“Above the Trend Line” – Your Industry Rumor Central for 12…
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: firstname.lastname@example.org. 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 start off with new partnerships, alignments and collaborations … Anodot, the autonomous analytics company, and professional services firm, Deloitte Australia, announced a strategic alliance to help companies supercharge their real-time analytics capabilities. Deloitte will use Anodot’s AI/ML solution to expand its Consulting Analytics & Cognitive practice’s portfolio of AI-powered service offerings. Bringing together Anodot’s anomaly detection capability with Deloitte’s business advisory, data and analytics capability creates a unique value proposition to help businesses proactively identify and fix anomalous business and technical incidents that would otherwise lead to millions of dollars lost in sales, production or fraud. With vast amounts of data collected across business and IT metrics, it becomes increasingly difficult to track, analyse and derive valuable business insights, especially with traditional Business Intelligence (BI) and do-it-yourself approaches. With Anodot’s autonomous analytics, data is rapidly analysed across all data sources in real-time. Its predictive capabilities detect risks before they reach customers, allowing users to make the right decisions and prevent major crises … Iguazio, the serverless platform for intelligent applications, announced it is partnering with Google Cloud to enable real-time AI across the cloud and intelligent edge. Google Cloud and Iguazio’s hybrid cloud is enabling Trax, a leading provider of computer vision and analytics solutions for retail, to benefit from Kubernetes and a cloud-native architecture without managing its underlying infrastructure. Trax’s retail solutions leverage image recognition and predictive analytics to efficiently manage the physical shelf for consumer packaged goods manufacturers and retailers.
In the stealth category we learned … Under-the-radar, metro Denver start-up Turbine Labs is completing its inaugural launch year with millions in revenue and millions more in pre-booked orders that all but guarantee that the information discovery start-up will at least double or triple sales in the coming year. According to CEO and Founder Leigh Fatzinger 2018 is exceeding all expectations and the company is eagerly looking forward to the new year with marquee clients like Microsoft, Lenovo, Verizon and Time Warner. Turbine Labs solves the problem of an overabundance of data for C level executives and communications professionals by providing a solution that saves hours of time each week and enables better decision making. Indeed a recent study by IBM indicated that faulty decision making due to bad data cost US Corporations over $3.1 trillion dollars annually. Those findings were supplemented by another study the consulting giant KPMG that showed fully one third of all US corporations do not trust the data they receive. These two studies underwrite a category estimate by International Data Corp that the cognitive/AI software platform market will have an average annual growth rate of 39% and exceed $8 billion dollars within 3 years.
We also heard of some new products, services and solutions … MarkLogic Corporation, a leading operational and transactional Enterprise NoSQL database provider, launched Data Hub Flight School, a unique learning and development program to educate technologists on next generation data management and data hub technology. The Data Hub Flight School, aimed at application developers, takes training to new heights by enabling guided simulations of real-world data integration projects, covering such challenges as security and data modeling that often prevent companies from getting the most from their data assets … ASG Technologies, a trusted provider of proven solutions for information access, management and control for the world’s largest enterprises, announced ASG Data Intelligence 9.8, an updated release of its metadata management, data governance and data catalog solution. Named a leader in the 2018 Gartner Magic Quadrant for Metadata Management Solutions, the latest ASG release helps chief data officers and data protection officers comply with privacy regulations and streamline attestation by automatically finding and reporting personal data stored in both structured and unstructured sources … A major issue facing enterprises today is how to leverage advanced and predictive analytics on a large scale while data scientist resources are scarce. Datawatch Corporation (NASDAQ-CM: DWCH) announced the release of Datawatch Swarm 2.2, which addresses that problem by delivering built in automation that connects data scientists with business analysts to better scale advanced analytics across the entire organization without increasing headcount. The integration between Datawatch Swarm and Datawatch Angoss, the powerful predictive and data science platform, creates the industry’s only enterprise data intelligence marketplace that acts as a virtual exchange of trusted data and an execution environment for every data role in an organization … Accelerite announced ShareInsights 3.0 for non-developer data analysts, featuring new capabilities that significantly reduce the complexity and costs associated with extracting insights from AWS Data Lakes. The new version of the platform gives users a no-code, browser-based, drag-and-drop toolset that unifies functions of Athena, EMR, Redshift, Kinesis, Elasticsearch and SageMaker to enable rapid data analysis, visualization and collaboration. Automated service selection, cost forecasting and cost management lowers cloud utilization costs by as much as 20X.
Video analytics can provide 100x the value of numerical analytics,” reported Quantum executives. “Customers will increasingly need deep media catalogs that provide visibility to all the media assets at their disposal, whatever form are they in, showing who has edited them. This is a much richer catalog than just a file system – to accommodate more analytics functions. With video surveillance, do two people look like they’re having an argument? Does someone look like they’re holding a weapon? Are 10 people in line at a grocery store and the people at the end of the line are getting frustrated? Such applications provide tremendous potential, given the right tools. Providing those data services for video will be an area for growth.”
