Currently, there are more connected devices on Earth than humans – and those connected devices are creating massive amounts of data. To handle this growing data velocity, businesses are turning to streaming analytics to stay adaptable, and keep their competitive edge, with real-time data analysis.
Why is streaming analytics important right now?
Between mobile devices, IoT, social media, and other versatile data sources, companies are dealing with tons of data that needs to be processed and analyzed quickly to make informed decisions.
That’s where streaming analytics comes in, enabling you to conduct real-time customer data analysis and make strategic decisions at the right moment (whereas with batch processing, data ingestion and analysis occurs on a fixed cadence, like overnight or on a monthly schedule).
How streaming analytics came to exist
For something that seems so futuristic, streaming analytics as a concept has been around for a surprisingly long time.
In 2002, Stanford researchers published a paper called “Models and issues in data stream systems,” in which they made a case for why new data event processing methods were necessary. The researchers indicated that data doesn’t “take the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying data streams.”
From there, companies and data engineers began developing applications to process data as it came in and achieve a low latency – something traditional batch processing doesn’t offer. Now the streaming analytics industry is projected to grow to more than $50 billion by 2026.
Streaming analytics use cases
Because streaming analytics allows you to analyze data on a massive scale, there are use cases in every industry. Industry titans like Netflix, Uber, Amazon, Twitter, and Spotify use data streaming to optimize their operations. Here are a few examples.
Credit card companies use streaming analytics to detect fraud. For example, if a credit card transaction meets the company’s criteria for suspicious activity, it triggers an automated alert via text or email to the cardholder to verify the transaction. Detecting credit card fraud is imperative – the Federal Trade Commission (FTC) fielded nearly 390,000 reports of credit card fraud in 2021.
Advertising and marketing
Streaming analytics gives marketing teams valuable insights into customer behavior that can improve the efficacy of their efforts and increase ROI. Data such as website page views, click logs, and customer demographics helps marketers target the right audience with personalized messaging. According to our annual State of Personalization Report, 62% of consumers say they’ll stop being loyal to a brand if they have an “un-personalized experience.”
Streaming analytics tools can detect anomalies in your data stream, enabling you to identify security issues in real time and isolate threats before they escalate. For example, an IT team can identify suspicious activity, like a massive amount of traffic coming from a user with a single profile.
3 benefits of streaming analytics
The real-time nature of streaming analytics provides numerous benefits, including the ability to take proactive measures based on insights gained.
Real-time analytics provides near instantaneous insights into customer behavior or events, allowing for data-driven decision-making. Instead of learning about something after the fact (when it might be too late) you can take action in the moment. For example, with real-time insights businesses are able to engage with users based on specific actions (like the products they’ve viewed, articles they’ve read, or emails they’ve opened, among other possibilities).
Another benefit of real-time insights comes in the form of predictive maintenance, which aims to proactively predict system or equipment failure before it occurs (using a blend of real-time data, historical data, and machine learning models).
Data granularity refers to the specificity of data. Or in more general terms, how much detail are we gaining from this data? Let’s use the example of a business keeping track of every transaction that occurs (a standard practice). Theoretically, this company could work with two datasets: a view of how many transactions occur each month, and then how many transactions occur each week. Looking at a week-to-week view of transactions would be a more granular view than purchase activity happening month-to-month.
Because streaming analytics processes data as it comes in, businesses can gain more granular insights by understanding what is happening moment to moment. This granularity can then be used for rapid and highly nuanced audience creation (like basing audiences on customer behavior that happened in the past hour, day, or week).
Better machine-learning models
Machine learning models are trained on data sets. Without data there is no machine learning, and the quantity and quality of the data you use will directly impact the accuracy of your machine learning model.
We interact with machine learning models everyday. Google Search is an example of machine learning at work. Think of a time that you misspelled a search time, and Google surfaced results for what you meant to say. That is an example of machine learning recognizing common misspellings or guessing that the term was a typo based on keyboard layouts.
With streaming analytics, machine learning models gain a faster feedback loop. As an example, ML is increasingly being used in the finance sector to analyze fluctuating stock prices and help hone market predictions. This real-time data can also make customer experiences even more personalized. Look at Netflix or similar streaming platforms: as soon as you watch a show, these platforms update their algorithm to better recommend content that matches your preferences.
Tips for effective streaming analysis
Here are some best practices for designing, developing, and managing data streaming pipelines.
Select data capture sources
Choose the most relevant sources from which customer data can be continuously processed, such as IoT devices, websites, social media, or your CRM. Say you’re an e-commerce store; the most important data to monitor might be your website activity so you can adapt your site to make it more user-friendly or highlight popular items.
Aggregate and transform data
With data streaming from multiple sources, it’s important to have an infrastructure and process in place for aggregation and transformation. As a quick recap, aggregation is the process of combining data from multiple sources into a central repository or data set to spot larger patterns and trends (e.g., aggregation makes it possible to spot anomalies in data, calculate important metrics like totals and averages, and so forth).
Transformation is when data is cleaned (e.g., incomplete entries removed, duplicate entries detected, etc.), and potentially reformatted to fit with its target destination.
React swiftly & appropriately to real-time insights as they arrive
Throughout this article, we’ve continually come back to one of the main benefits of streaming analytics: the access to real-time insights. Especially in today’s market, there’s a pressure to respond quickly, to adapt on the fly to changing circumstances, and to do this all with a high level of precision. The ways that real-time analytics are used today show their power and potential, like:
Ride hailing apps
Marketing campaign orchestration
And much more!
Build your streaming analytics infrastructure with Segment
Twilio Segment’s customer data platform helps businesses collect, clean, and consolidate data in real time – processing hundreds of thousands of events per second, with high-performance Go servers ensuring a 30 ms response time and “six nines” of availability.
With Connections, businesses are also able to implement integrations in a matter of minutes, allowing businesses to collect data from every single touchpoint. (And with Protocols, businesses can rest assured that data is automatically being QA’ed to avoid duplicate entries or errors.)
Segment also assures that businesses are working with a complete, trusted, and real-time view of their customers by providing identity resolution at scale. With Profile Sync, businesses can automatically sync customer profile data to their data warehouse, ensuring it stays holistic and up to date. Then, with Reverse ETL, teams can extract these profiles from their data warehouse and send it to any destination.