What is Graph Data Science? And Why Does it Matter?

What is Graph Data Science? And Why Does it Matter?

The use of graph data is rapidly becoming mainstream in enterprise IT. Gartner notes in its report titled Top 10 Data and Analytics Technology Trends for 2020 that “understanding relationships and generating graphs based on the combination of diverse data at scale are the keys to modern analytics.”

In a Gartner survey, 92 per cent of companies said they would use graph AI and machine learning (ML) in the next five years. This trend has been bolstered by an explosion of academic research. Over 28,000 peer-reviewed papers have been published about graph-powered data science in the past decade. Globally, graph data science is viewed as a significant research focus.

Graph data science is a powerful and innovative method. It can calculate the “shape” of a connected context for each piece of data using graph algorithms. As a result, it enables ML predictions that are significantly better and richer. Earlier, big tech companies like Google were capable of handling massive volumes of connected data. Today, these approaches are available to all enterprises.

Graph data science can transform the way enterprises make predictions in many diverse scenarios, from fraud detection and tracking customers to patient journeys. It makes use of the connections between data points to make more accurate and interpretable predictions.

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To identify potential new associations between genes, diseases, drugs, and proteins in a drug discovery context means providing the immediate context necessary to assess a discovery’s relevance and validity. In customer recommendations, it means learning from user journeys to make accurate suggestions for future purchases while presenting options relevant to their buying history to boost trust in suggestions.

With ML, organisations could go to the next level by learning generalised, predictive features quickly from data. Several companies are still learning how to use connected data in their existing ML workflows. However, there is less friction to get started, and there are more real-world examples and case studies.

In terms of value, knowledge graphs are an excellent starting point. The concepts they illustrate connect ideas and serve as a foundation for more advanced approaches, such as graph algorithms or deep learning. The potential payback of graph technology encourages data scientists to recognise that, whether it’s finding patterns, identifying high-value features, or training machine learning models, a lot of their work cannot be accomplished without it.

  • The British government is also using graph science data in a post for GOV.UK, Felisia Loukou and Dr Matthew Gregory discuss deploying their first ML model. Which automatically recommends content to users on the site based on which page they are on — quite similar to how you might get recommended a new show to watch on your favourite streaming app.
  • Predictive Maintenance: Another example, Caterpillar, one of the world’s leading manufacturers of construction equipment, gained recognition for its graph data science innovations. For predictive maintenance, the firm uses natural language processing (NLP) and a knowledge graph. Parsing more than 27 million documents, its IT team identified valuable data. To discover trends and connections that were previously hidden, the team developed an NLP tool. The classification tool uses the sections of data already tagged with terms like “cause” or “complaint” to apply to the rest of the data. Stanford Dependency Parser is accessed via WordNet, a lexicographic dictionary. It then analyses text and graph technology to find patterns and connections, build hierarchies, predict outcomes and add ontologies. Using the newly connected data, users can conduct meaningful, data science-enhanced searches.

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  • Enhancing The Detection Of Infections: New York-Presbyterian Hospital is another example of graph data science in action. Using the technology, the analytics team tracks infections and takes strategic measures to contain them. Using graph technology, their developers could connect all dimensions of an event — the what, when, and where of the event.

As a result of this insight, the team created a “time” tree and a “space” tree to model all the rooms patients could be treated in on-site. Although the initial model revealed many relationships, it was not sufficient to meet project goals. To connect the time and location trees, an event entity was included. Using the resulting data model, the analytics team can analyse everything in its facilities. The system can then proactively identify and contain diseases before they spread.

A quarter of global Fortune 1000 companies will possess a graph technologies skills base in three years, predicts Gartner’s data industry team. Their data and analytics initiatives will be able to benefit from graph technologies. By 2023 and beyond, graph-enabled data science will become an essential part of business analytics. Organisations need to focus on understanding it as a key to unlocking business advantages.

Originally published on: Datatechvibe

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