WhatsTheBigData

‘Tis the season for the public relations exercise known as “here’s what we think (or hope) will happen in the tech sector next year,” flooding my inbox with predictions for 2017. No one knows what will happen tomorrow, let alone over the next 12 months, but the exercise yields interesting insights into what’s hot (and what’s not) in technology today. Artificial intelligence (and machine/deep learning) is the hottest trend, eclipsing, but building on, the accumulated hype for the previous “new big thing,” big data. The new catalyst for the data explosion is the Internet of Things, bringing with it new cybersecurity vulnerabilities. The rapid fluctuations in the relative temperature of these trends also create new dislocations and opportunities in the tech job market.

The hottest segment of the hottest trend—artificial intelligence—is the market for chatbots. “The movement towards conversational interfaces will accelerate,” says Stuart Frankel, CEO, Narrative Science. “The recent, combined efforts of a number of innovative tech giants point to a coming year when interacting with technology through conversation becomes the norm. Are conversational interfaces really a big deal? They’re game-changing. Since the advent of computers, we have been forced to speak the language of computers in order to communicate with them and now we’re teaching them to communicate in our language.”

Frankel continues:

Search engines like Google and Bing have already made big moves enabling search queries via spoken word while Facebook launched an AI-effort, DeepText, to understand individual users’ conversational patterns and interests. Meanwhile, the move toward natural language interfaces has already picked up steam with the explosion of companies focused on enabling chatbots, digital assistants and even messaging apps eclipsing social networks in monthly activity. Beyond 2017, think of a future when we can casually ask our personal devices for information regardless of subject—“How much money do I have in checking?”, “When was my last physical?” or “What restaurant within a 10-minute driving distance has an open table for 2 people?”

Aman Naimat, SVP of Technology, Demandbase, looks at the supply-side of the new consumer interface. “Marketers will start having hyper-personalized conversations at scale using AI,” he predicts, adding: “The most interesting and valuable use for AI is the ability for marketers to have one-on-one personalized conversations with buyers who know their pain points, goals and ambitions. This type of personalized communication eliminates the worthless spam that often plagues marketing today. These personalized conversations are already happening between strategic account managers, but in 2017 artificial intelligence will allow these conversations to grow beyond a select group of people. Instead, each of a company’s 10 million website visitors can expect to have a unique conversation with a brand based on their specific needs. From dynamic ad copy, to 1-to-1 emails and customized website experiences, AI will make hyper-personalization at scale possible.”

The recent success of deep learning in tasks such as image recognition and machine translation has served as a catalyst for investments in and experimentation with AI and Bill Franks, Chief Analytics Officer, Teradata, predicts that “Deep learning will move out of the hype zone and into reality.” Says Franks, sounding a note of caution: “Deep learning is getting massive buzz recently. Unfortunately, many people are once again making the mistake of thinking that deep learning is a magic, cure-all bullet for all things analytics. The fact is that deep learning is amazingly powerful for some areas such as image recognition. However, that doesn’t mean it can apply everywhere. While deep learning will be in place at a large number of companies in the coming year, the market will start to recognize where it really makes sense and where it does not. By better defining where deep learning plays, it will increase focus on the right areas and speed the delivery of value.”

The value is in the data. Artificial intelligence, especially deep learning, needs big data to show its value and it is also a new source of data generation. Alan O’Herlihy, CEO, Everseen, predicts that “AI will inform, not just perform, across industries.” O’Herlihy: “Take, for example, the retail industry, which has suffered from its inability to detect non-scanned items at checkout—which are responsible for 30% of retailers’ annual loss—until they discover their loss in inventory well after the fact. AI is stepping in to address issues of this caliber across industries, and as a result, it’s often gathering just as much data as it’s processing. This resulting data is becoming a secondary benefit to businesses that use AI.”

Similarly, Quentin Gallivan, CEO, Pentaho, predicts that “the early adopters of AI and machine learning in analytics will gain a huge first-mover advantage in the digitalization of business.” Gallivan: “Early adopters will gain a jump start on the market in 2017 because they know that the sooner these systems begin learning about the contexts in which they operate, the sooner they will get to work mining data to make increasingly accurate predictions. This is just as true for the online retailer wanting to offer better recommendations to customers, a self-driving car manufacturer or an airport seeking to prevent the next terrorist attack.”

“Just as most companies evolved to include cloud capabilities and features, 2017 will bring machine learning to almost every aspect of IT,” predicts Ash Ashutosh, founder and CEO, Actifio. He sums up the connection between big data and AI: “As we move beyond the era of simply capturing big data, machine learning will usher in a new era of data understanding and analysis.”

No doubt responding to dire predictions from the likes of Stephen Hawking, Peter Isaacson, CMO, Demandbase, predicts that “Artificial intelligence will destroy the world but not before it really helps B2B marketers.” Isaacson: “AI will allow B2B marketers to tap into more data and understand the entire business network of a company from customers, partners, suppliers and more. This complete 360-degree view will allow marketers to better predict potential buyers, personalize campaigns and close more deals, further extending the value of marketing in the C-suite.”

Data is key to the success of artificial intelligence, which is why the quality of the data matters so much.

“In 2017 foundational data quality will be a prerequisite to quality AI predictions,” predicts Darian Shirazi, co-founder and CEO, Radius. Shirazi: “We will see more companies focus on solving the challenge of maintaining accurate, valuable data, so that AI technology lives up to its promise of driving change and improvement for businesses.”

The (continuing) success of big data solutions, depends in turn, on fast adaptation to the new realities of the cloud, and with it, the rise to prominence of self-service big data. Dave Mariani, CEO, AtScale, predicts that “HDFS as a file system will give way to object storage” and as a result, “the public cloud providers will compete on having the fastest, most cost-effective object storage technologies.” “The existing on-premise Hadoop distros (Cloudera, Hortonworks, MapR),” adds Mariani, “will be at a disadvantage compared to the cloud based ‘Hadoop-as-a-service’ providers like Amazon EMR, Google Dataproc and Azure HD Insight.”