Many of today’s marketing processes are powered by AI and machine learning. Discover how these technologies are shaping the future of customer experience.

Knowledge graphs have been around for almost half a century – as the term was first coined in 1972! For a long time, they simply languished in the academic world until Google announced their knowledge graph in 2012. Since then, knowledge graphs have evolved quite dramatically, and now there is no turning back. The last 10 years have seen a meteoric rise in machine learning (ML) and artificial intelligence (AI). Because of their ability to drive intelligence into data and add context, knowledge graphs are used to make ML and AI more reliable, robust, trustworthy, and explainable.

Pandemic, new regulations send enterprises hunting for data to level-up their AISAN MATEO, Calif., May 18, 2021 (GLOBE NEWSWIRE) -- Explorium, the External Data platform that automatically discovers thousands of relevant data signals and uses them to improve analytics and machine learning, has closed its $75M Series C funding round, led by global venture capital and private equity firm Insight Partners, with existing investors Zeev Ventures, Emerge, F2 Venture Capital, 01 Advisors and Dynamic Loop Capital also participating. The round brings Explorium’s total investment to more than $127M, as George Mathew, Managing Director at Insight Partners and former President & COO of Alteryx, joins Explorium’s board of directors. Explorium’s latest capital raise, eight months after its Series B, comes on the heels of a year that revealed the limitations — and perils — of predictive models based entirely on internal data, as well as the challenges of obtaining impactful and reliable data from outside a company’s four walls. Reviewing the events of 2020, consulting firm McKinsey & Co. noted: “In a few short months, consumer purchasing habits, activities and digital behavior changed dramatically, making preexisting consumer research, forecasts and predictive models obsolete. Moreover, as organizations scrambled to understand these changing patterns, they discovered little of use in their internal data.” As business conditions evolved and regulations restricted access to crucial sources of information, business teams went hunting outside their organizations for data to support machine learning and other mission-critical analytics—and discovered it was no easy task. A recent Explorium study found enterprises were hungry for external data but had no clear idea how to get it. Of the respondents: 79% perceived external data as very valuable for analysis and modeling93% said finding relevant data took high or medium effort81% said they spent at least $100K a month acquiring external dataLess than 33% have an organized strategy for acquiring it. “Just a few years ago, enterprises were trying to get machine learning up and running. Now they’re realizing the importance not just of data quality, but data diversity,” said Dave Menninger, SVP & Research Director at Ventana Research. “Nearly 80% of the participants in our Machine Learning research are working with external data to enrich their models. The challenge is how to access that information easily and keep it up to date.” Explorium stands out as a singular solution to enterprises’ data dilemma. Its External Data Platform analyzes data models, searches its wide collection of thousands of external data signals and automatically discovers the most relevant signals to improve analytics and machine learning. Data scientists and business analysts can quickly enrich their predictive models with external data, benchmark the boost in performance and deploy the models with just a few keystrokes, with the assurance that all data is compliant with current regulations. Since the start of the pandemic, Explorium has doubled its customer base and more than quadrupled revenue. Companies like BlueVine, GlassesUSA.com, Melio and PepsiCo use Explorium to enhance AI models for use cases including lead scoring, identifying default risk and fraud and upleveling analytics such as demand forecasting and customer lifetime value. “Machine learning is key to our competitive strategy, and external data is what powers it,” said Elad Zoldan, Head of Data at Melio. “Before we started using Explorium, we had to search for data source by source and strike separate deals with different providers. It wasn’t a scalable or flexible solution. With Explorium, we have a universe of external data at our fingertips, and an incredibly efficient way to figure out what data will make the biggest impact.” In addition to its ML Engine for automated external data discovery and feature engineering, Explorium has released Signal Studio, a new product that enables data and business analyst teams to quickly find and integrate the most relevant external data signals to their analytics pipelines. “We’re seeing data become the new differentiator,” said George Mathew, Managing Director at Insight Partners. “AI and machine learning are already table stakes. Everyone has them. Competitive advantage will depend not just on the quality of your models but on the diversity of data fueling those models, making Explorium a unique proposition for data scientists and analysts alike.” “As we saw last year, machine learning models and tools for advanced analytics are only as good as the data behind them. And often that data is not sufficient,” said Maor Shlomo, CEO at Explorium. “We’re addressing a business-critical need, guiding data scientists and business leaders to the signals that will help them make better predictions and achieve better business outcomes.” About ExploriumExplorium provides the first External Data Platform to improve Analytics and Machine Learning. Explorium enables organizations to automatically discover and use thousands of relevant data signals to improve predictions and ML model performance. Explorium External Data Platform empowers data scientists and analysts to acquire and integrate third-party data efficiently, cost-effectively and in compliance with regulations. With faster, better insights from their models, organizations across fintech, insurance, consumer goods, retail and e-commerce can increase revenue, streamline operations and reduce risks. Learn more at www.explorium.ai. About Insight PartnersInsight Partners is a leading global venture capital and private equity firm investing in high-growth technology and software ScaleUp companies that are driving transformative change in their industries. Founded in 1995, Insight Partners has invested in more than 400 companies worldwide and has raised through a series of funds more than $30 billion in capital commitments. Insight’s mission is to find, fund and work successfully with visionary executives, providing them with practical, hands-on software expertise to foster long-term success. Across its people and its portfolio, Insight encourages a culture around a belief that ScaleUp companies and growth create opportunity for all. For more information on Insight and all its investments, visit insightpartners.com or follow us on Twitter @insightpartners. Media contactTheresa Carper415 848 9175 explorium@firebrand.marketing

