Data science and AI predictions for 2019
Data science became the highest-paid IT profession in 2018, and the field is set for further growth in 2019 as the tools and techniques become more accessible and AI moves from hype to practical use cases.
A recent Deloitte survey estimated 57 percent of businesses are increasing spending in AI as organizations start to wake up to the potential business benefits.
“We are just at the beginning of the enterprise machine learning transformation. In 2019, we’ll see a new step in maturity, as companies advance from PoCs to production capabilities,” says Stephen Line, VP of EMEA at Cloudera.
“Enterprise machine learning adoption will continue as businesses look to automate pattern detection, prediction and decision making to drive transformational efficiency improvement, competitive differentiation and growth. As early adopters advance from proof-of-concepts to production deployment of multiple use-cases, we’ll continue to see an emergence of technologies and best practices aimed at helping operationalize, scale and ultimately industrialize these capabilities to achieve full transformational value,” he predicts.
Forrester principal analyst Michele Goetz believes that these developments will help use cases shift from unblocking bottlenecks in a process to uncovering new ways to execute the process.
“The AI capabilities that come pre-trained will still be popular, but they will become more embedded in broader solutions,” she says. “I can see acquisitions gaining steam. As firms become more adept at using AI to reengineer rather than tuning and automating tedious tasks, the value of AI will start to outshine existing analytic approaches that focus on narrow tasks and scoring.”
Forrester analyst Duncan Jones expects more vendors to embed AI in their software, reducing the need for IT departments to build it into their own tools.
Jones also believes that automation will shift the focus of business intelligence software “from drill down to alert up”, using automated checks to alert people about what warrants their attention.
“You end up managing more by exception than by rubber-stamping everything. That’s the prediction with business software,” he says. “The processes will become very much less manual. Everything will be stripped out and automated so that the human beings will be looking at stuff that they really need to look at, and the software will be dealing with everything else.”
The growing capabilities of data science could also lead to some rapid developments in emerging technologies. Gartner fellow David Cearley believes that the growth of autonomous things such as robots, drones, and vehicles to deliver advanced behaviors that interact more naturally with their surroundings and with people.
“As autonomous things proliferate, we expect a shift from stand-alone intelligent things to a swarm of collaborative intelligent things, with multiple devices working together, either independently of people or with human input,” he says.
“For example, if a drone examined a large field and found that it was ready for harvesting, it could dispatch an ‘autonomous harvester.’ Or in the delivery market, the most effective solution may be to use an autonomous vehicle to move packages to the target area. Robots and drones on board the vehicle could then ensure final delivery of the package.”
Forrester analyst Goetz also expects a growth in conversational experiences as natural language processing becomes more sophisticated.
“Virtual agents will come with more job expertise and the ability to engage across a broader set of conversational dimensions in a single engagement,” she says.
Exasol CTO Mathias Golombek shares the sentiment. “Amazon Echo, Google Home, and Apple Home pods have brought connected assistants to the home. For the first time, voice interaction has become a mainstream method of controlling devices to play music, get basic information, and administer smart home devices.
