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Gartner wrapped up the Data & Analytics Summit Americas 2021 virtual event this week with a lively overview of top trends for enterprises to watch. Overall, Gartner analysts saw pressing trends around accelerating change, operationalizing business value, and — increasingly — “distributed everything.”
Accelerating change these days means feeding and scaling AI, and composable data and analytics are key, Gartner analyst Donald Feinberg said.
This is about making it easy to assemble AI from across many different tools for BI, data management, and predictive analytics. This trend will allow companies to use microservices and containerization to bring together the pieces necessary to create a service, Feinberg said.
“This is a great way to pursue experiments because you can pick and choose how it works together,” Feinberg added.
Composable data and analytics initiatives might uncover new ways of packaging data as part of a service or product. These could be built using low-code and no-code tools that are sold via the cloud or new kinds of data service intermediaries.
Providing the foundation for composable data and analytics is the data fabric that allows easy access and sharing across distributed data environments.
“You should not have to worry about where it is and how to access it,” Feinberg said of composable data. It is not a single tool but rather the set of tools put together into a solution. Metadata powered by a graph database is the glue that holds this together. It is not easy to do, but the technology is getting better.
Big data becomes small and wide data
There is a growing need to weave a wider variety of data into applications to improve situational awareness and decision-making.
COVID-19 caused a lot of historical data to become obsolete. There are also many small data use cases where there is just less data to work with. This trend requires investigating technologies like federated learning, few-shot learning, and content analytics that can organize new types of data such as voice, text, and video.
There is more on the accelerated road to digital transformation and responsible, scalable AI.
Teams now need to pay attention to new privacy and AI models, Gartner analyst Rita Sallam said.
Trust is growing in importance, owing to regulations like GDPR in Europe and CCPA in California, along with new AI regulations being proposed in Europe.
“We see that many organizations are struggling with scaling AI prototypes and pilots into production, and the effort to integrate AI into production is underestimated,” Sallam said.
Operationalizing business value
Gartner said business-facing data initiatives were key drivers of digital transformation in the enterprise. Research showed that 72% of data and analytics leaders are leading, or are heavily involved, in their organizations’ digital transformation efforts. These data leaders now confront emerging trends on various fronts.
XOps: The evolution of DataOps to support AI and machine learning workflows is now XOps. The X could also stand for MLOps, ModelOps, and even FinOps. This promises to bring flexibility and agility in coordinating the infrastructure, data sources, and business needs in new ways.
Engineering decision intelligence: Decision support is not new, but now decision-making is more complex. Engineering decision intelligence frames a wide range of techniques, from conventional analytics to AI to align and tune decision models and make them more repeatable, understandable, and traceable.
Data and analytics as the core business function: With the chaos of the pandemic and other disruptors, data and analytics are becoming more central to an organization’s success. Companies will have to prioritize data and analytics as core functions rather than as secondary activity handled by IT. This will also drive data literacy efforts and new organizational models that distribute analytics functions across more teams.
Everything is distributed, and graph relates everything: Graph databases have been around for a while but struggled due to limited tools, data sources, and workflows. But the technology is seeing major growth due to graph data improvements in popular BI and analytics tools. There are a wide variety of graph techniques for representing knowledge, relationships, properties, social networks, business rules, and metadata. Gartner predicts that graph technologies will underpin 80% of data analytics innovations by 2025.
Data and analytics at the edge: The internet of things (IoT) allows enterprises to work with data at the edge. What’s new is the different ways enterprises are also embedding analytics, AI, and decision intelligence into edge applications. Use cases include providing better predictive maintenance for factories, delivering new insights to oil rigs, and enabling better mobile apps. The edge improves speed and resiliency because there’s no need for constant cloud connectivity. However, handling analytics at the edge complicates governance, so enterprises need to find tools that help with governance and analytics at the edge, Feinberg said.
Rise of the augmented consumer: Gartner is focusing on business consumers and the importance of making analytics exploration easier and richer, such as with the shift from predesigned dashboards to new, more automated and dynamic presentation and delivery of analytics. This will shift the analytics superpower to the augmented consumer, Sallam said. Expect to see significant growth in vendors that deliver more conversational and interactive analytics experiences across new channels, such as voice, mobile, and web applications, she said.
Gartner’s presenters advised enterprises to keep in mind that these are all technologies and practices companies can pick up and apply today using commercial software.
It’s useful to consider the entire collection of trends in an integrated manner and then prioritize the one worth researching for your own business domain, with an eye toward how it may work for others, the analysts said.
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