In today's fast-paced business world, it's more important than ever to have access to real-time data to make informed decisions. That's where real-time analytics comes in. Real-time analytics is the process of collecting and analyzing data in real time, allowing businesses to make quick decisions based on current trends and patterns. 

In this article, we'll explore the benefits of real-time analytics, how it works, and provide examples of real-time analytics in action.

Real-time analytics refers to the ability to provide data for analysis almost immediately after it is collected. This allows users to analyze and understand data from a system in real time, providing insights for real-time decision-making. With traditional business data being historical, digital business data is constantly changing, requiring the ability to see, analyze, and act on data in real time.

While real-time technology has been used for decades, custom development of real-time business reports and dashboards was required for operational staff, limiting their use to niche business areas. However, real-time analytics provides a common view of operations and can tremendously improve the way businesses are run. By visualizing and analyzing historic and real-time data together, businesses can improve their knowledge of past events and better respond to current conditions.

Real-time analytics can also provide high-value insights and opportunities by setting alerts for key problems and allowing users to drill down into emerging patterns in real-time data. Analytics can be on-demand or continuous, with continuous updates being programmed to automatically respond to certain events. Examples of real-time customer analytics include tracking orders and customer activity for a better understanding of user behavior and targeting customers with promotions during their shopping experience.

If you're looking for a real-time analytics solution, FineBI is a great option. With its real-time unity dashboard capabilities, you can track your data and make informed decisions on the fly. Its powerful analytics tools allow you to analyze and visualize your data in real time, providing you with the insights you need to stay ahead of the competition.

Also, as a self-service real-time BI tool designed for business/data analysts to do problem-oriented exploratory analysis, you do not need to know SQL or other codes to use FineBI, every business personnel can use FineBI to easily establish professional real-time BI dashboards. Through simple operations like drag-and-drop, you can create professional dashboards without difficulty.

Most BI and analytics data is analyzed on a monthly, weekly, or daily basis. However, organizations need to be able to analyze data in real time as it is being generated to respond quickly to frequent changes and take advantage of real-time opportunities.

Real-time analytics offers many benefits, including increased speed to insight. With real-time analytics, businesses can work faster to make necessary changes or respond to critical information, which can help organizations identify potential problems and seize opportunities when they arise.

Real-time analytics can also improve the customer experience by helping businesses anticipate problems and streamline operations. These on-the-fly adjustments can greatly influence customer interactions and improve the end-to-end experience.

In addition, real-time analytics allows organizations to gain a clear view of the business and understand how to address potential operational issues. It helps users to understand what resources are available to make necessary changes.

Finally, when there is a need for deeper analytics to inform business decisions, real-time analytics can compare real-time and historical data to provide a deeper understanding. This can help businesses make informed decisions based on the most up-to-date information available.

Real-time analytics can be broken down into two components: "Real-time" refers to the rapid and continuous collection and processing of data, while "analytics" involves the application of algorithms to extract actionable insights. To achieve success with real-time analytics, all components must operate in real time.

Here are the five main steps in the real-time analytics process:

The first step is to collect useful data that are important for your business. For instance, if you're running a manufacturing company, you need to collect data from machine sensors to determine whether a machine is working properly or showing signs of failure. Integrated data platforms such as Apache Kafka provide real-time data ingestion, and the MongoDB Connector for Apache Kafka enables you to consume data from Kafka topics and write it to a MongoDB Atlas cluster.

In practice, data is usually collected from multiple sources. To perform a complete analysis, you need to converge data from all of these sources. 

FineBI is fully capable of managing data from multi-source based on business. Including database tables, SQL sets, Excel, self-service data sets, etc. And FineBI also supports instant classification.

The third step is data stream processing. Data streams can be processed in various ways, such as splitting, merging, calculating, and combining with external data sources. Once processed, the data can be read by visualization components.

In FineBI, data processing is also very convenient. Through functions such as filtering and merging calculations in FineBI, data can be processed quickly without writing SQL statements or other codes.

Visualization components read the processed data. The processed data is stored in a structured format (such as JSON or XML) in the database and is read by visualization components. The refresh rate for processing data in the JSON/XML file is called the update interval.

FineBI supports more than 50 chart styles, covering basically all basic and high-level charts on the market, and also has excellent dynamic effects and a powerful interactive experience. Various features can be set according to the needs during use, and can also be self-adjusted and displayed on the mobile terminal and large LED screen.

Visualization components draw charts, gauges, or other visual behaviors from the structured data file (JSON/XML) on the chart interface. The frequency at which processed data is displayed on the client machine is called the refresh interval. In some applications, such as stock trading applications with charting, data stream-based triggers are pre-set.

