Gartner: Ten Trends in Data and Analytics Technology in 2019
At the Gartner Data and Analysis Summit, Gartner Research Vice President Rita Sallam said data and analytics leaders must analyze the potential impact of these trends on the business and adjust their business models and operations accordingly, otherwise there is a potential for losing competitive advantage.
“Data and analytics are evolving, from supporting internal decisions to sustaining intelligence, information products, and appointing CIOs. Deep understanding of technology trends is driving this ever-changing trend and prioritizing them based on business value. These are all crucial."
According to Donald Feinberg, vice president and distinguished analyst at Gartner, the challenge of digital disruption – with too much data – has created unprecedented opportunities. The sheer volume of data and the increasingly powerful processing power realized by the cloud means that we can now train and execute the necessary algorithms on a large scale to ultimately realize the full potential of artificial intelligence.
Feinberg said: "The size, complexity and distributed nature of data, as well as the speed of action required for digital business and continuous intelligence, mean breaking rigid, centralized architecture and tool constraints. Any company's continued survival will depend on it. A flexible, data-centric architecture that responds to changing speeds."
Gartner recommends that data and analytics leaders discuss their key business priorities with senior business leaders and explore how the following key trends can achieve these priorities:
Trend 1: Enhanced Analysis
Enhanced analysis is the next wave of disruptive trends in the data and analytics market. Enhanced analysis uses machine learning and artificial intelligence techniques to transform the way in which content is developed, consumed, and shared.
By 2020, enhanced analysis will be the primary driver for analytics and new acquisitions for BI, data science and machine learning platforms, and embedded analytics. Data and analytics leaders plan to use enhanced analytics as the platform matures.
Trend 2: Enhanced Data Management
Enhanced Data Management uses machine learning and artificial intelligence engines to classify enterprise information management categories, including data quality, metadata management, master data management, data integration, database management system (DBMS) self-configuration, and self-tuning. Enhanced data management automates many manual tasks and allows those with lower skill levels to use data more autonomously, while also allowing high-skilled technology resources to focus on higher-value tasks.
Enhanced data management transforms metadata into auditing, lineage, and reporting, as well as powering dynamic systems. Metadata has changed from passive to active, becoming the main driver of all artificial intelligence/machine learning.
By the end of 2022, the number of manual tasks for data management will be reduced by 45% by increasing machine learning and automated service level management.
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