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: email@example.com. Be sure to Tweet Above the Trend Line articles using the hashtag: #abovethetrendline.
Year End Special! The next several “Above the Trend Line” columns will include a number of 2019 prediction commentaries from our friends in the big data ecosystem. Don’t miss these insights by industry luminaries from well known companies.
Let’s get started with some new funding news … MachineMetrics, which equips factories with the digital tools needed to increase productivity and win more business, announced it has raised $11.3 million in Series A financing. Tola Capital led the round with participation from existing investors Hyperplane Venture Capital, Long River Ventures, Mass Ventures, Hub Angels and Firebolt Ventures. With the new funds, the company will expand its data science and product development teams while accelerating global sales. MachineMetrics is a pioneer in Industrial IoT (Internet of Things) and artificial intelligence technology. Its system is designed so that customers can install it themselves without the need for expensive and time-consuming customization. Once installed, manufacturers can collect, visualize and analyze data from any industrial machine. It automatically senses when there is a problem, even learns to predict some problems hours or minutes before they occur, and recommends solutions that reduce costly unplanned outages … Import.io, a leading web data integration solution provider, announced it has closed a $15.5 million Series B funding round to accelerate global growth and expand its product offerings to meet the growing needs of enterprises. Talis Capital, a London-based venture capital firm, led the investment with participation from existing investors IP Group, OpenOcean, Oxford Capital and Wellington Partners. This capital infusion comes at a time when companies are urgently trying to become “data-driven,” as a key part of digital transformation. Alternative data sources such as the web are crucial to gaining a competitive advantage. The web is the single largest data source on the planet, but traditional solutions for gathering that data are complex, unreliable, time intensive and poor quality … Dataiku Inc., a leading enterprise data science and machine learning platforms, announced a $101 million Series C funding round led by ICONIQ Capital and supported by Alven Capital, Battery Ventures, Dawn Capital, and FirstMark Capital. This announcement follows the company’s $28 million Series B in September 2017 and the release of Dataiku 5 in September 2018. Since its founding in 2013, Dataiku has focused on the vision of democratized data science as the key to uninhibited possibilities instead of restricted technologies reserved for the elite few. Today, Dataiku provides a platform that enables enterprises to fundamentally transform their business – global leaders use it to build AI that optimizes marketing budget, enables maintenance services, anticipate market trends or detects fraud, and more … Cien Inc., a leading provider of AI-powered sales productivity solutions, announced it closed a seed equity round and obtained financing, via Spain’s Center for the Development of Industrial Technology (CDTI). The proceeds totalling over $1.8M USD will among other things be used to fund a Center of Excellence (CoE) for data science and artificial intelligence (AI) in Barcelona. The purpose of the Center is to understand B2B buyer and seller behavior by analyzing Customer Relationship Management (CRM) data.
We also heard of some new M&A activity starting with … Planet, an integrated aerospace and data analytics company, announced it has entered into a definitive agreement to acquire Boundless, the leader in open and scalable GIS. The acquisition will expand Planet’s commercial business with the U.S. government and commercial agriculture clients … OpenText™ (NASDAQ: OTEX, TSX: OTEX), a global leader in Enterprise Information Management (EIM), announced it has completed the closing of its previously announced acquisition of Liaison Technologies, Inc., a leading provider of cloud-based information integration and data management solutions … Accenture (NYSE: ACN) has entered into an agreement to acquire Knowledgent, a data intelligence company, expanding its data management capabilities that help companies gain deep insights into their businesses and customers, creating a strong competitive advantage. Based in New Jersey, Knowledgent employs more than 200 highly skilled “informationists” that provide data strategy and architecture, data engineering, and data management services. Accenture is driving innovation in data services with investments in machine-led solutions, highly skilled data specialists and leading platform and industry data capabilities, helping clients rotate to data-powered intelligent enterprises. Knowledgent also helps enterprises maximize the value of their data and create competitive advantage by applying innovation to better manage data, using cutting-edge data technologies and significant experience in the life sciences, healthcare and financial services industries.
In the new partnerships, alignments and collaborations department we learned … Due to the accelerating data growth in our decade, the focus for all businesses has naturally turned to data collection, storing, and computation. Nevertheless, what companies really need is getting useful insights from their data to ultimately automate decision making processes. This is where Machine Learning can add tremendous value. By applying the right Machine Learning techniques to solve a specific business problem, any company can increase their revenue, optimize resources, improve processes, and automate manual, error prone tasks. With this vision in mind, BigML and SlicingDice, the leading Machine Learning platform and the unique All-in-One data solution company respectively, are joining forces to provide a more complete and uniform solution that helps businesses get to the desired insights hidden in their data much faster.
