Of the Gartner Data & Analytics Summit in Sydney, Donald Feinberg, vice president and distinguished analyst at Gartner, said the disruption created by digital disruption — too much data — has also created an unprecedented opportunity. The vast amount of data, together with increasingly powerful processing capabilities enabled by the cloud, means it is now possible to train and execute algorithms at the large scale necessary to finally realize the full potential of AI.
Gartner identifies top 10 data and analytics technology trends for 2019 as listed below:
Trend No. 1: Augmented Analytics
Augmented analytics is considered, by Gartner, will be the next wave of disruption in the data and analytics market because it will use machine learning (ML) and AI techniques to transform how analytics content is developed, consumed and shared.
Gartner says by 2020, augmented analytics will be the main selling point for analytics and BI solutions. Data and analytics leaders should plan to adopt augmented analytics as platform capabilities mature.
Trend No. 2: Augmented Data Management
Augmented data management leverages ML capabilities and AI technology to make data management categories including data quality, metadata management, master data management, data integration as well as DBMSs (database management systems) self-configuring and self-tuning.
It automates many of the manual tasks and allows less technically skilled users to be more autonomous using data. It also helps highly skilled technical resources be able to focus on more valuable tasks. According to Gartner, manual tasks in data management will be reduced by 45% because of ML and automated service-level management through to the end of 2022.
Trend No. 3: Continuous Intelligence
Continuous intelligence leverages multiple technologies such as augmented analytics, event stream processing, optimization, business rule management and ML. It is a design pattern where real-time analytics are combined with business operations, processing current and historical data to prescribe actions in response to events. It provides decision automation or decision support.
“Continuous intelligence represents a major change in the job of the data and analytics team,” said Rita Sallam, research vice president at Gartner. “It’s a grand challenge — and a grand opportunity — for analytics and BI (business intelligence) teams to help businesses make smarter real-time decisions in 2019. It could be seen as the ultimate in operational BI.”
By 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.
Trend No. 4: Explainable AI
AI models are increasingly used in data management and replace human decision making. However, most of AI models are not able to explain why they reached a specific recommendation or a decision. This is where explainable AI comes in.
Explainable AI in data science and ML platforms is about generating an explanation of data models in terms of accuracy, attributes, model statistics and features in natural language.
Trend No. 5: Graph
According to Gartner, graph analytics is a set of analytic techniques that allows for the exploration of relationships between entities of interest such as organizations, people and transactions. The application of graph processing and graph DBMSs will grow at 100% annually through 2022.
Trend No. 6: Data Fabric
Data fabric enables a single and consistent data management framework, which allows seamless data access and processing by design across otherwise siloed storage.
Through 2022, bespoke data fabric configurations will be deployed primarily as a static infrastructure, forcing organizations into a new wave of cost to completely re-design for more dynamic data mesh approaches.
Trend No. 7: NLP/ Conversational Analytics
Gartner predicts 50% of analytical queries will be generated via search, natural language processing (NLP) or voice, or will be automatically generated by 2020. The need to analyze complex combinations of data and to make analytics accessible to everyone in the organization will drive broader adoption, allowing analytics tools to be as easy as a search interface or a conversation with a virtual assistant.
Trend No. 8: Commercial AI and ML
Gartner predicts that 75% of new end-user solutions leveraging AI & ML techniques will be built with commercial solutions rather than open source platforms by 2020.
Commercial vendors have built connectors into the Open Source ecosystem and they provide enterprises with features necessary to scale and democratize AI and ML, such as project and model management, data lineage, reuse, transparency, and platform cohesiveness and integration that Open Source technologies lack.
Trend No. 9: Blockchain
The core value proposition of distributed ledger technologies, such as blockchain, is providing decentralized trust across a network of untrusted participants. The ramifications for analytics use cases are significant, especially those leveraging participant relationships and interactions.
But, according to Gartner, it will be several years before four or five major blockchain technologies become dominant. Until that happens, enterprises will instead partly integrate with blockchain technologies and standards which will likely be dictated by their dominant customers or networks. This includes integration with your existing data and analytics infrastructure. The costs of integration may outweigh any potential benefit.
Blockchains are a data source, not a database, and will not replace existing data management technologies.
Trend No. 10: Persistent Memory Servers
New persistent-memory technologies will help reduce costs and complexity of adopting in-memory computing (IMC)-enabled architectures. Persistent memory represents a new memory tier between DRAM and NAND flash memory that can provide cost-effective mass memory for high-performance workloads.
It has the potential to improve application performance, availability, boot times, clustering methods and security practices, while keeping costs under control. It will also help organizations reduce the complexity of their application and data architectures by decreasing the need for data duplication.
According to Gartner, augmented analytics, continuous intelligence and explainable artificial intelligence (AI) are among the top trends in data and analytics that have significant disruptive potential over the next three to five years. Gartner recommends that data and analytics leaders talk with senior business C suite about their critical priorities and explore how these trends can enable them to the success.
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