Read this solution profile to learn how Lumada Manufacturing Insights supports your digital transformation by providing AI-enabled industrial analytics to optimize machine, production, and quality outcomes.

Imagine an IT organization that can deliver services faster with improved service quality while radically reducing the number of IT tickets. This is not a vision for the future. It is the reality today for growing numbers of organizations that are leveraging machine learning to transform the service desk. Download this white paper and learn how embedded analytics and machine learning can help you improve IT service quality and desk staff efficiency, while reducing the number of tickets. See how this can enable you to deliver actionable insight where and when it is needed to drive superior business outcomes.

Healthcare has traditionally been slow to adopt emerging technologies, and this is a challenge that must be overcome. Healthcare has been described as a conservative field, one that is slow to embrace change. For example, there was some initial resistance to the idea of using electronic blood pressure cuffs in hospitals. Also facing skepticism and resistance was the advancement of electronic medical records, because of concerns that it takes away from the patient-physician interaction and increases the time required to write notes. Analytics and machine learning is certainly no exception; it is simply another new, unfamiliar technology, and while industries such as automotive and manufacturing embraced it with little issue, healthcare will likely be a different story.

The release goes on, "Additionally, developers now have superior insight into data metrics within an Alluxio cluster as well as expanded visibility into the application and persistent storage layer. Increased metrics coverage within the Alluxio cluster, the application layer and underlying storage systems makes it easier to connect and manage multiple data sources. A new dashboard in the UI provides overall health and utilization metrics with accompanying command line interface (CLI) tools for live cluster statistics. All remote procedure call (RPC) requests are recorded, providing a detailed set of machine-consumable metrics with API-based statistics generation for third party tools such as Grafana and Prometheus. Developers can quickly diagnose storage system performance issues with new tools that include latency histograms and capacity utilization. The application configuration checker provides a one-click integration check for third party applications such as Hive, MapReduce, Spark, and more."

Newswire) 8x8, Inc. (NYSE:EGHT), a leading provider of global cloud communications and customer engagement solutions, today announced key appointments to accelerate the company's Artificial Intelligence (AI) and Machine Learning capabilities, and expand its human resources organization globally. The team will lead the company's efforts to leverage big data, analytics and machine learning to allow companies to gain deep, actionable insights and improve customer experiences. Dr. Ali Arsanjani was formerly the Founder and Chief Technology Officer of Analytics and Machine Learning at Deep Context, a deep-learning startup. Prior to Deep Context, he was a Distinguished Engineer and Chief Technology Officer for Analytics and Machine Learning at IBM. Ali was responsible for worldwide enablement of highly customized solutions that combined real-time, unstructured content and structured analytics and machine learning to solve customer's complex problems while at IBM. He is a recognized authority in the AI industry and has chaired and participated in numerous machine learning research bodies, including The Open Group, and is responsible for co-leading the SOA Reference Architecture, SOA Maturity Model and Cloud Computing Architecture standards.

The MongoDB Spark Connector integrates MongoDB and Apache Spark, providing users with the ability to process data in MongoDB with the massive parallelism of Spark. The connector gives users access to Spark's streaming capabilities, machine learning libraries, and interactive processing through the Spark shell, Dataframes and Datasets. We'll take a tour of the connector with a focus on practical use of the connector, and run a demo using unsupervised machine learning algorithms to process data in MongoDB.

Marketing typically has the largest discretionary budget in any organization because of the variety of activities we do, but now it also has the largest discretionary technology budget. That shift of dollars away from IT has been causing tensions for some time, but marketers now must be at the head of the table when purchasing everything from CRM, to business intelligence and analytics tools, to ecommerce platforms, and of course the website. Just like technology, customer experience budget and planning will move more towards marketing--as will customer satisfaction KPIs. The entire customer journey from pre-sale to customer advocacy is part of the overall brand experience. "Predictive analytics driven by AI and machine learning are going to change the way we do just about everything" One of the biggest obstacles marketers still run into is resistance to change.