Real-Time Analytics in Big Data for Quick Actionable Business Insights
The internet dominates modern work culture. Speed has become quintessential as the environment in which today’s businesses operate is changing faster than ever. Being agile and speeding up the processes is the mantra to survive the rising competition and maintain pace with change. In this fast-paced business environment, real-time analytics helps organizations become agile and faster.
Being agile means making informed and quick decisions, respond speedily to exploit opportunities, and mitigate risks. Even a minor delay counts as a lost opportunity. Every organization must learn to stream actionable insights effectively. With real-time analytics in big data, you would expand real-time responsiveness to achieve your goals.
In 2020, the real-time analytics market was valued at $ 7.08 billion, expected to reach $ 38.53 billion by the end of 202. The market is expected to grow at a CAGR of 32.67% from 2020 to 2026 – (Mordor Intelligence).
What is Real-Time Analytics in Big Data?
Real-time analytics in big data is a process to immediately gather, prepare, and measure accessible data as soon as it enters the system to offer near-instant insights. Simply put, users get immediate insights through accessible data to adjust, improve, and capitalize on opportunities.
Real-time analytics is a special kind of analytic technique in Big Data where the incoming data is processed and analyzed as it arrives in real-time. It is significant because of its capability to deliver real-time insights that are informative, actionable, and yield business value.
Benefits of Real-Time Data Analysis
The capability of a business to process mass data is paramount in the fast-paced business world. To thrive and reach optimal productivity, not only need better decisions, but faster decisions are necessary.
Real-time data analysis helps businesses in minimizing risks, decreasing costs, insights on employees and customers. Besides, it extends a better understanding of the working environment and the financial health of the business.
Here is a list of a few benefits businesses achieve using real-time data analysis,
- Data Visualization: Real-time data, used for data visualization that reflects changes in the business as and when they occur in real-time. It means the insights you get are accurate and interactive at any given moment.
- Testing: It becomes easier to take calculated risks once real-time data analysis shows you how the changes you intend to apply will impact your business. After making changes, you can gauge negative impacts and revert decisions without taking much heat.
- Real-time Monitoring: With proper knowledge and insights related to employee or customer behavior, you can dive deeper into the issues and concerns to understand what is working, what is not, and what is beneficial for your business.
- Cost Reduction: No need to hire big-data experts of coding enthusiasts to drive the sense of all your business data. Business analytics software with real-time analysis capabilities will do the job for you. It will also reduce the bottlenecks to ensure teams can customize what insights they need from the big data.
- Enhanced Decision Making: Real-time data analysis empowers organizations to move ahead with better and faster decisions. The accurate insights allow you to make informed decisions, update and introduce new ideas and processes with minimal risks.
How Does Real-Time Analytics Work?
It is simple. It either pushes or pulls data into a specific system with streaming. Streaming, however, requires various resources. Other people raise concerns that streaming may be impractical for other uses. The trick here is to pull data in intervals. It can be a few seconds or hours.
The data analytics method requires several components to work. Real-time data analytics works using the following components
- Data Aggregator: It aggregates or pulls data from numerous data points.
- Data Analytics Engine: It compares data values for streaming while performing analysis.
- Data Broker: It generates data demand or creates data availability.
- Data Stream Processor: It performs real-time analytics by executing logic and by receiving and sending data.
Real-time analytics also require processing in memory, in-database analytics, massively parallel programming, and in-memory analytics.
Data Analysis for IoT Application in Real-Time Processing Using Microsoft Power BI and Azure Stream Analytics
For years, since its inception, Microsoft has been helping businesses in every possible way. By augmenting evolving technologies, Microsoft is simplifying and automating the business operations.
Started as a mobile-first concept, Microsoft Azure is today amplified as a cloud-specific approach. Today, it is in direct competition with the major players. Similarly, Microsoft Power BI fosters real-time data analysis for businesses to take better, insightful, and improved business decisions.
Microsoft Power BI triggers data extraction, analysis, and visualization. However, its most exciting feature is integration with IoT solutions. It allows organizations to access, display IoT data in real-time, which quickens the decision-making time. Using the Azure platform, Power BI can stream a range of data sets accepting data. It then creates data dashboards for storing, updating, and displaying data obtained from all the streamed data sources.
For example, a manufacturing unit with IoT devices can utilize Power BI and Azure stream for real-time display, analysis. It empowers them to get instant and insightful data for better decision-making.
Real-time analytics in Power BI
Previously, you needed an on-premise gateway acting as a passthrough to refresh and migrate data from a source into the Power BI. In the Power BI Pro plan, users can now schedule up to 8 data refreshes in a day and 48 times in a Premium plan.
Now Power BI offers real-time analytics in two ways – Automatic Page Refresh and Streaming.
- Automatic Page Refresh: Automatic Page Refresh connects easily with the existing database and easy to set up by leveraging all the visuals and features of Power BI accessed with streaming datasets. It refreshes data as frequently as in one second.
- Streaming Data: All the streaming datasets in Power BI come from Azure data Streams, PubNub, and Rest API to offer real-time data. As of now, it supports only text, number, and date/time types of data.
Microsoft Power BI and Azure Stream Analytics allow you to read and even examine data whenever it flows into any system.
Azure Stream Analytics is responsible for analyzing and processing high volumes of streaming data from countless sources simultaneously. Microsoft Power BI will provide real-time dashboarding.
Data Visualization of Real-Time IOT Data in Microsoft Power BI
The business generates data on many key performance indicators, including sales revenue, costs, marketing performance, staffing levels, customer interactions, production metrics, and inventory levels.
However, it is complicated and poses complexity for people to examine and comprehend much data. Things changed today.
As Azure Stream Analytics examines and processes streaming data, Power BI turns every granular data into easy-to-understand information. It is convenient.
Challenges in Real-Time Analytics
Although the pace may vary, analytics in real-time must process data at the right speed. The architecture should also handle the increase of data volume and be easily scaled up when needed. Data architecture consulting is a recommended approach for this.
Change Comes with Resistances
Implementing real-time analytics in a company by replacing traditional intelligence methods is an undisputed challenge.
Existing employees will be the primary source of headaches, literally. Although this may open new opportunities, it may be a disruption to employees. The trick here is to clarify and explain the reasons for the sudden shift.
Implement New Ways of Working
In conventional analytics, companies get their insights weekly. But this analytics process is different. Businesses can gather real-time data insights and generate Power BI reports taking a different approach to analysis. For organizations to succeed, their working environment and culture must be relevant with this non-traditional method.
Optimizing Internal Processes
To use real-time data analytics an organization, need to understand the changes and ways to improve the internal processes.
Optimizing processes for requirement gathering, designing the architecture for the solution, choosing appropriate technology stack, and overcoming bottlenecks related to software and hardware maximizes the benefit of real-time data analytics in big data.
Frequently Asked Questions in Real-Time Data Analytics
What is the value of real-time data?
Real-time data gives us enough information needed for business operations, from sales, employee engagement to marketing. Before, we needed to jump from database to database. Today is different. We can collect data in one place.
What can real-time data tell you?
Generally, real-time data provides organizations deeper insights about anything related to the business. It helps level up the quality of service, improves marketing, establishes a highly competitive team, and much more.
Every business dreams of standing apart from the competition. Once integrated into your business operations, it will be easy to make decisions. But the implementation is always challenging. Do not worry. Take your time, and there is no pressure. Also, there are experts to count on and hire.