Introduction:
Gartner wrapped up the Data and Analytics Summit Americas 2021 virtual event this week. It was all about a lively overview of top trends for enterprises to explore. Overall analysts see processing trends around accelerating change, operationalizing business value, and “distributed everything”. Accelerating change these days means seed and scale AI. The composable data and analytics are key, says Gartner analyst Donald Feinberg. It is about making it easy to assemble AI using various different tools for data management and predictive analytics.
The trend will have companies use microservices and containerization to piece together. This will build a service according to the requirements, says Feinberg.
Statement from Feinberg:
“This is a great way to pursue experiments because you can pick and choose how it works together,” Feinberg adds. Composable data and analytics initiatives Mira will new ways of data packaging as a part of a service or product. This could be created using low code and no-code tools. These tools are available via the cloud or new kinds of data service intermediaries.
The foundation for composable data and Analytics is the data fabric. It enables 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 only one tool but a set of tools put together to form a solution. Meta-data powered by graph databases holds this together.
Big data turns into small and wide data:
There is a pressing requirement to bring together a wide variety of data. This will improve situational awareness and decision-making. Covid-19 has turned a lot of historical data obsolete. There are many small data use cases where there are fewer data to work with. This requires investigating technologies that include federated learning, few-shot learning, and content analytics. These things can organize fresh data like voice text and video.
There is more on the accelerated road for digital transformation and responsible, scalable AI. Teams have to pay attention to privacy and AI models, says Gartner analyst Rita Sallam. Trust is becoming increasingly important. Also, regulations like GDPR in Europe and CCPA in California propose new AI regulations.
“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 includes.
Operationalizing the business value:
Gartner says that business-facing data initiatives are key drivers of digital transformation. Research shows that 72% of data and analytics leaders are heavily involved in digital transformation.
XOps: The DataOps evolved into XOps to support AI and machine learning. The X represents MLOps, ModelOps, and even FinOps.
Engineering decision intelligence: Decision support is not new, but decision-making is complex. Engineering decision intelligence brings a wide range of techniques. This includes conventional Analytics to AI that aligns and tunes decision models. It makes them repeatable, understandable, and traceable.
Data and analytics as core business functions:
Data and analytics have become central to the organization’s success. But the pandemic has brought about a lot of chaos. Companies have to prioritize data and analytics as core functions. This is compared to secondary activity handled by IT. This will drive data literacy efforts and new organizational models. It will distribute analytics functions across various teams.
Everything is distributed, and the graph relates everything:
There are a huge variety of graph techniques to represent knowledge, relationships, social networks, business rules, and metadata. Gartner anticipates that graph technologies will underpin 80% of Data Analytics innovation by 2025.
Data and analytics at the edge:
The IoT enables enterprises to work with data at the edge. Use cases include better predictive maintenance, new insights, and enhanced mobile apps. The edge improves speed and resiliency since there is no need for constant cloud connectivity. But handling analytics at the edge complicates governance. Therefore enterprises need tools that help with governments and analytics at the edge, adds Feinberg.
Rise of the augmented consumer:
Gartner focuses on business consumers and the importance of making analytics exploration easier and richer. This includes a shift from pre-designed dashboards to automated and dynamic presentations. In addition to this it will shift analytics superpower to augmented consumers, says Sallam. There is an expectation to have significant growth in vendors that deliver more conversational and interactive analysis across new channels. This will include voice, mobile, and web applications.
