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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 per cent of businesses are increasing spending in AI as organisations start to wake up to the potential business benefits.
"We are just at the beginning of the enterprise machine learning transformation," says Stephen Line, VP of EMEA at Cloudera. "In 2019, we'll see a new step in maturity, as companies advance from PoCs to production capabilities.
"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 operationalise, scale and ultimately industrialise these capabilities to achieve full transformational value."
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.”
New applications
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 behaviours 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. However, these devices haven’t made much of an impact in business," he says.
"My expectation is that in 2019, they will find their voices in niche business scenarios too, and that connected assistants will be interfacing to email, CRM systems, and diaries, to streamline processes and give a helping hand."
New dangers
Reuven Harrison, CTO at security policy company Tufin, believes this growth in voice applications will lead to an increasing number of data breaches of audio.
"These attacks will manipulate people into inadvertently giving voice commands or playing audio on their computer, prompting a sequence of events that leads to information on company performance or to further gather network information to ease an attack," he says.
New methods of machine deception will further threaten consumer trust, as the emergence of deepfake video manipulation technique has demonstrated.
"At least for now, detection and forensic technologies have been able to ferret out fake video and images. But the tools for generating fake content are improving quickly so we must ensure that detection technologies are able to keep pace," says Ben Lorica, Chief Data Scientist at O’Reilly Media.
"Machine deception does not just refer to machines deceiving humans, however. It also refers to machines deceiving machines – bots - and people deceiving machines - troll armies and click farms.
"Information propagation methods and click farms will continue to be used to fool ranking systems on content and retail platforms, and methods to detect and combat this will have to be developed as fast as new forms of machine deception are launched."
GDPR and building trust
The introduction of GDPR could make any such breaches extremely costly. The ICO is yet to issue a big fine for a GDPR breach, but David Francis, head of security at IT services provider KCOM, believes 2019 will be the year it happens for the first time.
"If 2018 was the year of compliance, 2019 will be the year of retribution for everyone's favourite data privacy regulation," he says. "The period of grace is drawing to a close, and the new year will see the ICO taking its first high-profile scalp over treatment of personally identifiable information.
"That will set the precedent by which all further cases are judged – letting companies know along the way just how strictly enforced the rules are going to be, and how heavy the fines. Now is the time to check your compliance levels – don’t wait for the hammer to fall," Francis said.
Despite the risks, companies are struggling to implement safe and effective data protection practices. A recent KPMG survey revealed that 61 per cent of CEOs view building trust as a top three priority for their organisation, but just 35 per cent of IT decision-makers have a high level of trust in their organisation's analytics.
Forrester analyst Goetz believes explainable AI will need to evolve to build trust.
"Much of the emphasis on ML training has focused on the algorithm. However, the number one issue with AI is trusting the data," she says.
"This isn't just a data science issue; it's also a business issue. First, business experts need better ways to instruct data scientists what representative data should go into the model.
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