“Above the Trend Line” – Your Industry Rumor Central for 1/7/2019

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Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz. Our intent is to provide you a one-stop source of late-breaking news to help you keep abreast of this fast-paced ecosystem. We’re working hard on your behalf with our extensive vendor network to give you all the latest happenings. Heard of something yourself? Tell us! Just e-mail me at: daniel@insidebigdata.com. Be sure to Tweet Above the Trend Line articles using the hashtag: #abovethetrendline.

Year End Special! In this edition of our popular “Above the Trend Line” column we’ll feature a number of 2019 prediction commentaries received from our friends in the big data ecosystem. Don’t miss these insights by industry luminaries from well known companies.

2019 is the year containers and AI meet in the mainstream, said MapR SVP Data and Applications, Jack Norris. “NVIDIA announced open source Rapids at the end of this year. Harbinger of how the focus on operationalizing AI, better sharing across data scientists and distributing processing across locations will drive containerized. Another rising technology that drives this prediction is Kubeflow which will complement containers and distributed analytics.”

All DevOps strategies have universal goals: agility, faster deployment, increased end-user experience, and “smart” operational decision-making,” said Tom LaRock of SolarWinds. “With DevOps transitioning from a hype to a standard practice in agile IT departments, technology and operations professionals striving to add value to their businesses should consider the next step in enhancing their departments: DataOps. In today’s increasingly digital world, data cannot be excluded from the agile decision-making process. In fact, we predict that 2019 will be the year that data is recognized as a key business driver. “Data Culture” will become increasingly implemented into tech environments, and organizations will become data-driven and data-first. This shift will also give rise to DataOps as traditional admins start to understand that their days of tuning indexes are ending, one page at a time. Operations teams must adopt a “data mindset” to discern the type of data that exceeds their department and can be polished into something that adds value to the business overall. With DataOps, organizations can begin to transition their IT team into a data science team, as they adopt a data-first frame of mind. DataOps can help the C-suite operate their businesses more effectively by extracting and analyzing the most pertinent pieces of data and distilling and crafting them into a compelling and “business-digestible” narrative that can be easily understood across the organization. Companies will begin to actuate on this data, not just report and track in Excel—they will start using valuable data to make more informed decisions. The ability to share this actionable, business-digestible narrative may even earn tech pros a seat at the strategy table.”

In 2019, AI and machine learning will continue to mature and empower employees to perform better – helping them strengthen their core skills and capabilities, more efficiently get work done, and stay engaged at work through increasingly personalized and intuitive experiences—ultimately helping to elevate the enterprise,” said Joe Korngiebel, CTO Workday.

Behavioral metadata will lead businesses to more accurate predictions: Organizations will experience further disillusionment with all the vague hype around machine learning and AI,” said Aaron Kalb, Alation Co-founder and VP of Design and Strategic Initiatives. “They’ll increasingly realize that accurate predictions require not just a large volume of training data, but a particular type: behavioral metadata. Analysis of this data can be mined to better shine a spotlight on what’s used and what’s useful. This is the same insight that drove Google Search’s ranking prowess two decades ago: the content of a webpage was less predictive of its utility than how often other pages — built by other people — linked to it. As the ML/AI buzz continues to wear thin, we’ll see a strong appetite emerge for this type of impact-driven technology and behavioral metadata among organizations.”

Trust and transparency will continue to drive the AI-conversation – with companies applying new anti-bias techniques, in combination with guidance from in-house and industry ethics advisory groups, to make their products and platforms fairer,” said Dr. Dario Gil, COO of IBM Research.”

Shadow IT will become a bigger player in providing companies with the expertise they need in 2019,” said Rob Consoli, CRO of Liaison Technologies. “Leaders must demand a rejuvenated focus on connected and integrated application networks and on eliminating data siloes. With the help of these additional tech-oriented team members in many departments, organizations are able to refine their focus on providing accurate data and better integrate their existing data sources. Because organizations need to be agile, these additional experts operating in the shadow IT landscape can make the entire company more focused on data accuracy.”

AI will continue to advance to address real-world problems,” said Mike Duensing, CTO of no-code development platform Skuid. “AI will continue to be the key to harnessing the data growth mega trend. With the unfathomable growth of data and transactions, companies will need to corral insights that AI can provide to understand and make decisions.”

As Baby Boomers shift out of the daily workforce, the wealth of knowledge they represent regarding mainframes is at risk of being lost,” said Jeff Cherrington, VP of Product Management at ASG Technologies. “Because Gen Xers joined the workforce as distributed and cloud was taking off, the majority of them focused their careers in those areas. As a result, there is a generational trough in mainframe expertise, even as the platform still very central and important to many enterprises. To fill the gap, we already see millennials stepping forward to fill the vacuum of mainframe expertise left as the most proficient professionals retire. Those looking for challenging opportunities in IT, using many of the modern tools and techniques they’ve learned, are applying open positions managing and developing for mainframe. That said, there is a lot that needs to get done to bring them up to speed before their Boomer counterparts retire. To support this transition, many enterprises expect to use AI and machine learning to capture and transfer knowledge to younger generations, and augment mainframe management by automating time-consuming tasks. To do this, Baby Boomer employees are tasked with “guiding” machine learning systems how to manage mainframes, creating a digital record of their approaches and enabling these tools to identify opportunities to optimize processes.”

