Almost every business is looking to evolve to be data-driven. Data offers incredible insights into business processes, customer interactions, sales performance, employee productivity, etc. With so much data and limited time, it isn’t easy to understand the metrics that provide actual value. The existing self-service BI tools and analytics models answer a few questions.
But to derive more value from data, it is vital to analyze the data as it gets created across systems and processes. If you analyze data in no time, businesses can take immediate action and be more responsive in this agile world. Hence real-time analytics play an essential role for every business, irrespective of the industry or sector.
As per IDC, nearly 30% of data will generate in real-time by 2025. Businesses need to invest in data architecture and infrastructure to support the stream processing for various business decisions. With real-time analytics, decision-makers can act within seconds and make the necessary decision. For example, a retailer can catch up on consumer trends to increase their sales in no time.
What is Real-time Analytics?
In simple words, real-time analytics is about real-time processing in which you can prepare and analyze the data as it gets stored in the database. It allows the key stakeholders – C-suite, Sales, operations, production, HR, etc. to derive the conclusions immediately. Real-time analytics is also a fantastic opportunity to seize the actual value of data without being lost.
Do you think that the data remains unprocessed for real-time analytics? No, users will not see any raw data, and it gets processed with next-gen data engineering capabilities. Unlike batch processing, real-time stream processing allows businesses to reduce delays and gain insights.
Businesses can make this possible with automated data pipelines, pre-defined cleansing routines, and advanced algorithms. So, when pulling the data from the source, it is already filtered, processed, and adjusted for meaningful data points.
Real-time Analytics – The Process
The video recommendations that we observe on YouTube and the product recommendations on Amazon seem to be the best examples of real-time analytics. Real-time analytics also enables businesses to use advanced machine learning algorithms and AI. A real-time analytics software/tool has the following components:
- Data Aggregator – It pulls all the data from different sources and compiles them for real-time processing
- Broker – Another element that makes the data available for real-time usage.
- Data Analytics Engine is the vital component in the tool that compares the data and transforms them to correlate and analyze.
- Data Processing – All the logical operations are performed according to the inputs and analyzed with this component.
Organizations utilize stream processing to push and pull the data and create it ready for analysis to generate compelling real-time insights. In most systems, businesses pull the data at different intervals. Usually, these intervals vary from milliseconds to hours, depending on the needs and the amount of data. It is better to have these intervals at seconds for accurate real-time analytics.
Growth Drivers for Real-time Analytics
- Relevance – A few studies show that around 70% of the data loses its value and relevance if it remains on the shelf longer. With real-time analytics, businesses can capture meaningful insights to fuel their decisions and business value in no time. There is little chance that the value of data diminishes and organizations lose their relevance to the customers.
- Innovation – Rapid insights not only help businesses to make decisions faster but also helps in increasing sales, improving customer experience, and enhancing operations. Real-time analytics also helps bring more context to the insights as it depends on events, trends, and information as they happen. If you are a bank, you can have visibility into customer transactions to help them with their eligibility and credit scores. Based on that, you can promote any relevant products and services to consumers quickly. Real-time analytics promotes innovation and swift decision-making to be data-driven.
- Latency – In today’s digital world, a slight delay in decision-making may cause significant losses for businesses. It is also critical to adapt to the latest technologies to have a significant competitive advantage. With real-time analytics tools, you can gain the confidence that you are acting on the relevant insights with more agility. Success in the new digital era seems to respond to data faster than the competition.
Benefits of Real-time Analytics
Business decisions at swift pace – Although businesses use BI and analytics tools for insights, they are still considered decision-support tools. The best decisions need to be faster and in pace with business growth. In short, real-time analytics allows businesses to make decisions whenever and wherever they need them.
Agile processes – As organizations capture and manage the data in real-time, information is more condensed and available for reporting at any time. So the need to have different visualizations reduces while also allowing the data processes to be more agile.
Respond to short-term market changes – Industries like retail, travel, and BFSI depend are sensitive to rapid fluctuations in the market and consumer expectations. Today, as the experience changed to online, industries need to respond to demand quickly and customer trends. Real-time analytics enables faster and more effective monitoring of these rapid market changes.
Personalization – As we see businesses like Amazon and Netflix offering personalized recommendations in real-time, it is gaining prominence across industries. Customers don’t even realize that their data and behavior patterns are analyzed to provide a unique experience. Though a few people think that customers are bombarded with personalization, it offers numerable opportunities to businesses to understand and cater to customer needs.
A few Use Cases
The application of real-time analytics is not limited to any industry, and it is also widespread across business functions. Let us look at a few cases:
- Fraud detection – Payment fraud detection poses a significant threat to many businesses. Understanding the transactions in real-time to detect fraud helps companies to mitigate fraud and reduce the cost burden.
- Location data – We all now use location apps in our everyday operations. With-real time processing, customers can access more accurate information about the target location.
- Personalization – Customer choices depend on a variety of factors. As they get distracted by multiple options in the online shopping era, it is vital to grab their attention with personalization. Businesses across industries are considering a significant investment in personalization – communication, offers, products, and services.
- Internal IT monitoring – Downtime costs business significantly. So it is critical to monitor the technology assets within the business landscape without any delay.
- Customer support – The 24/7 call support is now transforming to voice assistants. With technologies like NLP and conversational AI, it is vital to understand customer queries in real-time to provide the best solution.
Real-time analytics also comes with challenges. Register now for our Dallas Data Warehousing Summit 2022 to understand the nuances and use cases of real-time analytics.