Real-time Analytics for Monitoring and Testing
Big Data with Microsoft Power BI

What is Real-time data analytics in big data?

The discipline that applies logic and mathematics to real-time data analytics to provide information for quickly making better decisions is called real-time analytics. It refers to planning and measuring data as soon as it enters the database. In other words, users get insights immediately after the data reaches their system, which may conclude.

Real-time data insights help organizations to respond without hesitation. By using real-time data analytics, they can stop challenges before they happen.

Examples of real-time data procession usage:

• Financial institutions use predictive analytics to determine whether to extend credit automatically.
• Companies use real-time analytics to understand user behavior in Customer Relationship Management (CRM). It helps them optimize loyalty and company outcomes during each contact with the customer.
• Fraud detection during sales.
• Using historical data analytics for targeting individual consumers in shopping stores with discounts and rewards.

Benefits of real-time data analytics

Real-time analytics is a powerful tool for instantly deducting results from a pool of data. The following are the benefits of real-time data insights to any organization using them:

Faster response time

With instant answers at their fingertips, companies can confidently anticipate and refine their data to identify the best available solutions.

Real-time business intelligence

Sudden shifts in the economy can mean great opportunities for companies. Real-time analytics will make sure you get ahead of problems that could cost money or, conversely, could be a major money-maker.

Cost benefits

Real-time reporting can help increase sustainability by saving resources around the company in areas such as recruiting and retention, staff involvement, and, of course, reducing the workload of the IT department. Thus, data analytics provides a competitive advantage to the organization.

Understand the consumer

Using real-time customer analytics, companies can quickly foresee what consumers want, their needs, and the challenges they encounter. It ensures a more customized experience that benefits the company.

Reduction of errors

The use of in-database analytics eliminates coordination loss and accelerates data convergence. All business details can only be in one place, making analytical processes more efficient and successful.

Follows agile models

Using the database with analytics, interactive analytics tools present the information in a simplified and ready-to-analyze manner, reducing the need for detailed documentation. In this way, workers who already have this obligation will have more resources left for other activities, improving their productivity.

Simplified aggregation from multiple sources

With real-time web analytics, all organization-related information can be aggregated into a single system. This way, access to information is much simpler and the time historically spent gathering information from multiple sources is minimized.

How do real-time data analytics work?

Real-time analytics is a rapidly rising technology. The developmental idea used is complex. Following is an explanation of how predictive analytics has used computational methods to work so efficiently.

• When data was collected from the data sources, data were computed and analyzed using machine learning and the earlier data (from the data warehouse).
• Later, the data was made available to the user (maybe business, clients, or sometimes, normal people) through IoT data analysis.
• The process took a lot of time, and it needed months for a business to take action on the informational statistics generated.

Real-time processing also takes place at the network's edge to ensure that data analysis is carried out as close to the root of the data as possible. In addition to cutting-edge computing, other innovations that enable real-time analysis include:

Memory processing
It is a chip architecture in which the processor is built into a memory chip to eliminate latency.

In-database analytics
It is a technology that enables data analysis to be carried out inside the database by building computational logic into the database itself.

Data warehouse appliances
It is a mix of hardware and software products primarily designed for computational analysis. The appliance enables the purchaser to install a high-performance data warehouse straight out of the box.

In-memory analytics
An approach to querying data as it remains in random access memory, as opposed to querying data stored on physical disks.

Massively parallel programming
Coordinated execution of the program by several processors running on separate areas of the program, each using its operating system and memory.

A real-time data procession engine takes in data from machine learning calculated models, preprocessed data from data warehouses, and streamed data from raw data lakes. The engine derives statistical results through computational methods and models from the refined data within minutes or seconds.

Next, it presents data in a real-time dashboard, notifications, or messages as output.

Data Analysis for IoT application in real-time processing using Microsoft Power BI and Azure stream analytics

Data analytics has surely transformed within the decade. Technologies like Microsoft Power BI and Microsoft Azure Stream Analytics are flexible frameworks designed for self-service BI, and data analytics delivers real-time insight into business data. Following is a quick look at the powerful technologies currently serving the big data and IoT industry.