FineBI updates data in real-time on the dashboard for businesses to make informed decisions through real-time data analytics. Also, FineBI supports multiple data functions like data filtering, data drill-down, and so on, which provides users with different points of view, allowing them to navigate multiple layers of data without having to create new ones. This makes it easy to improve the user’s understanding of the data and the reasons behind the graphs. 

Real-time data analytics has revolutionized data collection for organizations, allowing them to gather information from user interactions, operational infrastructure, and machines in real-time. With this technology, companies can act on data as soon as it enters their systems, leading to a competitive advantage in various industries. For instance, real-time analytics can aid in detecting fraudulent activity in finance, enhancing supply chain execution and delivery speed, and optimizing inventory management in manufacturing.

Business representatives from consumer goods companies need to upload customer data, sales data, and competitor product data via their mobile devices every day. In the past, the business and analytics systems were separate. After 12 p.m. each day, the analytics system extracted data from the business system and produced corresponding analytical reports. Sometimes it took half an hour or even longer to generate the report based on the previous day's data. However, with the use of real-time FineBI analysis systems, the speed of report generation is dozens or even hundreds of times faster, and it only takes a few seconds.

The sales representatives' time is very valuable. They have many customers to visit every day, and each one takes up time. With the real-time BI analysis system, they can spend more time visiting and developing their customers. More importantly, for fast-moving consumer goods companies, they can quickly see the sales situation and the status of their competitors, and make real-time decisions, which can greatly improve the company's competitiveness.

Real-time analytics is particularly suited for the finance industry due to its association with vast amounts of data, high levels of volatility, and the requirement for real-time detection of intricate patterns. Real-time analytics enables the correlation, analysis, and execution of actions on financial data such as transactional data, market prices, company updates, and trading data. The finance industry receives a continuous stream of data from various sources at an extremely high volume per millisecond, making it essential for financial institutions and banks to act on this data swiftly.

Real-time analytics has proven effective in detecting market manipulation, especially in identifying insider trading and price manipulations that aim to gain profit in real time. Dubious methods like artificial deflating or inflating stock prices can be detected by collecting data from various sources, including Twitter streams, newsfeeds, and company announcements.

Generative Adversarial Networks (GANs) are one of the models used to identify manipulation in stock pricing. This model uses a type of classifier to differentiate between real and fake data. The generator is used to create data that resembles manipulated stock prices, which is used to train the discriminator to identify correct or fake price data.

Additionally, real-time analytics can be used to prevent money laundering and payment fraud in the banking sector. Machine learning and Markov modeling can be applied to analyze incoming streams of data in real-time, using a set of rules based on the bank's specialized domain knowledge of fraudulent behavior. This can help banks to avoid financial losses and reputational damage caused by fraudulent activities.

Real-time analytics is a valuable tool for the manufacturing industry, but many executives struggle to derive value from it. Although 72% of manufacturing executives view advanced analytics as important, only 17% have found it to be satisfactory. Implementing real-time analytics can greatly improve operational efficiency and help you continuously monitor and adjust manufacturing processes, including inventory management. By collecting data from a variety of sources, such as CRMs, ERPs, machines, sensors, and cameras, you can track the functioning of your manufacturing plant in real time and identify any bottlenecks that may arise.

Real-time analytics offers valuable insights into inventory management that can help you monitor your inventory status, sales potential, and product aging. By utilizing a real-time analytics dashboard, you can stay on top of your inventory and prioritize sales for soon-to-be expired products. There are four ways that you can use real-time analytics for inventory management:

1. Descriptive analytics provides an overview of your inventory figures, such as the cost per unit of newly arrived items in the warehouse.

2. Diagnostic analytics helps to identify the root cause behind inventory changes, such as the reasons behind month-over-month growth.

3. Predictive analytics uses real-time data to anticipate future events, such as predicting shortages of PPE equipment in response to a new COVID-19 variant outbreak.

4. Prescriptive analytics recommends the actions you should take, such as filling 80% of orders for a client within a four-day time frame.

In conclusion, real-time analytics has become an essential tool for businesses that need to make quick decisions and stay ahead of the competition. Real-time analysis can provide a wealth of information about customers, operations, and market trends in real-time, helping organizations to make informed decisions based on current data. Real-time analytics can also help businesses to identify potential problems and respond proactively, rather than reactively, to events. If you are looking for a reliable real-time analytics tool, we recommend FineBI, which provides comprehensive data visualization and analysis capabilities for businesses of all sizes. 

With FineBI, you can easily create real-time dashboards, analyze data, and make informed decisions based on real-time information. So why wait? Start using real-time analytics today and take your business to the next level.