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Top Real Time Analytics Use Cases

Chandan Gaur | 06 December 2023

Real-Time Analytics Use Cases

Introduction to Real-Time Analytics

Data without context is worthless. All our efforts will be in vain if we hesitate to act, pivot, or modify. We can examine every rock and draw every potential lesson from it. We will only maximize the return on our investment if we utilize all the technology. Today, we can successfully communicate with our data, ask it questions, use it to make predictions, and train it to recognize new patterns. This is what data's potential is. The fundamental change from IT-driven business analytics initiatives to one where business and IT share in this choice is now the new normal amid the analytics market's ongoing evolution. There is no question that analytics has taken on a strategic role for most firms today.

What is real-time analytics? 

Real-time analytics is a powerful approach that enables data processing as soon as it becomes accessible, allowing for the prediction, comprehension of relationships, and automation of various processes. Real-time analytics empowers decision-makers to gain valuable insights and make faster, more informed decisions by applying logical and mathematical techniques to data. Its ultimate objective is to provide a rapid and efficient understanding of data.

As a result, new consumers and new expectations have emerged. What has changed is the requirement that judgments be made immediately and communicated to a large audience. The shifting workforce is bringing about a new method of working. Training manuals are no longer standard in offices; today's workforce demands an accessible interface for quick setup. This drives us to the notion of real-time analytics. Data scientists can now employ real-time analytics for the following aspects:

1. Making operational decisions and continuously applying them to production operations, such as business procedures and transactions.
2. Real-time viewing of dashboard displays with continuously updated transactional data sets.
3. Using current predictive and prescriptive analytics.
They are reporting both current and historical data at once.

Explore more about the top real-time analytics tools

Growing Importance of Real-Time Analytics

Real-time analytics is a method that makes use of the fact that standard batch analytics tools frequently work against the user's interests by not only analyzing data later but also by waiting for data to show up. Decision-making delays brought on by this data gap can cost businesses time, money, and resources.

Data that is streamed is almost always more profitable. Companies know that most data has a limited shelf life; therefore, the quicker they can transform data into knowledge, the more valuable it will all be.

Businesses can utilize real-time data analytics to:

  1. Predicting client behavior
  2. Fixes the technical issues with standard data batching processing.
  3. Increase speed
  4. Make wiser business choices.
  5. Be proactive in increasing client satisfaction
  6. Lengthen your reaction time
  7. Develop more innovative products and services.
  8. Process improvement and automation for business
a multi-step process and includes complex transformations that require total durability and fault tolerance. Taken From Article, Real-time Data Streaming using Kafka

How do Real-Time Analytics work?

Let's dissect the phrase "real-time analytics" to gain a better understanding of what it means:

1. Real-time

Data is continuously and quickly gathered and processed.

2. Analytics

Actionable insights are retrieved by applying algorithms to the data.

Real-time analytics is now clear to us as a process rather than a tool. Real-time operations across all areas are necessary to "make real-time analytics work."

Explore more about Continuous Intelligence in real-time analytics

1. Gather Beneficial Data

Real-time analytics can only be effective if we gather pertinent data as it becomes available. We must comprehend what information is crucial to our company and how it might be gathered. The process begins with this stage. One might be the manager of a manufacturing business, for instance. They must be aware of whether a machine is functioning flawlessly or is beginning to exhibit signs of failure. They must gather and continuously monitor data from the machine sensors to accomplish that. 

Integrating data platforms enables the seamless ingestion of real-time data, allowing for the collecting of relevant information as it becomes available. Using appropriate tools, such as the MongoDB Connector for Apache Kafka, allows data transfer with event streams to be easily implemented. This means we can efficiently extract data from Apache Kafka topics and seamlessly write it to a MongoDB Atlas cluster, among other possibilities.

2. Combine Information from Several Sources

We usually gather data from multiple sources. We must assemble the information from each source to conduct an exhaustive study. Collecting data from several sources is the second step in the real-time analytics process.

This frequently leads to cumbersome ETL (extract, convert, and load) procedures or tailor-made pipelines for convergent data. These products are expensive, challenging to manage, and slow down real-time analytics. Using the MongoDB Atlas Online Archive, we can mix transactional and historical data to produce a more thorough analysis while keeping costs low. With the Online Archive, it is possible to query and automatically archive old data simultaneously. MongoDB Atlas Data Lake also enables you to retrieve data from your MongoDB Atlas cluster and an AWS S3 bucket using a single query. This makes it simple to study both recent and old data.