Microsoft Power BI
Power BI is a cloud-based analysis service that provides quick insight and is used to extract and visualize data. Power BI draws together data from various sources to provide a holistic view of the company's information properties.

Microsoft Azure Stream Analytics
Azure Stream Analytics is Microsoft's completely operated, serverless online analytics engine. It allows for real-time research on various data streams from sensors, online data sources, social media, and other applications.

Azure Stream Analytics can be used if the input data is in AVRO, JSON, or CSV format, and the application logic can be coded in a query language such as SQL. The whole programming work in Azure Stream Analytics is declarative and does not need you to be a specialist in programming.

Data visualization of real-time IoT data in Microsoft Power BI
With Power BI, you can view all the data from a single glass pane and easily create an analytical framework for data monitoring and reporting. Live dashboards and analyses display metrics and performance benchmarks focused on data both locally and in the cloud. This gives you a consolidated view of your entire company, independent of architectural principles.

Challenges encountered with Power BI:

Provides real-time analysis
Since data is always in various places, it can be difficult for business customers to get a full image of the business.

Multiple data source management
Data residing in SaaS solutions and other remote locations can be difficult to handle or safely retrieve and repair programmatically.

Provides correct data at the right time
Mobile business consumers require up-to-date operating data by clicking a button, regardless of location and system.

Basic Features

• Pre-built dashboards and reports for common SaaS solutions such as Marketo, Salesforce, GitHub, ZenDesk, Dynamics CRM, and many more (> 120 connectors)
• Dashboards and visualizations that support real-time notifications
• Safe live access to local data sources to obtain visibility into the full continuum of business information (Analysis Facilities, Azure SQL Database, SQL Database Auditing, Azure SQL Data Warehouse)
• Automated planned notifications to keep the Power BI data locally aligned with data sources
• Native smartphone apps to allow consumers connectivity on the move
• Intuitive exploration of data via text entry

Biggest Challenge of Real-time Analytics
Real-time Analytics is an evolving technology. Several companies embrace technology to improve their business results, while many face challenges in using IoT data analysis and implementing the results. Following are the challenges faced by companies today while using the technology:

Data Quality
Since real-time reporting allows decisions to be taken easily based on the current data, one must enter data correctly. According to Gartner, 75 percent of the companies had consequences of erroneous results that harmed corporate budgets, with half of the additional expenses incurred to reconcile the evidence. Also, bad data consistency impacts many divisions of companies.

Information Deficiency
Another downside to real-time analytics is the confusion about exactly what to do with all the available data. Ironically, the preponderance of up-to-date records could make certain businesses paralyzed by confusion about what to do with the details. Many businesses do not have data plans to understand real-time analytics's advantages. In a survey of 1,600 firms, just 4 percent of businesses had mechanisms to understand their data's commercial and operational advantages.

Affordability
Companies who struggle to understand and accept the DDS face making their company lag behind. Financial entities are a perfect example of the value of including real-time data. By moving to digital, European banks will reduce costs by between 20% and 25%, according to McKinsey's study. However, delaying digitization will place up to 30% of the bank's sales in jeopardy, leaving it to other banks to easily capitalize on digital information.

Traditional job mindset
Of necessity, this "traditional reporting" mentality would become a barrier to introducing cloud-based reporting tools. Since difficulties in setting up enterprise-wide applications and the subsequent data migrations would be seen by some short-sighted traditionalists as not worth the effort, it would be necessary to psychologically educate staff with help and instruction in the software reporting process.

The future ahead
Organizations introducing data analytics improvements should predict revenue margins to rise between 8 and 25 percent. From the book Sales Growth: Five Validated Tactics from Global Sales Experts reveals how to incorporate improvements based on big data knowledge in the areas of multi-channel experience, cross-selling, positioning optimization, location-based sales, assortment optimization, and predictive analytics increased sales by 2-8 percent. The future of real-time data analytics tools is bright and well-positioned.

Conclusion
Real-time analytics has positioned itself with a promising future ahead. The Grand View Research forecasts the global streaming analytics market will grow at a compound annual growth rate of 29 percent through 2025, starting from $6.32 billion in 2018, as businesses invest in improving their performance and operations. It is high time companies start implementing and embracing the new technological change.