Globalization and the increasing interdependence of economies around the world have led to a significant rise in the influence and reliance on global information for enterprises. The modern business landscape is characterized by an abundance of data, which poses complex challenges for organizations that need to process large volumes of information at incredible speeds. To overcome these data-related hurdles, companies must identify and implement innovative technology solutions.
Today, enterprises rely on vast amounts of data from various sources within their supply chains. From traditional monolithic applications to big data warehouses and operational data stores, the scope of data management has evolved to connect producers, suppliers, and consumers on a global scale.
In addition to the expanding scope, companies face the daunting task of managing enormous data volumes. They must not only handle real-time data from operational technologies like sensors, devices, news feeds, and social media but also integrate historical data into their analytics for immediate use cases. Such cases include credit card fraud prevention, financial portfolio risk analysis, and energy grid operations management.
To prevent credit card fraud while maintaining a seamless transaction experience, credit card companies rely on real-time processing of incoming transaction requests. Asset portfolio managers seeking an advantage in pricing assets analyze micro- and macro-economic situations, market conditions, and numerous variables in milliseconds. Energy grid operators continuously monitor the health of the grid with hundreds of thousands of sensors, necessitating real-time processing of massive event volumes to facilitate timely decision-making.
These use cases demonstrate the need for multi-sourced data, various data life cycle stages, and diverse data processing strategies. For instance, credit card fraud prevention involves streaming data processing, intelligent analysis, historical data utilization, intelligent model execution, and low latency measures. While the specific processing requirements may vary between use cases, all of them demand multidimensional processing and ultra-low latency for effective solutions.
Overcoming the challenges posed by multidimensional data processing and speed requirements is crucial. Data that flows from multiple sources often passes through silos and encounters latency during network transfers between storage and processing technologies. While data hubs provide comprehensive data repositories, executing queries against these hubs and transferring large result sets over the network can add substantial latency.
Several vendors recognize these challenges and are striving to address them by combining key capabilities. These include the ability to process data in motion and data at rest, virtualization of data from multiple sources with persistent storage and data integrity, security, durability, and availability, as well as the execution of advanced artificial intelligence/machine learning (AI/ML) algorithms or complex computations on large datasets at high speeds.
The information relevant to today's data-driven enterprises extends beyond their organizational boundaries and firewalls. The complexity of data, coupled with its scale and the need for speedy processing, necessitates a unified approach. A unified data platform can combine the processing of data in motion with historical data at rest, enabling organizations to execute complex analytical workloads at ultra-low latencies. In our next article, we will delve deeper into the essential elements of a unified data platform.
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