Real-time analytics gives organizations the ability to process and query new data as it’s being created. Rather than waiting for minutes or even hours for critical information, teams can immediately make use of streaming data from sources such as consumers’ mobile devices, smart sensors, and point-of-sale systems. Real-time analytics has opened up a variety of game-changing benefits for many industries including retail, manufacturing, and finance. In this article, we’ll explore how real-time analytics works and show how it’s being used in today’s businesses.
What Is Real-Time Analytics?
Real-time analytics is the application of data analytics principles to data that’s seconds to minutes old. In the past, data analytics was held back by the limitations of batch processing, which creates significant delays in how quickly data is made available for analysis.
The introduction of stream processing made real-time analytics possible. Stream processing is designed to handle data in motion, making it available for analysis within seconds. Real-time analytics comes in two flavors: on demand and continuous. Continuous real-time analytics alerts users when events occur or initiates an automatic response. On-demand real-time analytics is a more passive approach, providing teams the ability to query real-time data on an as-needed basis.
Requirements for Real-Time Analytics
Real-time analytics technology uses cloud resources to handle the intensive processing requirements of querying data immediately after it’s generated. Unlike systems configured to handle batch processing jobs, real-time systems are optimized to make data available for querying as quickly as possible. Organizations need to consider data latency and query latency when implementing a real-time analytics initiative.
Data latency is the measure of time between when data is created and when it’s made available for analysis. All systems experience some lag time, but real-time analytics systems are designed to reduce that time to as close to zero as possible. This requires a system capable of writing incoming data and allowing an analytics application to query the most recently written data simultaneously.
Also crucial is the time required to execute a query and receive a result—query latency. Advances in cloud computing have dramatically reduced query latency in real-time analytics applications.
Benefits of Real-Time Analytics
With near-instant access to actionable data, decision-makers can react quickly to emerging trends, adjust to rapid market changes, and improve their operational efficiency. Let’s explore a few of the benefits:
Enables faster decision-making
The longer data sits, the less valuable it becomes. Real-time data enables decisions based on current conditions, not what existed hours or days ago. With near-instant access to data, business leaders can capture time-sensitive opportunities.
Provides a better user experience
Real-time data provides new opportunities for brands to better understand their customers, assess how well their products are meeting customer needs, and evaluate their customer service efforts. For example, analyzing social media feeds provides a current picture of how a brand and products are perceived. And companies can improve their customer experience by analyzing how consumers are interacting on channels including ecommerce platforms, online chat, and mobile apps.
By automating manual business processes, organizations can improve their performance and lower costs. Real-time data analytics can be used to automatically optimize transportation routes, evaluate a customer’s credit worthiness, and create customized machine maintenance schedules based on real-world operating conditions.
Use Cases for Real-Time Analytics
Access to real-time data has become foundational to many business processes. Additionally, companies are using this technology in new ways, helping them gain a competitive edge. Here are just a few of the many use cases for real-time analytics.
Real-time analytics in manufacturing
Predictive maintenance programs depend on real-time data from an interconnected network of sensors distributed across an industrial environment, averting costly system failures and extending the life of critical manufacturing equipment. For example, when a machine-mounted sensor detects a temperature reading in a production robot that exceeds the maximum allowable threshold, operators can react quickly.
Real-time analytics in logistics
Supply chains rely on the smooth transport of raw materials and finished goods. Supply chain analytics uses real-time data to scan for potential disruptors such as port congestion or adverse weather that could snarl transportation routes, sending automated alerts that contingency planning is required to avoid a delay.
Real-time analytics in finance
Financial services involve split-second decision-making. Should credit be extended to someone who has just applied for a credit card? Does a pending transaction match the holder’s regular purchasing behaviors or does it have markers of fraud? Real-time data has become indispensable to the finance industry for making sound, data-driven decisions quickly.
Real-time analytics in retail
As consumers move through an ecommerce website, their actions and purchasing behaviors create a trail of data. This data can be used to optimize customized promotions, suggest related products, and support other valuable marketing activities. Brick-and-mortar retailers have similar opportunities using location-based marketing. Because these opportunities are fleeting, they require real-time insights to be effective.
Powering Real-Time Analytics with Snowflake
Accelerate your real-time analytics initiative with the power of the Snowflake Data Cloud. Snowflake provides real-time data ingestion, data integration, and querying at scale. Snowflake’s platform supports fast, efficient, at-scale queries across multiple clouds with Streams and Tasks features that enable you to build data pipelines and turn Snowflake into a nimble data transformation engine. Realize your data’s full potential with the Data Cloud.