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“Above the Trend Line” – Your Industry Rumor Central for 1/7/2020

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, industry partnerships, 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.

For this installment of “Above the Trend Line,” we continue to offer a number of commentaries from the big data ecosystem regarding 2020 predictions and 2019 year-in-review:

“Top-down, C-level support is required to create a culture of data science and analytics,” commented Alteryx Ashley Kramer, Senior Vice President of Product Management. “As companies progress in their analytic journey, there will be an impetus for executive management to drive self-service data science and analytics from the top down. Leadership must commit with conviction to evolve beyond the antiquated approach to analytics and propel a cultural shift within their organization. This will be required for buy-in from all lines of the business. Successful executives will identify and champion employees who demonstrate innovative thinking and embrace new technologies, as breaking away from entrenched legacy practices becomes critical in creating this new culture. With the proper C-level support, these innovative employees will be able to embrace new technologies, advance those technologies across their teams and apply them to internal organizational processes to achieve greater business outcomes.”

“AI will go from identifying trends to making intelligent decisions,” commented commented Dave Wright, Chief Innovation Officer of ServiceNow. “AI will begin to drive real-world productivity across all aspects of business in 2020. As companies start using AI to gain deeper insights and understand trends, the technology will lead to more prescriptive actions and further automation of tasks. As AI continues to improve, we will see AI taking automatic actions that are ‘intelligent.’ As humans become more familiar with this newfound ‘intelligence,’ they will remove themselves from the equation, and businesses will benefit from greater productivity gains. For example, right now AI can predict when a printer needs a new toner cartridge, but taking a step further, AI can order the toner before it runs out, creating a seamless experience.”

“If you thought the marketing noise around AI was loud in 2019, prepare for it to get deafening in 2020,” commented Sudheesh Nair, CEO of ThoughtSpot. “For all the smoke and mirrors we saw in 2019, 2020 will be even murkier, driven by the financial fervor for all things AI. Private companies, especially startups, will increasingly tout themselves as AI companies in a bid to capture VC dollars, regardless of what they actually offer. Similarly, we’ll see companies, especially public ones, repackage their offerings with some sort of AI spin to impress shareholders hungry for stories of digital transformation success.”

“’AI-First’ will become the standard for creating a great application experience,” commented John DesJardins, VP of Solution Architecture & CTO, Hazelcast. “We are on the frontier of a radical reshaping of business models brought along by AI. The introduction of 5G is leading to greater IoT adoption, creating more bandwidth for AI/ML to get closer to the edge. However, applications that generate such high volumes of streaming data will require performance on a very different scale, as millions of transactions per second are routine for AI/ML systems. To fine-tune the transaction process, enterprises will increasingly need to track and manage applications while in the hands of the user in real-time. This opens up operational opportunities where improving the speed and efficiency of applications can elevate the customer experience, which is a significant burden of 64% of IT decision-makers a recent Hazelcast survey found.”

“2020 will be an inflection point for data privacy & AI, where consumers will be more aware of how their data is being used even by robots,” commented Jim Kaskade, CEO, Conversica. “With the rise of AI and machine learning, data privacy is an ethical question that we will no longer be able to ignore. In 2020, consumers will become much more aware of how their data is being used by businesses, and AI will be a big part of that. There are different classes or levels of PII data – Personally Identifiable Information – and as we allow machines to automate our lives, we need to consider what information a machine gets to look at, process, and store. ‘First-class citizen data’ includes information such as healthcare records and social security numbers, and the average consumer feels that their rights regarding this information have increasing governance. “Second-class citizen data” is the metadata that’s created around the exchange of information by the machine throughout the consumer’s digital journey. And based on a person’s behavior, AI can determine your gender, political attitudes, personal preferences, and affiliations – all based on inferences. Even under existing regulations, there’s still potential for bad actors and their use of data using AI. On the flip side, we believe that AI could also be used to better secure information in a way that a human can’t. For example, AI can be programmed to automatically purge PII rather than storing it.”

“Container observability – over the past few years, many folks were dipping their toes in Kubernetes, learning and doing proof of concepts,” commented Bob Moul, CEO of machine data intelligence platform Circonus. “In 2020, we’re going to see a huge number of those deployments go online, tightly aligned with the DevOps function within enterprises.The caveat is that container environments emit an enormous volume of metrics, and many legacy monitoring products won’t be able to handle the high cardinality requirements.”

“Cloud and AI/ML will continue to be strong technology focus areas for enterprises across the globe,” commented TIBCO CTO Nelson Petracek. “Cloud will continue to be ‘hybrid’ and ‘multi-cloud’ in nature (it is really hard to get rid of those mainframes!), and the effective use of analytical models (statistical models or otherwise) will continue to be a key differentiator for organizations. I believe that there will be a broad shift in focus away from the models themselves to the process(es) that make these models possible.”

“Ensembles will become big again,” commented Kevin Gidney, Seal Software‘s co-founder and Chief Technical Officer. “With the introduction of large scale neural networks–like BERT, RoBERTa, DistilBERT and others–new state-of-the-art levels have been achieved. However, the NETs are extremely large and contain many unused connections. In much the same way as the human brain operates, trimming connections as children grow into adulthood to remove unused neural connections, reducing complexity and size while increasing speed and allowing for further learning, sub- and smaller NETs within larger ones will be trained and then merged via ensembles, to achieve further performance increases, but with a smaller footprint and network size. Each NET will be specialized for a set task and pooled together into a collection.”

“In 2020, content will find where it’s needed,” commented ASG executive Rob Perry. “Agents have long been sending content based on predefined searches, but by leveraging AI and ML–teamed with decision and policy engines—it will finally find its way to the teams that need it based not on a search, but based on their activity. Much like targeted advertising, engines will present new and updated content as the need arises. Further, collaborative content tools will provide new ways to ensure compliance with privacy, records and governance relating to content. As adoption of team collaboration products expands, providers will make it easier to protect both the privacy of information collected and created and to govern it in compliance with regulations. Profiles contain personal data that must be protected. Collaboration can create documents and information that must be retained as records, or should be disposed to reduce risk.”

“AI & Machine Learning will be the best friend that cloud vendors have ever known,” commented Sreedhar Veeramachaneni, CEO of SSTech. “Why? Because as data fuels Machine Learning and Artificial Intelligence to drive predictive and prescriptive analytics, there will be an exponential need to store and process huge volumes of critical data on a near-real-time basis. Most organizations lack the organic “horsepower” to handle these exploding demands. And the only organizations with enough capacity and capability to scale up to meet these rigorous demands will be the top cloud vendors.”

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