Gartner: top 10 data and analytics technology trends for 2019

The story of data and analytics is one that keeps evolving; from appointing chief data officers to procuring the latest analytics software, business leaders are desperately trying to utilise it, but it's not easy. "The size, complexity, distributed nature of data, speed of action and the continuous intelligence required by digital business means that rigid and centralised architectures and tools break down," says Donald Feinberg, vice president and distinguished research analyst at Gartner. "The continued survival of any business will depend upon an agile, data-centric architecture that responds to the constant rate of change. But while business leaders have to tackle digital disruption by looking for the right services and technology to help streamline their data processes, unprecedented opportunities have also arisen. The sheer amount of data, combined with the increase of strong processing capabilities enabled by cloud technologies, means it's now possible to train and execute algorithms at the large scale necessary to finally realise the full potential of AI. According to Gartner, it's critical to gain a deeper understanding of the following top 10 technology trends fuelling that evolving story and prioritise them based on business value to stay ahead. Gartner says by 2020, augmented analytics will be the main selling point for analytics and BI solutions. Using machine learning and AI, augmented analytics is considered, by Gartner, as a disrupter in the data and analytics market because it will transform how analytics content in developed, consumed and shared. Augmented data management utilises machine learning capabilities and AI technology to make data management categories including data quality, master data management, metadata management, data integration as well as database management systems (DBMSs) self-configuring and self-tuning. According to Gartner, this is a big deal because it automates many of the manual tasks opening up opportunities for less technically skilled users to use data. It also helps highly skilled technical resources to focus on more value-adding tasks. Through to the end of 2022, manual tasks in data management will be cut by 45% thanks to ML and automated service-level management. Continues data is more than a new way to say real-time data. Instead, it's about a design pattern where real-time analytics are combined with business operations, processing current and historical data to prescribe actions in response to events. "Continuous intelligence represents a significant change in the job of the data and analytics team," says 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.