As 2018 winds down, market volatility appears only to be ramping up,” said Marcell Vollmer, Chief Digital Officer, SAP Ariba. “And while futures may be tough to forecast for the year ahead, the tight job market is likely to maintain its grip on businesses large and small. That’s why now more than ever, in an increasingly digital economy, business leaders are turning renewed attention to artificial intelligence and its emerging practical capabilities to drive down costs, reap operational efficiencies, and take on tactical tasks. Through machine learning and other AI applications, cloud-based digital networks are unlocking new value for businesses at a time when human labor is scarce but data is abundant. The timing is perfect in 2019 for a rapid acceleration of AI adoption, as enterprises extend their competitive advantage by becoming increasingly intelligent, nimble and predictive, something I fully expect to directly accelerate intelligent spend management.”

AI-augmented analytics will be mainstream,” said Ketan Karkhanis, SVP and GM Analytics at Salesforce. “2019 will be the year when AI-led analytics (known as Automated Discovery) will become mainstream. Human brains are not wired to evaluate millions of data combinations at sub second speeds, but machine learning is literally built for this problem and the perfect solution. Business leaders and data analysts are better understanding that AI is not going to replace jobs, but augment them, and I expect that in the next year, the majority of data analysts will have the power of data science at their fingertips without the need to write code.”

Managing the AI Wild West – The growth of AI will spur a wave of legal, ethical and legislative action to manage how humanity deals with big data and technology,” said Lana Klein – Managing Partner, Growth Analytics & AI Transformation (GAIT) at Fractal Analytics. “AI and big data are creating many new moral and legal dilemmas for the human kind. Technology grew much faster than human ability to deal with legal, ethical and psychological implications, creating AI ‘wild west.’ In the coming years we will see action related to figuring out how to address these issues. How do we feel about someone possibly being privy to our every move, need and thought – even if they mean no harm? How much of our privacy are we willing to sacrifice for more convenience and safety? How do we feel about possibility of expanding one’s intellect by fusing AI chips in with our brains? These and many other questions will need to be addressed. New laws will be passed, new ethics will be formed and new psychological boundaries will emerge.”

The use of Machine Learning and AI solutions in production in enterprises will continue to grow rapidly from about 30% currently to 50% by the end of 2019,” predicted Striim. “Technologies supporting the data collection, processing, and analytics integrating with machine learning solutions will become essential for all enterprises by 2020. Machine Learning solutions’ impact will grow as they support an increasing number of real-time or near-real-time prediction use cases that influence operational decision making. With the operationalizing of ML models to solve critical operational problems, businesses will achieve higher ROI from ML, further driving the technology’s growth. Real-time ML prediction use cases will grow for both enterprises (e.g. real-time fraud detection) and individuals (e.g. sports performance, real-time health related alerts).”

Consolidation will continue: Based on 2018’s acquisitions, we predict we’ll see a steady rollout of acquisitions in the next 12-18 months, as major enterprise tech companies rush to get a piece of the latest innovations, said Cloud Foundry Foundation’s Executive Director Abby Kearns and CTO Chip Childers. “Shuffling in executive leadership at certain large companies is a telltale sign that acquisition opportunities will be used to grow business more rapidly. Consolidation around a specific technology is bound to happen, with the market solidifying around that tech.”

The long-promised enterprise AI transformation is poised to begin in earnest in 2019,” said Zachary Jarvinen, Head of Technology Strategy | AI and Analytics, OpenText. “Most enterprises have reached a point of digital maturity, ensuring access to quality data at scale. With mature data sets, AI providers can offer lower cost, easier to use AI tools for specific business use cases. The effect of enterprise AI at scale will be significant. Gartner expects the business value of AI to hit nearly $3.9 trillion in by 2022. Consumers also stand to benefit in almost every sector. They’ll see more innovative products and services, smarter homes, factories and cities, improved health, and a higher quality of life.”

AI is exciting but don’t forget about the people, tool and processes,” says Kai Grunwitz, SVP EMEA for NTT Security, “A Fortune 500 company could learn the hard way that, while AI is highly relevant for a security strategy, organizations still need the best human analysts and other complementary technologies. AI is no longer the stuff of science fiction films. It’s already here, driving a fourth industrial revolution, which promises to radically reshape the world and society we live in. While there is plenty to get excited about, AI is not a silver bullet and we could see it continue to create a false sense of security in 2019.”

Reinforcement Learning becomes mainstream: RL by itself will continue to have a broader impact in 2019 not just in cyber-security but also in every area that uses AI and ML services,” said Ravishankar Rao Vallabhajosyula, Head of Data Science at Impetus Technologies. “Deep RL has already shown significant promise in matching human performance in video games. This learning has begun to influence everything from transportation systems to utilities, advertising and personalizing patient treatments and will see major strides being made in 2019.”



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