There are 2.5 quintillion bytes of created every day, according to this infographic produced by Domo. All of that data must be filtered, parsed, analyzed, and potentially acted upon. It’s a job too big for mere humans, and increasingly enterprises are not going to be turning to artificial intelligence and machine learning for a potential competitive edge, but because there’s no other possible way to extract all of the potential value from their data.
This is one of the big reasons why it’s hard to find an industry not being affected by data analytics and artificial intelligence. Last spring, the research firm Gartner estimated that the enterprise AI market would hit $1.2 trillion, and that all AI-derived business value would reach $3.9 trillion by 2020.
Those business benefits will likely be witnessed both horizontally in industries such as cybersecurity, research and development, IT, customer engagement, and as well as in verticals such as healthcare, manufacturing, software development, retail, and energy exploration.
But how are data analytics, machine learning, and AI being applied within organizations, and what’s techniques and strategies within these disciplines are currently trending?
Gartner analyst Donald Feinberg said that the very challenge 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.
“The size, complexity, distributed nature of data, speed of action and the continuous intelligence required by digital business means that rigid and centralized architectures and tools break down,” says Feinberg . “The continued survival of any business will depend upon an agile, data-centric architecture that responds to the constant rate of change,” he said
The biggest trend next year will be that of augmented analytics, or using machine learning to “augment” humans and help to automate the complex task of building an analytics model and workflow.
“By 2020, augmented analytics will be a dominant driver of new purchases of analytics and BI, as well as data science and ML platforms, and of embedded analytics. Data and analytics leaders should plan to adopt augmented analytics as platform capabilities mature,” Gartner predicted.
Not surprisingly, Gartner also predicts in the next year enterprise information management practices such as data quality, metadata management, master data management, data integration, and database management systems (DBMSs) will leverage artificial intelligence and machine learning to become more self-configuring and self-tuning.
Gartner also predicts the data will be used for continuous decision-making and continuous intelligence, that there will be increased pressure to make the AI decisions on which enterprises rely explainable.
There’s more, of course, including the increased use of graph analytics, data fabrics, conversational analytics, the triumph of commercial artificial intelligence and machine learning over open source, blockchain, and persistent memory servers. You can access Gartner’s Top 10 Data and Analytics Technology Trends for 2019 here.
