When the world's largest healthcare company by revenue went looking for a technology solution that could improve quality of care while reducing costs, the search took ten years. What they found--an innovative way to model healthcare data--is saving the company an estimated $150M annually and enabling its medical professionals to provide accurate and effective care path recommendations in real time. This same solution, graph databases and graph analytics, proved crucial at the height of the Covid-19 pandemic. A testament to its potential, the market for graph technology is projected to reach $11.25B by 2030.[1] It's what social networking applications use to store and process vast amounts of "connected" data.

If you think you have a winning idea that enables people to monitor the impact of climate change, predict a global crisis like Covid-19 or solve one of the the world's many big problems today, then you might be interested in TigerGraph's million-dollar challenge that is on now. The Million Dollar Challenge is calling on people to use graph technology, which can link and map out a lot more information than traditional databases, to find answers to these pressing problems. Announced last month, the contest has already drawn 1,000 registrations from 90 countries. Participants include data scientists, developers, product managers, designers, data engineers, machine learning engineers and students. TigerGraph, which offers graph databases for advanced analytics and machine learning, is behind the challenge that promises US$1 million in prize money.

Every large financial services company is pursuing AI initiatives in 2022. Some are in the earliest stages. Others have been investing in the technology for years. Regardless, all recognize that natural-language processing (NLP) is a foundational capability for their long-term AI business goals. Unfortunately NLP is still an emerging technology, and the best options for deploying it are not immediately obvious.

In an era of AI adoption in industry, stark contrasts in our thinking begin to show about how we leverage computing, data, and inference. This article considers graph technologies in the context of business: enhancing human thinking and enabling data exploration, especially among teams of domain experts augmented by AI applications. Specifically, let's develop and deconstruct the notion of graph thinking. Suppose you have an errand to run, such as shopping for groceries: "Remember to buy eggs and more rice on the way home from work today." The needs are clear, and your approach is well understood. People use phrases such as "It's not rocket science" to describe the level of competency required here. Or perhaps still count on your fingers? In any case, let's call this a "Simple" context.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. This post is by Dr. Mukta Paliwal, senior data scientist at Persistent Systems. As many as 50% of Gartner client inquiries on the topic of artificial intelligence involve a discussion involving the use of graph technology, the market research firm said in its Top 10 Data and Analytics Trends for 2021. Every large enterprise wants to exploit available data to bring more insights for doing business at scale.

The massive data sets, complex processing capabilities and advanced analytical models in the current digital business landscape create the perfect storm of opportunity for data and analytics. After languishing for decades, graph approaches are being embraced by analysts, data scientists and data management professionals. Graph technology is a sort of catch-all phrase that includes graph theory, graph analytics and graph data management. IT executives have a growing interest in graphs, as there is a basic understanding that graph technology is somehow different from existing solutions. Data and analytics leaders are being asked to provide guidance regarding how graph technology can be used, but many still don't have a complete understanding.

When COVID-19 hit, organizations using traditional analytics techniques that rely heavily on large amounts of historical data realized one important thing: Many of these models are no longer relevant. Essentially, the pandemic changed everything, rendering a lot of data useless. In turn, forward-looking data and analytics teams are pivoting from traditional AI techniques relying on "big" data to a class of analytics that requires less, or "small" and more varied. Transitioning from big data to small and wide data is one of the Gartner top data and analytics trends for 2021. These trends represent business, market and technology dynamics that data and analytics leaders cannot afford to ignore.

When COVID-19 hit, organizations using traditional analytics techniques that rely heavily on large amounts of historical data realized one important thing: Many of these models are no longer relevant. Essentially, the pandemic changed everything, rendering a lot of data useless. In turn, forward-looking data and analytics teams are pivoting from traditional AI techniques relying on "big" data to a class of analytics that requires less, or "small" and more varied. Transitioning from big data to small and wide data is one of the Gartner top data and analytics trends for 2021. These trends represent business, market and technology dynamics that data and analytics leaders cannot afford to ignore.

Neo4j, the leader in graph technology, announced the latest version of Neo4j for Graph Data Science, a breakthrough that democratizes advanced graph-based machine learning (ML) techniques by leveraging deep learning and graph convolutional neural networks. Until now, few companies outside of Google and Facebook have had the AI foresight and resources to leverage graph embeddings. This powerful and innovative technique calculates the shape of the surrounding network for each piece of data inside of a graph, enabling far better machine learning predictions. Neo4j for Graph Data Science version 1.4 democratizes these innovations to upend the way enterprises make predictions in diverse scenarios from fraud detection to tracking customer or patient journey, to drug discovery and knowledge graph completion. Neo4j for Graph Data Science version 1.4 is the first and only graph-native machine learning functionality commercially available for enterprises.

Neo4j, the leader in graph technology, announced the latest version of Neo4j for Graph Data Science, a breakthrough that democratizes advanced graph-based machine learning (ML) techniques by leveraging deep learning and graph convolutional neural networks. Until now, few companies outside of Google and Facebook have had the AI foresight and resources to leverage graph embeddings. This powerful and innovative technique calculates the shape of the surrounding network for each piece of data inside of a graph, enabling far better machine learning predictions. Neo4j for Graph Data Science version 1.4 democratizes these innovations to upend the way enterprises make predictions in diverse scenarios from fraud detection to tracking customer or patient journey, to drug discovery and knowledge graph completion. Neo4j for Graph Data Science version 1.4 is the first and only graph-native machine learning functionality commercially available for enterprises.