[Research paper] The business impact of edge computing and IIoT on data management | SPONSORED
DataOps methodology – with the goal of delivering more agile and automated approaches to data management – seeks to enable organizations to control and leverage data ranging from edge to core to cloud. DataOps is not just theoretical in its objectives: improving business agility and automating business processes rank high in terms of responses when organizations are asked about the benefits of being more data-driven.
Challenges remain, though. Today, top barriers to being more data-driven include data privacy concerns and difficulty in integrating existing/legacy architecture, suggesting that organizations are struggling to gain cohesive control and governance of data sources and locations. In industrial verticals, this can further be complicated by devices and equipment that generate data and are difficult, expensive or impractical to replace or update.
The growth in IoT data collection and processing underscores the need for comprehensive data management strategies. According to 451 Research’s Voice of the Enterprise: Internet of Things, Workloads & Key Projects quarterly survey, the average enterprise today has deployed – and collects data from – nearly 4,000 IoT endpoints. And these organizations expect a 65% increase in the number of connected IoT endpoints over the next two years.
Meanwhile, critical processes are increasingly being executed at the network edge. Data analysis, especially, has seen growth. For organizations executing processes at the edge in late 2019, 62% reported conducting data analysis – the most popular edge process. With this increase comes the compounded need for faster ingestion and faster response times from local operators.
Download the complimentary research by 451 Research, part of S&P Global Market Intelligence, sponsored by Hitachi Vantara, to assess the business impact of edge computing and IIoT on data management.