‘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.”
Ashish Thusoo, CEO,
Qubole, also sees how “big data and the cloud will go hand-in-hand.” Says Thusoo: “As big data moves from an experiment to an organization-wide endeavor, the cost, time and resources needed to manage a massive data center don’t make sense. As a result, more and more companies will look to the cloud to help with the costs of data management.
In 2017, expect enterprises to move their big data projects to the cloud in droves.” In addition,
we’ll see more organizations opt for a self-service data model so that anyone in the company can easily pull data to uncover new insights to make business decisions.”
In general, says Ihab Ilyas, co-founder of
Tamr and Professor of Computer Science at the University of Waterloo, “
Data Analytics will go vertical, and companies that build vertical solutions will dominate the market. General-purpose data analytics companies will start disappearing. Vertical data analytics startups will develop their own full-stack solutions to data collection, preparation and analytics.”
The expansion of the market for big data solutions will be driven, in part, by the continuing adoption of the Internet of Things (IoT). Or maybe not. “
The Internet of Things (IoT) is still a popular buzzword, but adoption will continue to be slow,” predicts Prat Moghe, founder and CEO,
Cazena. “Analyzing data from IoT and sensors clearly has the potential for massive impact, but most companies are far (FAR!) from ready. IoT will continue to get lots of lip service, but actual deployments will remain low. Complexity will continue to plague early adopters that find it a major challenge to integrate that many moving parts. Companies will instead focus resources on other low-hanging fruit data and analytics projects first.”
Whether fast or slow, IoT adoption adds new security vulnerabilities to an already very busy cybersecurity scene. Here are a few predictions regarding the new IoT-related threats:
“IoT will shut down the internet and bring about new committees to focus on hardening it,” predicts Rob Juncker, VP of Engineering, LANDESK. “We saw it in the DYN DDoS in 2016… We now know how to shut-down the internet and more than likely, the whole DYN attack was nothing more than a decoy for an attack that will dominate the news for 2017. We’re going to re-evaluate the role of key protocols like DNS and come up with resilient ways to pave the passageways of the internet and plumb their pathways.”
“Securing critical infrastructures against cyber attacks will be prioritized as President-Elect Trump declared it a top goal for his first 100 days,” predicts Michael Shalyt, VP Product, Aperio Systems. “But regulators, politicians and SCADA operators will be challenged to implement meaningful changes due to the expense and difficult nature of securing systems that are antiquated from a cybersecurity perspective and cannot be taken offline. Political pressure will result in some security upgrades, such as legislative adoption of the NIST Cyber Security Framework that has been bandied about for several years, but many gaping holes will remain in 2017.”
“In 2017, we will see the first ransomware for IoT devices,” predicts Chad Bacher, Senior VP, Product Strategy & Technology Alliances, Webroot. “Ransomware will continue to proliferate and become more destructive. Ransomware may take on a different role as well where criminals will do research into individuals and threaten to reveal secret personal information along with their digital assets unless people pay up.”
The proliferation of data, new technologies to mine and analyze it, and new ways to profit from it legally and illegally, drives job market dislocations, and the rise of new organizational roles and occupations, including non-human ones.
Tomer Naveh, CTO,
Adgorithms, predicts
the rise of “’AI Supervisors.’” Says Naveh: “In 2017, we’ll see the beginning of the rise of ‘AI supervisors’ in the workforce. We already subscribe to the fact that many of the labor-intensive, manual tasks we do in our everyday life and work will eventually be automated by machines. In order to make this transition, however, there is a learning process involved. AI systems will get better at communicating their decisions and reasoning to their operators, and those operators will respond with new rules, business logic, and feedback that make it more and more useful in practice over time. As a result we will see people shifting from doing tasks by themselves, to supervising AI software on how to do it for them.”
“
Artificial Intelligence will create new marketing categories, like the B2B business concierge,” predicts Chris Golec, Founder & CEO,
Demandbase. “AI will allow marketers to create highly personalized ads tailored to buyer’s specific interests in real-time through superior and infinite knowledge. AI will also make mass email marketing tools obsolete (and the resulting spam email), automatically scanning out the ‘bad’ leads and creating custom, personalized communication instead. As AI continues to advance, we can expect to see the recommendation engines that power companies like Netflix and Amazon develop specifically for the B2B market. This will start to pave the way for a B2B business concierge—a completely automated and customized buyer’s journey throughout the funnel that is driven by AI.”
Michael Stonebraker, co-founder and CTO of Tamr (and recipient of the 2014 A.M. Turing Award), predicts that “there will continue to be a shortage of qualified data scientists.” Stonebraker: “I don’t expect the market to be in equilibrium until 2019 at the earliest. Every major university will have a data science program in place in 2017.”
Bruno Aziza, CMO, AtScale, predicts that “in 2017, we can expect the role of the Chief Data Officer (CDO) to move from ‘bad guy’ in the enterprise to the steward of significant initiatives.” Aziza: “The first generation of CDOs served as barriers to data. Their role was blocking users from data, as security and governance were the top issues. In 2017, we can expect CDOs to enable access. And with a huge increase in the number of CDOs across the globe, we can expect to see a maturing of their role as they are able to overcome some of the initial challenges they faced when the role was new to the enterprise.”
Mark Woollen, Chief Product Officer, Radius, predicts that “in 2017, CMOs will look to internal data specialists as their superheroes.” Woolen: “CMOs have previously looked to their marketing team as the Robin to their Batman, supporting areas like lead generation and campaign performance. Next year CMOs will put equal value on building a team of data specialist and marketing operations superheroes that understand how to grow pipeline through data-driven intelligence. With this team of marketing superheroes, CMOs will focus on appointing data specialists from within, tapping areas like marketing and sales operations.”
Dan Graham, Internet of Things Technical Marketing Specialist, Teradata, predicts that “the Internet of Things Architect role will eclipse the data scientist as the most valuable unicorn for HR departments.” Graham: “The surge in IoT will produce a surge in edge computing and IoT operational design. 1000s of resumes will be updated overnight. Additionally, fewer than 10% of companies realize they need an IoT Analytics Architect, a distinct species from IoT System Architect. Software architects who can design both distributed and central analytics for IoT will soar in value.”
Zach Supalla, CEO,
Particle, predicts that “
2017 will be a team-building year” for many in the IoT space, “where investments will be made in fostering internal talent and attracting the right external hires to address the complex needs of launching a connected product.”
Ashish Thusoo, CEO,
Qubole, predicts that “
data engineers will get pinched” in
2017. Thusoo
: “Data engineers are the rare individuals who understands infrastructure and architecture, and can also think about how to process data and how the data will be used. As more enterprises look to scale their big data initiatives the data engineer becomes increasingly important.”
In 2017 and beyond, there will be not only new types of jobs and organizational roles, but also new players, possibly impacting the dominance of US-based tech vendors. Ash Ashutosh, founder and CEO,
Actifio, notes that “US companies aren’t the only ones looking across the pacific for untapped business opportunities.
We’ll continue to see an explosion of Chinese companies coming to stake their claim in the US market as well, offering low-cost, commoditized versions of other tech products to accelerate their growth.