In the new customer wins category, we heard … WANdisco (LSE: WAND), the LiveData company, announced it has secured its largest ever cloud contract with a major US health insurer (the “Client”). The agreement is valued at approximately $3 million and will see the Client deploy the Company’s patented Big Data and Cloud product, WANdisco Fusion (“Fusion”), on a volume limited, perpetual license. The contract initially spans a three-year subscription period valued at $3 million. The Client, one of the largest health insurers in the US, has substantial data requirements across their 22,000 employees and 15 million members representing a significant opportunity to grow the subscription over time. Both hybrid-cloud and cloud migration are LiveData use cases where, in order to take advantage of the significant benefits of cloud, customers must be able to move data without interruption to business operations … Parents of newborn babies have a lot of reasons to be happy, but the lack of sleep that comes with it is not one of them. Enter Nanit, an automated sleep adviser that utilizes computer vision, image recognition and deep learning to monitor and analyze a baby’s sleep behavior and provides guidelines to improve the quantity and quality of sleep. For parents, that means a lot less worrying about whether their child is okay and a lot more nights without interruptions. Nanit met with psychologists and sleep and behavioral experts to gain a deep understanding of the sleep behavior of babies and then developed an algorithm to give its deep learning model the ability to monitor and analyze sleep data effectively. To perfect the algorithm, Nanit needed to run hundreds of experiments concurrently; however, the Nanit team found that, in reality, reproducing experiments is a very time-consuming process—and not always successful. They then turned to MissingLink.ai, a deep learning platform for AI and machine learning technologies. Today, thanks to the way MissingLink automatically stores information on all experiment-related elements, the Nanit team can seamlessly revisit, examine and reproduce experiments. MissingLink integrates seamlessly with other data platforms that Nanit uses, the team was able to implement highly automated data lifecycle management processes.
In people movement news we found … Alternative Data Group (ADG), a company which specializes in processing unstructured data, announced that it had brought Professor Petter Kolm on its board in an active development role, effective imminently. Dr. Kolm is a Mathematics Professor and the Director of the Mathematics in Finance M.S. Program at the Courant Institute of Mathematical Sciences, New York University. Dr. Kolm has been at the forefront of innovation in quantitative data finance and has decades of experience in both the theoretical machine learning techniques used in alternative data as well as well as industry experience applying his expertise of the topic to a wide range of financial technology companies.
And finally, in the new products, services and solutions area we have … Researchers have demonstrated a new technique that can store more optical data in a smaller space than was previously possible on-chip. This technique improves upon the phase-change optical memory cell, which uses light to write and read data, and could offer a faster, more power-efficient form of memory for computers. In Optica, The Optical Society’s journal for high impact research, researchers from the Universities of Oxford, Exeter and Münster describe their new technique for all-optical data storage, which could help meet the growing need for more computer data storage. Rather than using electrical signals to store data in one of two states — a zero or one — like today’s computers, the optical memory cell uses light to store information. The researchers demonstrated optical memory with more than 32 states, or levels, the equivalent of 5 bits. This is an important step toward an all-optical computer, a long-term goal of many research groups in this field … In a world where processing power is often a bottleneck, breaking the quantum barrier is expected to lead to huge benefits for businesses and society at large in 2019. With this in mind, Accenture announced it has been granted a US patent for a “multi-state quantum optimization engine” that leverages quantum computing technology to help organizations optimize business decision-making with unprecedented efficiency and precision. Accenture’s new patent – U.S. Patent No. 10,095,981 – reveals how businesses could take advantage of the best aspects of both classical and quantum computing techniques to enable breakthrough solutions to problems that couldn’t be solved before. The patent is one of the most recent in Accenture’s robust global intellectual property portfolio and builds on years of quantum investments, partnerships and R&D efforts.
The pressure to achieve greater ROI from AI and ML initiatives will push more business leaders to seek innovative solution,” said Dr. Ryohei Fujimaki, CEO and founder, of dotData. “While substantial investments are being made into data science across many industries, the scarcity of data science skills and resources limits the advancement of AI and ML projects within organizations. In addition, one data science team is only able to execute several projects a year given the iterative nature of the process and the manual work that goes into data preparation and feature engineering. In 2019, data science automation platforms will capture much of the mind share. Data science automation will cover much wider areas than machine learning automation, including data preparation, feature engineering, machine learning and the production of data science pipelines. These platforms will accelerate data science, execute more business initiatives whilst maintaining the current investments and resources.”
