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Real-time Data Analysis To Drive Smarter & More Effective Business Decisions

Posted by Errin O'Connor on Mar, 18, 2021 05:03

What is Real-time Data Analysis and its examples? 

In the field of business intelligence, the term real-time data analysis has become increasingly popular. In simple words, it refers to a process of analyzing the raw data as soon as it enters the database. The real-time analytics users are capable of deriving practical real-time insights very rapidly. In the modern world, real-time business intelligence involves making real-time decisions.

The traditional method used for data analysis never helped businesses in mapping user behavior. With real-time data processing, businesses can now understand customer behavior, customer details, shopping pattern, and trends. Today, almost every organization has to deal with a handful of big data. Getting over the necessity and accommodating real-time big data analytics will bring bundled advantages to the organization.

First, it makes real-time data streaming a possibility. Second, the company gets to access real-time insights that are meaningful, insightful, and helpful. All the advantages and benefits drastically improve decision-making and risk-management skills.

Ideal places to use real-time data analytics,

  • In financial institutions, to decide the credit-related issues.
  • Versatile businesses can use it to enhance customer experiences by employing real-time customer analytics.
  • Retailers can use it to detect fraudulent activities at points of sales.
  • Using customer data helps to run targeting and re-targeting campaigns for increasing sales.

What are the building block of Real-time Data Analysis: A comparison with Near Real-time Data Analysis

Companies in the modern world use hybrid cloud environments. These environments consist of a mixture of on-premise and cloud systems. Here are the building blocks forming the complex real-time data analytics system,

  1. Preparing datasets close to the source 

The organizations need to prepare analyzed datasets in a location near the source of the data. In hybrid systems, different data sources available at the on-premise system collect a large amount of data. Organizations prefer a cloud environment as it provides cheaper storage space. Nevertheless, it costs money to perform computing functions in the cloud. Hence, make it a point to perform real-time data analysis near the data source.

  1. Formatting data

Make it a habit to format the raw data before initiating the data analysis process. It reduces the file size of the dataset. In the on-premise system, there is the provision of formatting the data in the columnar formats.

  1. Catalog Data

The best practice is to create a catalog first and then store it. Practicing this technique helps in the data retrieval process later on. As a result, the performance of data processing systems improves.

  1. Select the right data storage

The APIs, machine-learning, query engine performance, and other systems utilize the raw acquired data in various forms. Therefore, the selected data storage platform has to be strong, flexible, and capable enough to handle numerous workloads at a time.  

Difference between real-time data analysis and near real-time data analysis

  • In real-time data analytics, the data inputs are continuous. And, in near real-time data analytics, the data input takes place in batches.
  • In a real-time analysis, data analysis is an ongoing and non-stop process; whereas, in near real-time data analysis, the same function is not continuous.
  • In the real-time data analysis, the analysis pattern provides a steady data output. But, in near real-time analysis, there is never output of data.
  • The real-time processing method involves immediate and crucial data processing. The near-real-time analysis method does not include the constant processing of data.
  • Real-time data analysis plays a pivotal role when an organization needs to process information immediately. But the same organization prefers near real-time data analysis when compared to the output data, the analysis speed is more important to them.
  • If the real-time data results in business intelligence, then near real-time analysis produces operational intelligence.
  • An instance of real-time data analysis includes data streaming, radar systems, and others. Near real-time analysis, examples include situations like processing sensor data.

Advantages of Real-time Data Analytics:

Global organizations are producing a significant amount of data. All this data is productive and helpful to those organizations. Using this big data appropriately and for the organization’s benefit has been the focus of modern businesses. Therefore, companies started relying on big data analytics.

Thus, analyzing the data in real-time to derive meaningful and actionable insights that will help make informed decisions and achieve growth is what we call real-time big data analytics. When used correctly and knowledgeably, enterprises can reap tremendous benefits from data analytics.  

  1. Customer data

Companies can have insights on changing customer behaviors through real-time customer analytics. It aids the company to cater to the customer’s needs more appropriately.

  1. Identifying change

The reporting tools and method produces real-time data insights. These insights empower an organization to recognize changing patterns and trends in the market. It further helps to identify the probable risks for the future. Preparing strategies for facing and overcoming those risks then becomes seamless.

  1. Make corrections in functional methods

Certain production mishaps can affect the customer base of a company. The real-time analytics applications help the company to rectify such occurrences.

  1. Combining the sources of data

The real-time web analytics method helps the company in aggregating the data in one system.

  1. Increasing the functioning speed

Real-time analytics software presents the organizational data in a ready-to-analyze format. As the data received is ready for analysis it decreases the time needed for data segregation and analysis. Besides, the company employees performing the data analysis use that saved time to concentrate on other tasks.

  1. A decrease in the mistakes

The real-time business intelligence tool performs data analysis with increased accuracy. Lesser human errors and no forced errors resulting in glitch-free and error-free analysis reports.

  1. Availability on mobile devices

Anyone, from any location, at any time, and on any device can access the real-time data analysis reports. It means people working remotely or employees on a business trip can access those reports using their mobile device, provided they have a working internet connection and are authorized to view the reports.

Real-time Data Analysis: Competitive advantage overview

The real-time analytics process provides companies with a competitive advantage over other companies in the market. 

