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Not getting your data when you need it? The truth about real-time vs. live-time

Author: Laura Ballam


Data has quickly become the most valuable resource in this fast-paced digital age. It’s all around us, constantly being generated and collected. From social media interactions to purchase activity, there’s an abundance of information available for marketing and data analytics.

Companies leverage data to improve products, and marketers and data scientists use data to make better informed decisions. But with so much data available, it can be hard to understand all the options. Two terms that are often used interchangeably in the data world are real-time and live-time data. But there are differences between them, and it’s essential to understand these differences to use them effectively. 

Real-time data is not the same as live-time data. While both terms imply information is being collected and delivered in real time, there are important differences between the two. When it comes to data vendors, many claim to offer real-time data – but they’re leaving out substantial details.

Real-time data is information that's captured instantly (real-time), or as it happens. It can also refer to data that’s available in real-time but may not be immediately actionable or relevant. This data can be used for a variety of purposes, including monitoring website traffic and app usage, tracking inventory levels and sales numbers, and much more. Marketers use this data to measure performance of long-term campaigns, while for data scientists real-time data can offer insight into patterns of customer behavior and inform machine learning.

What does this mean? Let's say you want to know how many people are visiting your website or app, or which products are being purchased; with real-time data collection on your digital properties (or any other point-of-sale device), you can see this information after each sale is made. However, you may not be able to do anything with it until after the fact - we’ll get to that later.

Likewise, real-time processing allows you to receive new information as soon as it becomes available, but it doesn't necessarily mean that all previous events have been processed already by your system before receiving this new batch of data from another source, like another database or API call, etc. The key here is “as soon as it becomes available.” Which means if your data solution takes 4 hours to process the data and send it to you, you’re not actually getting it in real-time.

While real-time data refers to information that's been gathered in a timely manner, live-time data is used in reference to data that users (or systems) can see and action as it’s happening. Think of video feeds or other types of media. In this sense, live-time refers to an event or object being observed at its current state - not just looking back at a moment in time.

Live-time data refers to information that’s constantly being captured and processed in milliseconds, so it’s available as it happens. This means the data is always current and can be accessed and analyzed immediately, providing instantaneous insight. Live-time data also refers to information delivered into your downstream decisioning and marketing systems in true real-time. Live-time data not only provides instant feedback on marketing and loyalty campaigns, offers, and ROI, the insight also enables swift adjustments based on new information. For data scientists, live-time data helps identify patterns and trends that lead to faster insight. Live-time data allows companies to keep track of what's happening at any given moment without having to wait until later (sometimes days, weeks, or months) to act on it. This means businesses can make better operational decisions, and marketers can make data-driven campaign decisions, so they don't miss out on opportunities or waste resources.

To clarify, the primary difference between real-time and live-time data is how quickly the data is processed and analyzed. Live-time data is analyzed and delivered instantly, as it’s generated, but real-time data can be delayed for processing and analysis. Real-time data is great for identifying patterns and recognizing trends over time to inform strategic planning, producing long-term analysis such as market trends, website traffic, and historical data in data science. Live-time data provides all that and more - enabling brands to identify opportunities as they happen and take immediate action. With live-time data, decisions (and adjustments) can be made quickly and accurately based on the most up-to-date information available.

When it comes to marketing or customer behavior analysis, long-term trends are important to analyze, but you must have live-time data to fuel hyper-personalization or customize the experience while the individual is actively on your site or app, or in your store.

So, which is better - real-time or live-time data? It depends on your needs and the situation at hand. If you’re only looking to identify trends and patterns over a longer period, real-time data may suit your needs just fine. But if you want to make decisions quickly and deliver dynamic, customized experiences in-the-moment, live-time data is the way to go.

Live-time data is superior for decisioning and actioning purposes because it helps companies monitor activity and act immediately if something goes wrong (like a website going down) or when new information is captured that alters the context of the data they already have. This enables true real-time personalization of digital and in-person experiences for consumers, such as customizing an in-page offer or notifying a customer service rep so they can adjust their approach while they’re talking to the customer. Live-time data is also used for real-time bidding in online advertising, predictive modelling, and even financial fraud prevention.

Both real-time and live-time data are essential tools for marketers and data scientists. Understanding the differences and applications of these two types of data is essential for making informed decisions and developing effective data-driven strategies. Based on your needs and use cases, you can decide which type of data best suits your objectives and make more informed decisions when reviewing vendors who claim to offer “real-time data.”

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