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AI and Big Data: The Next Frontier of Fintech

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Data analytics is becoming a cornerstone of the financial industry, with startups and established financial service firms looking to give investors clearer guidance with information collected and captured from multiple sources. Advances in machine learning and artificial intelligence (AI) in particular are providing greater insights and better customer experiences. AI-powered data analytics not only captures vast amounts of data in real-time, but also helps users understand how different data points relate to each other, providing insights that might otherwise be lost. Faced with a breakdown in brand loyalty as younger customers prioritize user experience, financial services are now racing to leverage data-driven cognitive technologies. Cambridge, MA-based Kensho, which recently received 58 million in funding from Goldman Sachs, San Francisco-based Alphasense, backed by Tribeca Venture Partners, and Toronto-based Bigterminal are some of the fintech players leveraging AI.



Guavus Acquires SQLstream

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SAN JOSE, Calif., Jan. 25, 2019 -- Guavus, a Thales company and pioneer in Artificial Intelligence (AI)-based analytics, announced today that it has acquired SQLstream, a real-time streaming analytics company based in San Francisco, CA. The acquisition enables Guavus to expand its offering, providing communications service providers (CSPs) and Industrial Internet of Things (IIoT) customers access – at the network edge – to real-time, cloud-enabled streaming analytics to address their growing big data needs. "With our integrated solutions, CSPs to IIoT customers will be able to take advantage of something that's radically different as we deliver AI-powered analytics from the network edge to the network core. With this solution, our customers can now analyze their operational, customer, and business data anywhere in the network in real time, without manual intervention, so they can make better decisions, provide smarter new services, and reduce their costs," said Guavus CEO, Faizel Lakhani. "In a world facing exponential growth in the volume of data coming from increasingly connected network devices and IIoT-based sensors, the inclusion of SQLstream's industry-leading technology opens up huge new opportunities for our customers and our partners. Their disruptive technology allows customers to interactively inspect and curate streaming data for analytics at the edge. We're excited to have the SQLstream team onboard," said Lakhani.


Water Sector Embracing Big Data

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The water sector has collected reams of data for decades, but it's only within the last few years that utilities, agencies, consultants and vendors have begun to use that data to improve everything from managing maintenance to predicting water flow to digitally mimicking an entire watershed. The move to leverage digital information in the sector over the last two to three years is "drastic," says Luis Casado, senior vice president of water for Gannett Fleming and one of several people who spoke passionately about the possibilities of water data at Water Environment Federation's annual WEFTEC conference Oct. 1-3 in New Orleans. Firms like Gannett Fleming, Arcadis, Brown and Caldwell, and Jacobs are taking previously underutilized information from supervisory control and data acquisition, or SCADA, systems, and pairing it with historic datasets and additional sensor data to create customized digital dashboards and applications for water agencies and related entities. "It's not a single piece of software, it's an approach of how you look at data and how you merge that information and use it effectively in day-to-day operation," said Kevin Stively, smart utility leader for Brown and Caldwell, in a presentation at the event. He said historical information can be layered on real-time information to help a younger workforce make the operational decisions that older workers relied on their "gut" to make.


Customer Data Platforms: The Next Big Shift in SaaS Marketing Stacks?

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I'm sure you consider yourself data-driven. You make decisions based on data. You see, the current structure of the modern marketing stack leads to a large amount of data fragmentation. As we collect more and more data, it's becoming increasingly hard to piece together and manage that data, and more importantly, to use that data in real-time to build better campaigns. That thread is what leads me to think that something is changing and today's' Marketing SaaS landscape might look like different very soon.