3. Analyze to Draw a Conclusion

The third and last phase in the process is extracting valuable insights from the data. The real-time analytics approach makes sense at this point. However we need the appropriate tools to examine data. The secret to success is being able to query data to comprehend and analyze it. That issue is resolved differently by several technologies. For instance, the MongoDB Query API enables on-the-fly data analysis in our operational database.

Batch and real-time processing are standard data processing methods with unique characteristics and applications. Taken From Article, Batch vs Real Time Processing

Use Cases of Real-Time Analytics

The trending Real-Time Analytics Use Cases are below:

1. Supply Chain

i. Logistics fleet managers can use real-time analytics to track shipping fleets and vehicles, optimize routes, and avoid bottlenecks like traffic problems to ensure the quick and secure delivery of goods. A travel route planning algorithm in contemporary data analytics software for transportation and logistics helps to optimize routes. Real-time data is fed into a route planning algorithm to identify the most inexpensive, effective, and rapid delivery route. Drivers can also benefit from this because they can avoid obstacles on their routes and save time.  
ii. Real-time data that is continuously collected over time can aid in identifying persistent problems that drivers encounter. For instance, a fleet controller can investigate if two drivers are traveling the same route, and the sensors indicate that one is using much more fuel than the other. Fleet management can use an electronic logging device (ELD) for driver behavior analytics. By informing drivers of regions with risky turns, you can raise their knowledge of safe driving among them and prevent potentially disastrous future situations. 
iii. Real-time analytics can be used to reduce operational hazards. Unexpected fleet or plant repair requirements occasionally make it difficult to run the supply chain. Data science-based techniques can assist you in determining when your equipment might break using real-time analytics. Thermal imaging, vibration analysis, infrared, and acoustics are all employed. Utilizing remote sensor networks, real-time analytics uses these technologies to measure and gather operational and equipment data in real-time (for example, using oil sensors to find wear debris). This may lower the cost of maintenance. For instance, you could utilize an accelerometer to gather information for your real-time analytics system's vibration analysis. The accelerometer's voltage signal displays the machine's frequency and vibration, produced every minute or second. These signals are translated into a time waveform or a rapid Fourier transform. 
iv. Enterprise resource planning (ERP) systems and uncoordinated data storage systems were the norm in supply chain management. As a result, data updates communicated across stakeholders were based on a predetermined time frame (such as daily or hourly). Today's continual variations in supply and demand necessitate real-time data collection and analysis from vendors. For instance, you can examine inventory turnover, a crucial inventory measure, in your supply chain dashboard. A higher inventory turnover shows that you are meeting demand and that your products are moving fast through the supply chain. You should examine the most recent social media sentiment data for demand forecasting.

Explore more about Supply Chain Analytics and its Use Cases 

2. Finance

i. The ability to correlate, evaluate, and take action on financial data, such as transactional data, corporate updates, price trends, and trading data, is provided by real-time analytics. Every millisecond, this data is generated from several sources in vast amounts, and financial institutions and banks must move swiftly to use it. 
Real-time analytics can spot market manipulation patterns, particularly illicit trading, and price tampering carried out in real-time for financial advantage. In stock trading, it's common to make money by engaging in questionable practices like insider trading or artificially inflating or deflating stock values. To spot potential efforts to influence the market, real-time analytics can be utilized to gather information from Twitter streams, newsfeeds, business declarations, and other external data sources. 

ii. The banking industry frequently has trouble catching payment fraud and money laundering. In addition to having a negative financial impact, it harms the bank's reputation. Banks can utilize Markov modeling and machine learning techniques to protect themselves from fraud by using real-time analytics. Using real-time analytics, banks may convert their expertise about how fraud operates into a set of rules that can instantly examine incoming data streams. Systems with random changes are modeled using Markov models. They calculate the probability of various states and their change speed. They are employed in fraud detection to identify unusual transaction cycles because of their capacity for pattern recognition and prediction. In this approach, banks can try to spot sophisticated fraud schemes where skilled criminals split up large transactions into numerous smaller ones to facilitate money laundering.

