A Complete Guide On Real Time Business Intelligence 
We are living in the age of information and today every organization and every device is creating huge amounts of data. But to keep up, with the increasing competition it is crucial for organizations to harness these huge datasets to make important business decisions. Staying a step ahead of your competitors requires not just data, but timely and actionable insights. Real time business intelligence solves this problem by providing organizations with real-time data which can help companies make informed decisions on the fly, respond swiftly to market changes, and capitalize on opportunities as they arise. 

If you are new to the real-time BI then this blog is the perfect guide for you. We will start by discussing the basics of real-time business intelligence and what are the benefits that it provides. We will also discuss some common types of RTBI systems and how they gather data from different sources. So, let’s get started! 

Understanding Real Time Business Intelligence 

Before we dive into the details of real time business intelligence, let us first clarify what we mean by “real-time”. Real time does not necessarily mean instantaneous but rather refers to the minimal delay between data generation and data availability. The acceptable level of delay depends on the business context and the use case. For example, real-time for a stock market trader might be a few seconds, while real-time for a manufacturer might be a few minutes. 

Real-time also does not imply that all data is processed and analyzed in literally real-time. Some data might be more relevant and valuable than others and therefore require more immediate attention. For example, a bank might prioritize real-time fraud detection over real-time customer segmentation. Therefore, RTBI involves selecting and prioritizing the data sources and the analytical tasks that are most critical for the business objectives. 

RTBI can be seen as an extension of traditional Business Intelligence (BI), which is the process of transforming data into information and knowledge that can support business decisions. BI typically involves collecting, storing, and processing data from various sources, such as databases, files, web services, etc. BI also involves applying various analytical and data visualization techniques, such as reporting, dashboards, data mining, machine learning, etc. to extract insights and patterns from the data. 

To summarize this, we can say that the real-time data analytics consists of the following important aspects: 

• Real-time data: Data that is generated and captured continuously, and reflects the current state of the business or the environment.  

• Real-time processing: The ability to process data as soon as it arrives, without any delays or lags. Real-time processing can be done using various methods, such as streaming, in-memory, or event-driven processing. 

• Real-time delivery: The ability to deliver data and insights to the end users or systems in real-time, or near real-time, to enable immediate decision-making and action. Real-time delivery can be done using various methods, such as dashboards, alerts, notifications, or triggers.

Advantages of Real Time Data On Business Decision Making 

The RTBI technology is very beneficial for all industry sectors. Some of the major benefits that these business intelligence tools and techniques can provide are the following: 

Fast And Informed Decisions: 

As you might have already guessed, the first benefit of real-time analytics is that it can allow businesses to make decisions faster and more intelligently. TBI can help businesses react to changes and opportunities in the market, customer behavior, competitor actions, and internal operations more quickly and effectively.  

For instance, let’s take the example of a hypothetical company ABC. The company sells laptops online and has to maintain a sufficient inventory for its operations. Traditional ways of managing this inventory and then collaborating with customers were very slow and increased the bounce rate. But with the real-time BI tools the sales team can access updated inventory levels at all times and can redirect the customer to another similar product. 

Improved customer satisfaction and loyalty: 

RTBI can help businesses understand and meet the needs and expectations of their customers more accurately and timely.  By using real-time data of customers and predictive analytics companies can easily personalize and optimize the customer experience, such as product recommendations, offers, services, etc. RTBI can also help businesses retain and attract customers by increasing their trust and satisfaction. 

Fault Detection: 

Imagine having a real-time detective for your business, constantly analyzing data to catch problems before they cause chaos. That’s the power of real-time business intelligence (RTBI). It continuously monitors data streams from sensors, machines, and applications, identifying unusual patterns and potential faults as they emerge. Think of it as an early warning system that flags suspicious activity, like equipment anomalies on a production line or fraudulent transactions in the financial world. But RTBI doesn’t just sound the alarm; it helps you pinpoint the root cause of the issue, saving you valuable time and resources.  

Enhanced Efficiency: 

Imagine your entire team infused with real-time intelligence, instantly spotting issues and collaborating seamlessly to solve them. This is what real-time business intelligence can provide. When every department of the company, from production to marketing has the same insights and data that showcases the current situation and is also timely available, the collaboration of the company gets a boost.  It’s like equipping your team with a real-time compass, guiding them to unlock their full potential and propelling your organization forward with unstoppable momentum. 

Common Sources of RTBI Data 

The sources for getting data are endless in today’s data-driven world. Each and every device from our smartphones to our car GPS systems can provide instant and real-time data. However, for our understanding, we can divide these sources into the following main categories: 

• Transactional data: This is the data that records the business transactions and events, such as sales, orders, payments, deliveries, etc. Transactional data can provide information about business performance, customer behavior, product demand, etc. 

• Sensor data: As evident from the name itself, sensor data is the data that is generated by devices and machines that measure and monitor physical phenomena, such as temperature, pressure, speed, location, etc. Sensor data can provide information about environmental conditions, equipment status, product quality, etc. 

• Social media data: This is the data that is generated by users and platforms on the internet, such as posts, comments, likes, shares, reviews, etc. Social media data can provide information about customer sentiment, market trends, competitor actions, etc. 

• Web data: This is the data that is generated by users and servers on the web, such as clicks, views, searches, visits, etc. Web data can provide information about the customer preferences, the website performance, the online behavior, etc.

Some Real Time BI Challenges 

RTBI is not without challenges, as it involves dealing with large volumes, high velocities, and diverse varieties of data. Some of the common challenges of RTBI are: 

• Data quality and consistency: RTBI requires ensuring that the data is accurate, complete, and reliable, as well as compatible and comparable across different sources and formats. It can be affected by factors such as noise, errors, duplicates, missing values, outliers, etc. If for some reason relevant data is not provided and a company uses incorrect information it can significantly hurt the overall business operations. 

• Data integration and management: RTBI requires integrating and managing the data from various sources and systems, as well as ensuring its availability and accessibility for analysis. Data integration and management can be challenging due to factors such as heterogeneity, complexity, scalability, etc. Data integration and management can affect the efficiency and performance of Real-time business intelligence and analytics. 

• Latency: One of the most challenging problems with real-time BI solutions is data latency. To make accurate decisions companies require spot-on data and if it isn’t received on time then big problems can arise. For example, in stock markets, a delay of just a few seconds or even milliseconds can affect huge financial decisions.

Types of Real-Time Business Intelligence Systems 

RTBI systems can be classified into two main types, based on their architecture, data processing, and functionality: 

Active RTBI: 

These are the systems that process and analyze the data in parallel with the operational systems, without affecting their performance or functionality. Active/active RTBI systems can provide near-real-time insights, as they have minimal data latency and high data availability. Think of them as an eagle-eyed sensor network alerting you to equipment hiccups, or clever algorithms flagging suspicious transactions. They are lightning-fast, super vigilant, and ideal for high-risk environments like finance or critical infrastructure. 

Passive RTBI: 

The second type is the passive RTBI solutions. They act as a watchful observer, analyzing data based on set rules. Imagine dashboards displaying real-time metrics or reports highlighting customer behavior shifts. It’s less demanding on resources, easier to manage, and great for monitoring overall performance and trends.  

Get Expert Real-Time Business Intelligence Consulting 

If you need help with your RTBI projects, contact our experts at Al Rafay Consulting. Our Microsoft-certified BI engineers have 10+ years of experience in providing premium business intelligence services to a variety of industries.