Competitive advantages of real-time analytics

  • The KPI visualization improves due to real-time analytics. It results in the enhancement of business patterns.
  • Real-time customer analytics presents the company with accurate information about the customers. It further helps companies to cater to the customers more.
  • Real-time big data analytics is one of the major components that detect possible risks and threats to organizations. The companies can then prepare well for managing these risks.
  • The real-time testing analytics provides information about the demands of the changing market.

Real-time Data Analytics Architecture: Brief Understanding

The real-time data analytics architecture is a framework of software components. These components acquire, ingest, and analyze large volumes of data. Besides, the real-time data analytics architecture analyzes the data as per the unique nature of every dataset ingested. The analytics architectures are essential, as they can analyze multiple streams of events simultaneously with increased accuracy.

Effect of Real-time Data Analytics on marketing campaigns:

Real-time analytics patterns largely influence marketing strategies in the modern world. The constant increase in competition has forced companies to choose wisely before making campaigns. It happens It happens due to the basic fact that a campaign is a holistic investment of time, effort, and finances. Moreover, successful campaigns can increase sales numbers and enhance the customer base.

But on the other hand, a failed campaign can negatively impact the impression of the company. Real-time analytics provides the user with a constant view of the changing patterns and trends. It enables the user to understand the methods and materials that would draw more positive attention. As a result, creating successful campaigns with the help of real-time analytics becomes an easy job.

Challenges faced by real-time data analytics

Some of the most gruesome challenges faced by real-time data analytics are as follows –

  • Network speed

Real-time data analysis becomes easy and seamless when information transfer from an on-premise system to the cloud storage lake happens. Network speed plays a dominating role in data transfer as an on-premise system has restricted memory structures; hence the data needs to travel long distances.

  • Computing capacity

The local data center has to have an immense computing capacity to receive data from infinite sources. Lower computing capacity fails to analyze a large volume of data. Therefore, regularly enhancing the computing capacity of these data centers results in uninterrupted real-time data analysis.

  • Availability

It is mandatory to prepare the local data centers, so it starts analyzing the data from various sources.

What is the Power BI approach for Real-time data analysis:

The real-time data analytics is carried out in Power BI with the help of real-time streaming. This feature enables the user to stream data and update dashboards and reports in real-time. All the reports and visuals created using the Power BI tools display the information in real-time. Using data from multiple sources, it becomes easier to achieve such a mesmerizing real-time data analytic performance.

These sources could include devices, sensors, websites, and others. But, in Power BI, real-time streaming is possible only after enabling one of the two methods. Power BI might have some limitations as you could consume data in those formats only while real-time streaming.
These methods are as follows:-

  • Tiles that contain visuals for streaming data.
  • Datasets created from streaming data that are present in Power BI.

Brief Explanation of Some Relevant Terms:

Before moving ahead, we need to discuss some relevant terms in the context of real-time data analysis 

These are as follows:

  1. Kinds of real-time data sets

In December 2017, Power BI announced the availability of the real-time datasets. Here are those datasets

  • Push dataset -In this dataset, Power BI stores the data permanently. Afterward, it performs a historical analysis using the same data. Besides, you can create business intelligence reports on top of these datasets. Once the dataset is in place, Azure SQL is added to it for enhancing its performance. Generally, a refresh query pattern occurs whenever new data pushes itself in this dataset. Every Push datasets have about 20,000 rows. However, with the inclusion of newer rows, the older ones get pushed aside.
  • Streaming – In this dataset, Power BI simply stores the data in the transient cache. In addition to that, it also disables functions like report creation and historic data analysis. The best thing is, the system uses these datasets when it requires the lowest latency. It also prohibits the creation of custom reports.
  • Hybrid – These datasets send data to both the push and streaming datasets. In this way, these datasets give the benefit of the other two kids too. But, this itself acts like storage for information.
  1. Rest APIs

Rest APIs are also known as RESTful API, are high-end application programming interfaces. The program conforms to the constraints put forth by the REST architectural style. It enables the interaction of the user system with RESTful web services. In simpler words, REST is a set of constraints of rules, not a standard or protocol. The developers of API can implement these methods of REST in several ways. Upon requesting a RESTful API, the source of the information is represented to the client.

  1. Azure Stream Analytics

Azure Stream Analytics is a  serverless event processing engine – Microsoft has developed it and is highly scalable. The data engine makes a user capable of running a real-time analysis on multiple sources of data. These data sources can be devices, sensors, websites, and others. 

A user can set alarms in the system to recognize future anomalies and predict changing trends. In this engine, the user can also build real-time dashboards that provide a live view of the data at a command. 

EPC Group approach for Real-time Data Analytics:

We at EPC Group dedicate ourselves to help organizations in deploying and using the Power BI services. Among the other services, real-time data analytics is very crucial. The organizations of the contemporary world are now focusing on big data analysis. 

In Power BI, big data analysis is done in real-time. EPCGroup performs consulting sessions and strains the existing employees of the user company. These sessions give an overview of the working of real-time data analytics. The consultants also educate the user company on the things required to perform real-time data analytics. They include stream processor, aggregator, broker, and analytic engine.

Final thoughts:

In conclusion, you can say that real-time data analytics is a step closer to improving overall analytics patterns. The companies of the contemporary can use this analytics pattern to its fullest potential.  

Among others, the real-time analytics of Power BI is more advanced. The consultants of the EPC group concentrate on consulting the employees of the user company. Regular training sessions are provided to these employees to teach them the pattern and significance of real-time analysis.               

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