Deep dive into Analytics Platform for the Banking and Finance Industry

3. Manufacturing

Manufacturing operations like inventory management may be continuously monitored, managed, and improved with real-time analytics. It can also alert you to bottlenecks and let you see how your manufacturing plant operates in real-time. The facility's installed machinery, sensors, CRMs, ERPs, and extra cameras can all provide this data. One may obtain a thorough overview of the activities taking place with your inventory in real-time using real-time analytics. This covers the likelihood of sales, inventory prices, and the condition of aging goods. For instance, watching a dashboard for aging commodities can help you avoid having unsold inventory and enable you to sell soon-to-expired things first. The following are some implications for real-time analytics in inventory management:

i. Descriptive analytics concentrates on the "what," i.e., what are your core inventory figures? These figures are displayed on dashboards. For example, you can examine a dashboard to see how much each unit of the recently delivered goods at the warehouse costs.
ii. Diagnostic analytics seeks to identify the underlying reason for the presented data. For instance, if you want to know why, diagnostic analytics can offer insights into the choices that sparked growth in your company's month-over-month performance.
iii. Predictive analytics leverages your real-time data to predict your future. For example, real-time analytics can leverage reports of a new COVID-19 variant outbreak to alert you to a potential PPE equipment shortage.
iv. Prescriptive analytics suggests the course of action you should take. For instance, it can instruct you to complete 80% of a client's orders within four days.

In many industries, preemptive maintenance can help cut maintenance and downtime costs. A manufacturing organization, for instance, might be using a machine that is starting to malfunction. This failure can be instantly fixed if you can identify it. Real-time analytics are crucial in this situation.

4. Customer Experience Management

Real-time analytics can offer up-to-date information about an organization's customers and communicate it to ensure better and faster business decisions can be made within the time frame of customer interaction in customer experience and relationship management.

The use of real-time analytics by enterprises is illustrated by the examples below: 
Functionalities for customer-facing apps are being fine-tuned. Real-time analytics helps make information-driven choices for core feature management and raises the complexity level of software rollouts.

I am using location information. Real-time analytics can help identify the data sets pertinent to a specific geographic area and alert users to the necessary updates. 
Strengthening marketing and advertising initiatives. Real-time data analysis can draw conclusions that will improve audience targeting, pricing schemes, and conversion rates. Data sources include ad inventory, web visits, demographics, and consumer behavior. To boost the likelihood of a sale, businesses can utilize real-time analytics to offer customers incentives and promotions while surrounded by the item in the store.

Users of a CRM business's suite of marketing, support, and sales solutions received a unified, 360-degree view of engagements from that company. The users could acquire a single view of each contact and corporate-level data in one location rather than accessing each system to obtain a customer profile component. To engage customers, it was essential to comprehend the customer journey more effectively and develop new prediction capabilities.

5. Healthcare  

Treatment adherence: “Special therapies are complex; patients often face problems including side effects or treatment failure and require active monitoring and individual engagement to keep patients compliant with treatment. The drug may be stopped if the side effects are severe or if the person no longer feels the benefit of the drug. Adequate monitoring to detect if this is happening and taking appropriate action are critical to saving payers money. Real-time analytics provide healthcare providers with updates every second. Healthcare professionals can view medical information as well as receive suggestions and recommendations based on this information. The real-time system is designed to pull all relevant data points (eg progress and nursing notes) from the EHR and identify patterns that can help make a diagnosis. It can detect subtle changes in patient condition as they occur and highlight individuals in need of priority due to deteriorating condition. An electronic health record (EHR) is a digital record of patient information stored in a database. This includes medical history, prescription, laboratory results, diagnosis, and treatment. Electronic health records can collect and display data, but cannot analyze this data in real-time.

Know more about Data Analytics in the Healthcare Industry

6. Personalization

One of the main applications of real time analytics is analyzing user behavior to offer customized experiences. A buyer, for instance, might be curious about a product they just bought. This customer's likelihood of returning to your store can increase if you give them a tailored experience.

The so-called "next best offer" study is a typical illustration of predictive analytics. This method uses real time analytics to present users with a suitable offer based on their past interactions and behavior. A simple example is displaying relevant products to an e-commerce customer based on the items they have already viewed. Given that the customer is engaged with the website, this analysis should occur immediately. A more detailed illustration is the "personalized shopping experience." As you might have observed, this analysis is done in real-time by merging transactional data (the active browsing session) with primary data (purchased products). Real-time customization boosted the customer's overall order value and contributed to the market success of new product lines. However, it can be challenging to apply because many analytics systems were created for batch processing, which produces outdated personalized offers.

7. Fraud and Error Detection

One of the main applications of real time analytics is detecting fraudulent behavior. For instance, real-time detection and prevention of fraudulent credit card transactions is possible. The processing and examination of the data could take hours, even though traditional analytical procedures can detect fraud. Real time analytics enable the instantaneous detection of fraud.

