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5 Tips for Making Data Work for Your Business

In this contributed article, Scott Ziemke, Director of Data Science, Vertafore, discusses how in order to overcome barriers to data & analytics success, companies must implement clear and relevant strategies that focus on real business cases. Too often data scientists get excited about data and its nuances, but the organization doesn’t know how to operationalize the insights and turn them into action that drives business results. Here’s how to overcome that problem, along with other tips that can help ensure you get the maximum ROI out of your data & analytics investment.

2021 Trends in Blockchain: Mainstream Adoption at Last

In this contributed article, editorial consultant Jelani Harper believes that mainstream adoption of blockchain is surely coming, both at the consumer and enterprise levels. In all likelihood, momentum in one of these domains will spur that in the other. At this point, cryptocurrencies are still the forerunners of this technology, particularly with the foregoing methods to redress measures for data privacy and oversight.

Using AI for Contract Management

In this special guest feature, Sunu Engineer, Principal Architect at Icertis, discusses using AI for Contract Lifecycle Management. Done right, AI for contract management has the potential to empower organizations to stay out front by turning repositories of contracts into indispensable strategic advantages.

Molecula Secures $17.6 Million in Series A Funding to Democratize Machine-Scale Analytics and AI

Molecula, an enterprise feature store built for machine-scale analytics and AI, announced it closed a $17.6 million Series A round of funding, bringing its total funding to $23.6 million. The round is led by Drive Capital, with participation from TTV Capital and existing investors including Tensility.

GridGain 8.8 Advances Its Multi-Tier Database Engine to Scale Beyond Available Memory Capacity and Meet Growing Customer Demand

GridGain® Systems, provider of enterprise-grade in-memory computing solutions powered by the Apache® Ignite® distributed database, announced GridGain 8.8, the latest release of the company’s in-memory computing platform. The release features enhanced support for GridGain’s multi-tier database engine, which scales up and out across memory and disk.

When Big Data Collides with Intellectual Property Law

In this contributed article, technologist Bernard Brode discusses how the realm of intellectual property – and the myriad twists and turns inherent – is being subjected to the latest waves of cutting-edge analytic tools which fall within the sphere of big data.

How AI Will Shape the Future of Customer Communications

In this contributed article, Eric Schurke, VP of Operations at Ninja Number, discusses how AI solutions are changing the way businesses communicate in 2021 and beyond. To succeed you should focus on three different aspects: incorporating customer feedback, over-communicating wherever you can, and building a culture of customer success.

Driving with Data: How AI is Personalizing the Auto Insurance Industry and Saving Lives

In this special guest feature, Gilad Avrashi, CTO & Co-Founder at MDgo, believes that now is the time for the insurance industry to leverage AI technology to unlock the power of data and provide personalized services that customers demand to retain them as loyal customers.

AI-driven Platform Identifies and Remediates Biases in Data

Synthesized has released the Community Edition of its data platform for Bias Mitigation. Released as a freemium version, the offering incorporates AI research and cutting-edge techniques to enable any organization to quickly identify potential biases within their data and immediately start to remediate these flaws.

Feature Stores are Critical for Scaling ML Initiatives and Accelerating both Top-line and Bottom-line Impact

Feature stores are emerging as a critical component of the infrastructure stack for ML. They solve the hardest part of operationalizing ML: building and serving ML data to production. They allow data scientists to build more accurate ML features and deploy these features to production within hours instead of months.