概要
The heavily digitized and data-driven world we live in has created the expectation of immediacy in so many areas.
- Everyday consumers type words, phrases, or questions into search bars of websites expecting—and often receiving—split-second answers.
- Enterprise business users expect similar brevity when looking up a client in Salesforce or a similar customer relationship management (CRM) platform.
- Lenders need a quick assessment of someone's financial health and get it from Experian, Equifax, or TransUnion in seconds.
Real-time data analytics is what makes all of that possible, and as such, it's arguably one of the truly crucial data trends of the past decade. It circumvents the limitations and delays that batch processing can cause to offer valuable insights that can help define—and redefine—enterprises' strategic direction in the interest of the bottom line.
What is real-time data analytics and how does it work?
Real-time data analytics refers to the processes and technologies that deliver an actionable insight based on a newly ingested data point in real time: immediately or almost immediately.
You can think of real-time analytics as receiving the immediate answer to a complex question. Just like search terms typed into Google, data queries often aren't literal questions, but a question is almost always what motivates the submission of a query: "When did we last upsell that client?" "Does this mortgage applicant have a history of unpaid debt?" "Has this facility been hitting its unit output production goal for the last two quarters?"
Real-time analytics follows a set of steps that are nearly identical to those of batch processing, the method of processing high volumes of data in "batches" based on the availability of computing resources. In both, data is ingested, prepared (cleansed and organized), processed, shared, and eventually stored. But the real-time data analytics process requires only seconds or minutes, rather than the hours or more that may be necessary for compute power to become available for batch processing.
There are several categories that fall under the umbrella of real-time analytics:
- Near real-time analytics: Data analysis takes place in a few minutes rather than seconds, but it's still a low level of latency when compared to batch analytics. The time between data ingestion and delivery of analysis can range from about 2 to 15 minutes, while batch processing operations can take hours or even days.
- On-demand real-time analytics: In this context, users submit queries that prompt analytics tools to deliver results in a few seconds, or in some cases milliseconds. This accounts for many use cases of real-time data analytics.
- Continuous real-time analytics: For this type of analytics, data is constantly being ingested from a variety of specific sources, which is why it's also often referred to as streaming analytics. Based on analyses that occur in real or near-real time, users of continuous analytics systems receive ongoing notifications, alerts, and information from the real-time analytics.
When to use real-time analytics
It's worth noting that there's no "one-size-fits-all" approach enterprises can take to analytics. There are times when batch processing, comparatively slow though it may be, is absolutely the way to go, as in highly complex analytics operations that require large-scale examination of historical data. But by any standard, real-time analytics is becoming increasingly important to enterprises across all industries.
Manufacturing
Streaming analytics, for example, allows the data collected from sensors belonging to internet of things (IoT) systems in manufacturing facilities to be analyzed in real or near-real time. Such a setup gives management constant visibility into machine performance, which in turn facilitates robust inventory management and proactive maintenance scheduling.
Driver assistance systems
Continuous real-time analytics, in conjunction with machine learning (ML), is at the root of advanced driver assistance systems (ADAS). These analytics tools improve vehicle navigation and automate various simple automotive functions.
Financial systems and services
Sales and finance also feature many different uses for all categories of real-time data processing and analytics. The vast majority of debit and credit card transactions utilize on-demand real-time analytics, with card readers querying bank or creditor databases and learning in seconds whether cardholders have sufficient funds for their purposes.
The same is true of mobile payment services like Venmo or Apple Pay. You can safely say that modern e-commerce as a whole might be impossible without real-time analytics. Fraud-detection systems also run on continuous real-time analytics—aggregating transactions in the data stream, searching for anomalies, and generating alerts about potential bogus transactions.
Operational intelligence
Near real-time analytics are at the root of operational intelligence (OI), a discipline centered around complex event processing. Notable OI applications range from cybersecurity—specifically, in the context of advanced threat detection tools—to generating sales leads and analyzing their viability.
These are just a handful of examples that showcase the power and value of real-time data analytics. In the years to come, this process—and the tools that drive its critical operations—is all but certain to crop up across countless verticals and drive new business value.
Overcoming potential real-time data analytics challenges
The core function of real-time analytics processes is something of a tightrope act: You have to balance the need for large-volume data processing with the most low-latency response times possible. High availability is also critical. In certain business cases, such as quantitative modeling of a hedge fund, the amount of data sent for examination is massive, but users expect their analytics systems to be ready at all times to handle their queries on demand.
Batch processing cannot handle this ultra-fast speed and isn't expected to, but sometimes it can even be a tough ask for real-time analytics tools. For an analytics platform to best ensure low data and query latency, it should be engineered to handle high write rates in real time. Data professionals must also endeavor to optimize indexes and cleanse data as much as possible, removing duplicates, whitespaces, and any other superfluous information that would slow down processing and insight delivery.
A robust data analytics platform with the capacity for high write rates, optimized indexes, and the flexibility to operate based on whatever algorithm is right for a given use case can clear whatever obstacles may be involved in real-time analytics operations.
How to get the most mileage from real-time analytics
Enterprises operate on a regular basis with what must often seem like nearly immeasurable stores of data. But without the processes of analytics and the associated proper technologies and best practices, that data flow is little more than noise.
Moving forward, the cloud will be of utmost importance to enterprises looking to enable and optimize real-time data analytics. It would be fair to argue that this is one of the key data analytics trends now, but it will become an indisputable truth in the next several years: Gartner projected that 75% of all databases worldwide will be migrated to the cloud or deployed there natively by 2022. For enterprise-scale organizations, multi-cloud platforms can provide near-limitless resources for analytics and data management—and those looking to retain some on-premises data infrastructure can choose a hybrid cloud deployment.
The other key piece of the puzzle is leveraging a platform like Teradata Vantage that can dynamically optimize workload performance for real-time analytics. The solution's integration of data from all sources, single-source-of-truth visibility, multidimensional scalability, and compatibility with streaming engines allow for the level of control necessary to support low-latency, high-performance real-time data analytics.
To learn more about how Vantage helps you leverage real-time analytics, read our blog detailing how the platform assisted a major bank in Turkey: The bank was ultimately able to reduce the end-to-end runtime of its data pipeline for credit loan processing operations from 30 minutes to 5.25 seconds.
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