The amount of data collected by organizations is continuing to increase, but companies are struggling to get useful business insights from the information; the sheer amount of data obtained makes it difficult to analyze historical data,” said Brian Brinkmann, VP of Product at Logi Analytics. “As the need for data analysis becomes a requirement, the demand for highly-skill data professionals will continue to increase. An on-staff data scientist is a simple solution to this data analysis struggle yet hiring one of these top talents is not always easy or within budget. As a replacement to these professionals, more organizations will turn to predictive technologies to utilize and analyze their massive amounts of data. In early 2019, application developers and product managers will pivot their focus to offering action-infused predictive insights embedded directly inside their existing applications in the form of artificial intelligence (AI) and predictive analytics. This will allow application end-users to predict what’s next for their organization without the need for R and Python expertise.”
82 percent of today’s enterprises experience three or more barriers to adopting artificial intelligence or AI,” said Steve Rodda, Cherwell’s Chief Product Officer. “That number will be cut in half. According to a recent IDG survey, 82 percent of respondents reported more than three significant barriers to adopting AI. This includes the challenges if integrating with current systems, the rapidly changing AI landscape and that the cost and effort of training an AI system was too high. Reducing or eliminating the barriers to adopting AI is possible, but it will take some work from end users and vendors. An understanding of what AI is combined with more streamlined opportunities to incorporate it into existing work flows through ad hoc integrations will help. Businesses and their teams need to have a better understanding of how to identify and prioritize the processes that should be automated, understand the opportunity cost of not automating tasks and ensure they can measure and quantify the impact of automation. Chat bot’s are a new cutting-edge tool providing efficiencies that can be reaped through implementation of big data/AI, providing companies solutions to complex problems.”
Across transportation generally, the availability of a wider range of data, combined with the ability to manage large data sets will result in a reevaluation of the investment program, work program and budget development processes,” said Bob McQueen, Industry Consultant for Teradata Government/Transportation. “This will feature the adoption of results driven approaches to budget development based on measurement of the effects of investments. We are already seeing this in one of two leading agencies across the US and this trend will continue as big data and analytics enable us to take a scientific approach to investment planning for transportation. The focus will switch the answers to concise questions like ‘if I want to influence a modal shift in favor of transit, then where should I invest, how much should I invest and what results can I expect?’”
Last year was the year of the data scientist,” said Nima Negahban, CTO and Co-founder of Kinetica. “Enterprises focused heavily on hiring and empowering data scientists to create advanced analytics and machine learning models. 2019 is the year of the data engineer. Data engineers will find themselves in high demand – they specialize in translating the work of data scientists into hardened, data-driven software solutions for the business. This involves creating in-depth AI development, testing, devops and auditing processes that enable a company to incorporate AI and data pipelines at scale across the enterprise.”
Our fascination with the use of computing power to augment human decision-making has likely outgrown even the tremendous advances made in algorithmic approaches,”said Christian Beedgen, Co-founder and CTO, Sumo Logic. “In reality, the successful use of AI and related techniques is still limited to areas around image recognition and natural language understanding, where input/output scenarios can be reasonably constructed, and that will not change drastically in 2019. The idea that any business can “turn on AI” to become successful or more successful is preposterous, no matter how much data is being collected. But the collection of data to support humans and algorithms continues and raises important ethical questions and is something we need to pay close attention to over the next few years. Data is human and therefore is just as messy as humans. Data does not create objectivity. It is well established that data and algorithms perpetuate existing biases and automated decisions are — at best — difficult to explain and justify. Appealing such decisions is even harder when we fall into the trap of thinking data and algorithms combine to create objective truth. With greater decision-making power comes much greater responsibility, and humans will increasingly be held accountable for the impact of decisions their business makes.”
A leading edge technique in the consumer space is around how to integrate website/app funnel analysis with UX insights to better identify how to grow conversion rates,” said Incedo’s CEO Nitin Seth. “When combined with machine learned product recommendations on the newly designed flow and UX, this can be a killer combination of how to leverage data to grow top and bottom lines of a company.”
Machine learning will drive the push towards cloud microservices – The push towards cloud-based microservices in 2019 will be driven primarily by new machine-learning capabilities for application development and data management,” said Rich Weber, CPO at Panzura. “Cloud providers have preached the value of integrating AI and machine learning within their offerings, and now that those technologies are more reality than fiction, businesses will be investing heavily in cloud microservices which take advantage of AI and machine learning technologies to lower costs, increase efficiency and out-develop their competitors.”
AI will evolve inside every Fortune 100 company, data science labs will be created in 2019,” said AlegionCEO and Co-Founder Nathaniel Gates. “Fortune 100 companies are testing the AI waters and experimenting with what is possible. As the benefits of AI become a reality, we predict they will start cannonballing into the pool with fully equipped and resourced data science labs. As this happens, we’ll all start to see Fortune 100’s putting well-tested AI projects into production.”
The Rise of Explainable AI! As AI becomes embedded in more and more processes, there is an increasing need for transparency in how it works and makes decisions on our behalf,” said Tom Wilde, CEO and Founder of Indico. “Users will demand real-world, plain English examples and explanations to for full transparency. This will also make it easier for data science and SMEs to collaborate on improving AI’s contribution to the business.”