Real-Time Big Data Analytics

Real-Time Big Data Analytics: Market Stats and Importance for Business Performance

The global big data and analytics market is expected to reach $924 billion by 2032, growing at a CAGR of 13% during the forecasted period. The major market drivers include the need for real-time customer management insights and the increasing adoption of machine learning and AI. The industries that are expected to gain maximum revenue share from implementing the technology include BFSI, healthcare, automotive, telecoms, media, and retail.

According to the Harvard Business Review survey on enterprise data and analytics strategies, 76% of organizations say that real-time data analytics is important for business performance. 80% of leaders (organizations that have mature data analytics processes) state that the importance of real-time data analytics is increasing. The survey features responses from 336 organizations across multiple industries, including financial services, healthcare, technology, and manufacturing.

High-Level Architecture of a Real-Time Big Data Analytics Solution

Real-time big data analytics is a way to ensure instant responses to high-velocity, multi-source data. Below, ScienceSoft's data engineers outline the description of key architecture blocks and data flows for a real-time big data analytics solution.

The data sources for a real-time big data analytics solution may include web and mobile user apps, IoT devices (e.g., sensors, wearables, actuators), and external systems (e.g., stock markets, social media platforms, weather information systems).

In most cases, a solution for real-time big data analytics would feature two layers for real-time (stream) and batch big data processing.

The analytics data storage (a data warehouse (DWH) or a big data database) stores unified views of the data generated by stream and batch processing blocks in a highly structured format relevant to the chosen data model. The insights are served to BI software and back-office systems. Business users can also perform analysis and exploration of the data in the DWH via ad hoc queries.

The machine learning or artificial intelligence (ML/AI) engine is an optional block that enables advanced real-time analytics (e.g., dynamic pricing in ecommerce, predictive maintenance in manufacturing, fraud detection in finance). The ML training module continuously improves the AI engine's accuracy based on historical data.

The data orchestration and governance system automates data cleansing, transformation, and other recurrent data processing actions. It also ensures quality, security, and regulatory compliance of data throughout its lifecycle.

ScienceSoft's Expertise to Drive Your Big Data Analytics Initiative

• Since 1989 in data analytics and AI.
• Since 2003 in big data consulting and end-to-end big data implementation.
• A team of business analysts, solution architects, and data engineers with 7–20 years of experience.
• Practical experience in 30+ domains, including healthcare, banking, lending, investment, insurance, retail, ecommerce, manufacturing, logistics, energy, telecoms, and more.
• In-house compliance experts proficient in GDPR, HIPAA, PCI DSS, SOC 1, SOC 2, and other global and local regulations.
• Partnerships with AWS, Microsoft, and Oracle.
• ISO 9001 and ISO 27001-certified to guarantee top service quality and complete security of our customers' data.
• Established practices for scope estimation, risk mitigation, change request management, and other project management aspects.

Fueling Financial Growth: The Game-Changing Benefit of Real-Time Big Data Analytics

For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65M additional net income.

Richard Joyce, Senior Analyst at Forrester.