The same anomaly detection approach can be applied to prevent or identify clerical errors. As an illustration, a vendor might change the prices of a line of goods in an online store. This is a surefire way to get price problems and clerical mistakes. To the rescue: automated anomaly detection! The seller may be alerted, and the price may be fixed if an anomaly is found.

8. Process Optimization

One of the objectives of digital transformation was to optimize current operations. A company process won't necessarily get better by being digitalized, though. With the help of digitalization, you may gather and analyze data to produce valuable insights. But how can you improve an active process? You can, however, analyze it immediately and make the required corrections. Real-time modifications should be possible during the process. Application monitoring is an additional use case. System logs can be gathered and analyzed to find faults quickly. This frequently enables you to fix the issue before it results in an outage. For instance, a web application's performance might have declined. You can fix the issue before the website drops and roll out the updated version.

A process that is amenable to real-time optimization is production planning. You may automatically change the production plan to accommodate client needs by integrating real time analytics with production planning. You can build a nimbler supply chain by tracking demand in real-time. Automating routine human jobs is another part of process optimization. For instance, a call center representative frequently repeats the same queries. Many conversations with clients can be automated using chatbots. Real time analysis of client dialogues with chatbots and agents can help the chatbot be more accurate and effective.

What are the Benefits of Real-Time Analytics?

Real-time analytics allows companies to respond instantly, spot patterns in user activity fast, seize opportunities that might otherwise be lost, and head off issues before they start. Real-time analytics also has the following advantages:

1. Data Visualization

Real-time data may be visualized and represent events across the organization, whereas historical data can only be used to create a graphic to convey a general picture.

Discover further insights into Visualizing Real-Time Streaming Data

2. Better Competitiveness

Real-time analytics enables businesses to spot patterns and benchmarks earlier than their rivals, who still rely on historical data. Businesses may analyze the performance reports of their partners and rivals instantly, thanks to real-time analytics.

3. Exact Information

To save time collecting pointless data, real-time analytics focuses on quick analyses that are constantly helpful in developing targeted outcomes.

4. Lower Prices

Real-time technology can be costly, but its numerous and ongoing advantages make it more cost-effective over the long run. The technologies also assist in preventing delays in the use of resources and information delivery.

5. Faster Outcomes

Automatically categorizing raw data enables queries to gather the correct data more effectively and quickly sort through it. This makes trend prediction and decision-making possible more quickly and effectively.

What are the Challenges of Real-time Analytics?

The issues with real-time analytics are:

1. Its Definition

The ambiguous concept of real-time and the varied criteria that follow from the various interpretations of the term are one of the biggest challenges in real-time analytics. To agree on a particular definition of real-time, what is required, and what data sources need to be used, enterprises must invest significant time and effort in gathering precise and thorough needs from all stakeholders.

2. Requirement of the System Architecture

The difficulty is to develop an architecture that can handle data quickly once the company has determined what real-time implies by consensus. Sadly, the processing speed requirements for different data sources and applications might range from milliseconds to minutes, making it challenging to design a strong architecture. Additionally, the architecture must be able to scale up as the data grows and be able to handle sudden changes in data volume.  

3. Changes to Business Processes

A company's internal operations may be hampered by deploying a real-time analytics system. Businesses frequently neglect internal process improvements because of the technical requirements to build up real-time analytics, such as developing the architecture. Businesses should see real-time analytics as something other than their objective but a tool and place to start when enhancing internal operations.

4. Team Member Education

Finally, while integrating real-time analytics, businesses can discover that their staff is resistant to change. Businesses should, therefore, concentrate on educating and thoroughly articulating to their workers the benefits of the switch to real-time analytics.

Deep dive into Real-Time Analytics Architecture and Design

Summary of Real-time Analytics Applications

The utilization of real-time analytics will significantly impact pharmaceutical advertising and sales in the future. Instead of depending on conventional approaches, more drug manufacturers are expected to start utilizing emerging technology and applying real-time analytics. This will allow them to acquire more profound insights into consumer behavior and the state of the industry. Accurate forecasting can lower expenses while improving revenue and sales through marketing optimization.

Real-time analytics are altering higher education as well. Organizations can promote potential pupils who best fit their institution based on criteria like exam results, academic records, and financial position. Educational institutions can assess students' likelihood of graduating and using their degrees to find lucrative employment. They can also forecast a class's debt burden and income after graduation with real-time, predictive data analytics.