Real-Time Data Analytics Predictions for 2021 and Beyond

2021 is poised to be a pivotal year for businesses striving to recover from the unprecedented challenges of the past two years. As the market gradually returns to normal, companies are increasingly turning towards real-time data analytics to automate business processes and enhance efficiency. This approach not only streamlines operations but also paves the way for a more data-driven future.

Convergence of Data Management, Data Science, AI, and Analytics

The convergence of data management, data science, AI, and analytics is set to transform the way businesses operate. This integration will make it easier to implement and automate end-to-end workflows. Additionally, it will foster the need for data literacy, enabling business users to access insights and predictions in real-time as needed, driving the further adoption of AI and machine learning (ML) in daily operations.

Instant Analytics on Data in Motion

Instant analytics on data in motion will be critical for businesses in 2021. While creating data models on historic data remains important, the emphasis will shift towards immediate insights from real-time data. Production lines producing the most power, or which products will sell out in the next 30 minutes – these are issues that real-time analytics on everyday transactional data can address.

With reopening of businesses and evolving customer behavior, there will be a surge in real-time analytics on health status, crowd density, and occupancy of locations and transport modes. Master data management (MDM), data virtualization, and other underlying technologies will play a key role in supporting continuous analysis of both historic and real-time data.

Information Examination and Production Network Optimization

Information examination will play a crucial role in optimizing production networks, providing greater visibility into lead times, stock levels, and coordination. At the beginning of the COVID-19 outbreak in March, we witnessed a classic example of a disrupted supply chain. Demand for specific goods skyrocketed while supply dwindled due to sudden factory closures, leading to customer panic and delays.

By utilizing data to monitor and analyze existing supply chain measures, distribution networks, and transportation arrangements, businesses can identify bottlenecks and opportunities. This proactive approach can address supply chain vulnerabilities before they cause significant issues. Understanding delivery lead times, coordination scenarios, and stock resource levels will drive higher levels of responsiveness, productivity, and effectiveness in a wide range of industries.

Demand for Interoperable Multi-Cloud Platforms

The demand for interoperable multi-cloud platform arrangements will significantly increase. As Software as a Service (SaaS) tools and applications continue to fragment on-premise information and across cloud-based operational workloads, there is a growing need for loosely coupled cloud-based data environments. These systems will enable businesses to quickly assess changing economic situations in a data-driven manner.

Innovative technologies like serverless computing will also shift to the Edge, bringing new levels of insight, information security, and future service offerings. This transition will be one of the legacies of the pandemic, allowing international organizations to respond to and manage the pandemic's impact, whether it be contact tracing, understanding disease rates, or vaccine distribution.

Conclusion

As businesses navigate the recovery journey, real-time data analytics will be a key driver of growth and innovation. By embracing these technologies, companies can not only enhance operational efficiency but also gain a competitive edge in a rapidly evolving market.