and goals based on indicators corresponding to major life events, such as buying a house, opening a business, or having a child.
• Transportation: For customers, last-mile delivery can be the supply chain’s most relevant – and frustrating – piece. Improve customer satisfaction by providing specific geolocation alerts as a package travels from the fulfillment center to a customer’s doorstep.

There’s no limit to potential use cases

Nstream is the fastest way to build full-stack streaming data applications – which also means it might be one of the fastest ways for you to accelerate your digital transformation efforts to see increased efficiencies, cost savings, and improved customer satisfaction. What’s more, streaming data applications are industry agnostic, allowing you to work from low-code templates or design an app that meets your specific company’s needs and challenges.

Want to see a streaming app in action? Check out how the city of Palo Alto gained greater visibility into real-time traffic trends with streaming data applications.