What is Real-Time Analytics?
Real-time analytics focuses on preparing and measuring data immediately after it enters the database
Real-time analytics focuses on preparing and measuring data immediately after it enters the database. It allows business users to gain insights and are able to draw conclusions as soon as it enters the system.
Real-time analysis enables organisations to document live streams of data, process them at a fast pace, and extract insights or perform operations in real time. This allows businesses to react immediately if issues arise or seize opportunities to make key decisions.
Today, most real-time analytic systems support semi-structured data and other data formats to avoid ETL processes.
This helps businesses achieve low data latency and allows for the optimisation for computer performance. This reduces the resources required to steadily process incoming data and allows efficient query execution on high volumes of data.
In comparison, batch processing is an efficient way of processing large volumes of data. Data is collected, entered, and processed in the system to produce batch results.
How does real-time analytics fit in an organisation's analytics strategy?
Most companies adopt a variety of analytic approaches, depending on the types of data, workloads, and business issues they are trying to resolve. Analytics are classified into the following:
- Descriptive Analytics: Provides analysis based on high volumes of historical data
- Diagnostic Analytics: Provides analysis to target specific business queries
- Predictive Analysis: Analysis on current and historical data to make key business projections
- Prescriptive Analytics: Analysis on current and historical data to make more informed business decisions
- Cognitive Analytics: Analytical systems are stimulated to think like humans, allowing the system to learn and extract data patterns
Predictive analytics are the entry point for “advanced analytics", where decision-making may be fueled by real-time information. Thus, predictive, prescriptive, and cognitive analytics are use cases that benefit from the capability of real-time data analytics.
Examples and Use Cases
In the following section, we will delve into some successful real-time data analytics use cases.
When running a marketing campaign, most organizations rely on A/B testing for real-time optimisation. With the capability to access data instantly, marketing team can adjust campaign parameters to boost success.
For example, if a company decides to run an ad campaign and retrieves real-time data of the click-through-rate and conversion rate, they will be able to better adjust the parameters to target the audience directly.
Financial institutions often have to make key decisions within the millisecond.
With the help of real-time analytics, traders can take advantage of information from various sources like financial databases, new sources, social media, and more. They will gain real-time insight on the market, helping them to make better trading decisions.
Financial operations are key to the organisation. They help to ensure that financial statements are always accurate to help businesses make the best decisions. Real-time analytics helps businesses to spot errors and reduce operation risks.
Financial statements always must be accurate to help inform the best decisions for the business. Analytics in real-time helps to spot errors and can aid in reducing operation risks.
The software has the ability to match records through account reconciliation, store data securely in a centralised system, and transform raw data into valuable insights through real-time analytics.
This makes all the difference in a team's ability to remain accurate, agile, and ahead of the curve.
Credit scores are important for any financial institution. With the help of real-time analytics, institutions can approve or deny loans almost instantly.
Real-time analytics has the potential to improve the patient-doctor relationship.
During hospitalisation, it is key for doctors to have constant updates on the vitals of the patients. This is done through the usage of wearable smart and IoT devices.
About the author: Sudarsan is a customer solution architect at ByteDance; and, he is a CDMP (DAMA) certified data architect with prior experiences with National University of Singapore, Development Bank of Singapore, Standard Chartered Bank and BNP Paribas.