The Hype of Machine Learning; Actual Delivery is Suspect,” said Jim Barkdoll, CEO, TITUS. “The promise will outweigh reality on artificial intelligence and machine learning. Any tech vendor worth its salt will want a piece of this market, so we can expect 2019 to yield a flurry of announcements around new AI and/or ML initiatives. That said, it will still take time for this excitement to convert to tangible solutions that will have a positive impact on day-to-day operations.”
BI is officially declared dead,” said Carlos Meléndez, COO of Puerto Rico-based nearshore software engineering services provider, Wovenware. “The term, Business Intelligence, has been around since 1958 (long before it could be traced as a search term) and it is showing its age. In 2019 the term will finally give way to Business Insights, marked by less of a focus on dashboards and reports, and more toward outcome-driven analytics – measuring analytics according to outcomes.”
The need for AI-enabled search and analytics solutions is becoming more prevalent,” said Kamran Khan, Managing Director of Search and Content Analytics, Accenture Applied Intelligence. “According to a recent Gartner Magic Quadrant report on Insight Engines, 50% of analytic queries will be generated using a combination of automation technologies by 2019. Traditional search functions will give way to the emergence of cognitive search – along with Machine Learning and Natural Language Processing – resulting in AI-driven solutions to help enterprises un-trap their data and derive more valuable knowledge and insights.”
The growing emphasis on AI and machine learning will make TensorFlow and H2O breakout technologies in 2019,” said Kunal Agarwal, CEO of Unravel. “In addition, Spark and Kafka will continue to see spiking popularity, as they did in 2018.”
2019 will be the year of education for DataOps, a more collaborative data management practice that focuses on automation, self-service, and real-time integration,” said Dan Potter, VP Product Management and Marketing at Attunity. “DataOps will change the way data is consumed; breaking down the silos in IT operations and building greater accuracy and speed of data analytics. By leveraging technologies such as change data capture (CDC), it will disrupt the way data is shared and made available.
2019 will be the year of the death of the data scientist,” said Aman Naimat, CTO at Demandbase. “In 2019, everybody is going to start learning Artificial Intelligence (AI) and the domain of data science will no longer be a purist data scientist. There are only about 5,000 folks who are data scientists and we can’t rely on them to lead an industrial revolution, which we’re on the brink of; everyone within an organization needs to have AI skills, from product managers to business analysts. The death of the data scientist is the pinnacle of this revolution.”
Effective solutions will be based on a strong synergy between rule-based engines that encode domain knowledge, and machine learning solutions that generalize and automate integration and analytics in a hybrid AI platform,” said Ihab Ilyas, Professor in the Cheriton School of Computer Science at the University of Waterloo. “The synergy between humans and automatic (mostly machine learning) tools have been limited so far to exercises such as: (1) providing training data and (2) validating decisions (such as clusters in record linkage or classes in classification). While this simple, human-in-the-loop interaction is a must, future tools will need a stringer synergy to model the domain knowledge and the enterprise memory into the data curation tools itself: for example, machine learning models that take as input human-written rules (as in weak supervision); and hybrid solutions that form ensembles of human-written classifiers with machine-learned ones. The machine learning research community has been pushing with leading results in this direction for the past few years. It’s time to see it in working, deployable solutions.”
ML/AI models get skinny with edgification,” said , Sastry Malladi, CTO of FogHorn. “Moving machine learning (ML) to the edge is not simply a matter of changing where the processing happens. The majority of ML models in use today were designed with the assumption of cloud computing capacity, run time and compute. Since these assumptions do not hold true at the edge, ML models must be adapted for the new environment. In other words, they need to be “edge-ified”. In 2019, “real edge” solutions will enable relocating the data pre- and post-processing from the ML models to a complex event processor, shrinking them by up to 80% and enabling the models to be pushed much closer to the data source. This process is called edgification, which will drive adoption of more powerful edge computing and IIoT applications overall.”
The black box of algorithms becomes less opaque: Part of the issue with data morality with AI and machine learning is that numbers and scenarios are crunched without insight into subsequent answers came to be,” said Talend CTO Laurent Bride. “Even researchers can have a hard time sorting it out after the fact. But in the coming years, while it won’t lead to complete transparency with proprietary algorithms, the black box will still become less opaque as end users become increasingly educated about data and how it’s used.”
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