As we advance deeper into the digitally roaring 2020s, data executives and professionals are seeing change on a scale never seen before in their careers. A new generation of technologies that often build on previous solutions means new ways of working and ensuring performance for today’s increasingly data-driven enterprises. We asked industry leaders for their views on what technology is enhancing enterprises’ ability to compete on data.
Data fabric is frequently mentioned by industry leaders as the technology to watch in 2022. Today, most data remains “unanalyzed, inaccessible, or untrusted” because of a siloed and distributed environment, explained Inderpal Bhandari, global chief data officer for IBM. Bhandari cited research showing that more than two-thirds of businesses use more than 20 different data sources to inform their AI, business integration, and analytics systems, with larger businesses sometimes tapping into as many as 500 data sources.
As a result of these multiple sources and silos, “businesses now need technology that supports an easy way to view and manage everything comprehensively, no matter where the data lives,” said Bhandari. “A data fabric architecture can simplify data strategy. A data fabric is like the connective tissue that removes complexity and helps foster easier data sharing and management while aiding in security and end-to-end governance. When done right, a data fabric architecture leverages AI to remove data complexity by learning patterns in how that data is transformed and utilized. It makes finding important data easier by creating a marketplace for self-service data consumption, and it also automatically enforces governance and compliance rules.”
The rise of data fabric comes out of a transition to just-in-time analytics, versus “data accumulation facility within data platform architectures,” said Igor Ikonnikov, research and advisory director in the data and analytics practice at Info-Tech Research Group. It’s catching on, he noted. “Data fabric is claimed to be supported by many vendors in data management, ETL, data cataloging domains. Proper implementation, based on knowledge graph, is not as widely represented, but already has prominent offerings.”
With data fabric, “you spend less time understanding and preparing the data, and more time using the data,” said Kristian Gravelle, vice president of marketing and digital transformation at Adastra Corp. This is achieved, Gravelle said, “all while reducing the workload on IT teams.”
However, the data fabric needs to build upon many moving parts, she cautioned. “Although there is maturity around data catalog solutions, with connected knowledge graphs, active metadata with AI discovery, there will need to be more advancement in machine learning-driven data integration and automated data orchestration before it can truly shine,” said Gravelle. “The organization truly gains agility through sharing and promoting data across the organization in addition to, discoverability, scalability, quality, security, accessibility, speed to delivery, and data reusability. Rather than digging through multiple systems to fish out what you need, you can quickly access it and spend more time analyzing and making decisions.”
There are a number of emerging “Ops” approaches, all intended to boost the automated delivery of data or software within more collaborative settings. DataOps, however, targets data management directly, promising to enhance delivery to data-hungry organizations. “DataOps has become the most effective way to manage and integrate organizational data,” said Ram Chakravarti, CTO of BMC Software.
DataOps is already in practice in many organizations, and “will continue to be adopted widely by organizations as an enabler of value realization for data and analytics programs,” he added. “It promises to improve the success rate of data and analytics initiatives.” He pointed out, however, that cultural change is required to make DataOps work as intended. “The challenge will be executive buy-in and support, along with collaboration across the ecosystem, which is necessary for what has traditionally been a relationship based on mistrust.”
IoT DATA MANAGEMENT
The Internet of Things as a working connected network of devices and sensors has been in place for a number of years now. The challenge is handling and extracting value from the volumes of data flowing in from endpoints, something enterprises are just beginning to grasp. “Enterprises have been amassing sensor data at an unmanageable rate,” said Paul Scott-Murphy, chief technology officer of WANdisco. “With sensors from billions of devices gathering data and storing it at the edge, it’s been a struggle to move this data to the cloud to support AI, machine learning, and analytics. In verticals like telecommunications, automotive, manufacturing, and healthcare, IoT is helping to bring innovative technologies to market, deliver more attuned services, and enhance daily life.”