Author | Song Wenzhe
Graph data analysis is closely related to enhanced analysis and interpretable artificial intelligence, and it is likely to bring huge disruptions to the data analysis industry in the next three to five years.
In February 2019, Gartner included Graph analytics as one of the top ten trends in data analysis in the "Top Ten Data Analysis Technology Trends". It was included in the list with Augmented analytics and Augmented analytics. Explainable AI (Explainable AI) is closely related and is likely to bring huge disruptions to the data analysis industry in the next three to five years.
For business managers and architects, data and analysis have become a key part of serving customers, hiring employees, optimizing supply chains, optimizing finances, and performing many other key functions in the organization. With the rapid development of graph databases, master graphs Analyzing ability, understanding the market situation of graph analysis, and doing a good job in the architecture design of the enterprise-level graph analysis platform, urgently need to be put on the agenda.
Currently, financial services, healthcare, pharmaceuticals, telecommunications and other industries are actively embracing graph analysis technology. However, companies encountered many technical obstacles when choosing a graph analysis platform and project to be implemented, mainly in four aspects: First, the infrastructure and data preparation is not perfect, and the data has not yet reached a certain level of maturity. Second, the business combing and exploration is not comprehensive, and it is impossible to understand the business from the perspective of graph technology or use the value of in-depth correlation analysis. Third, the selection and use of tools are not well understood. Fourth, there is a scarcity of landing experience and graphic technology talents.
Among these obstacles, what needs to be considered is the choice and use of tools. Tool selection is a key factor in the whole life cycle of the project. From the initial technology selection to business exploration and research, deployment and development, to the final online effect, it is closely related to it. In view of the pain points in the actual application of enterprises, the graph analysis platform TigerGraph summarizes four issues that need to be paid attention to when selecting tools for enterprises:
Deployment management: whether the graph analysis platform or graph database has backup and recovery functions, whether the security is complete, whether there are log monitoring and alarms when there is a problem, and whether the problem can be retrospectively studied when an abnormality occurs.
Performance: Whether the performance can support the real graph analysis scenario, the real graph analysis should be composed of deep, complex and deep fusion multiple data in terms of depth, breadth and real-time. Such graph analysis is really valuable and meaningful of.
User experience and integration: Whether it has a friendly graph exploration function and can it be linked with machine learning.
Enterprise-level functions and platform stability: whether it has high availability and data access control.
Aiming to help business managers and data scientists correctly understand graph technology and choose correct and efficient graph analysis tools, TigerGraph and InfoQ have planned an online live broadcast "Deep Analysis Based on Linked Data-TigerGraph Deployment and Actual Combat Analysis" to help companies Managers, data scientists, and architects and developers who are interested in graph databases answer questions.
Gartner lists graph analysis technology as one of the top ten trends in data analysis, and is closely related to the enhanced analysis and interpretable AI that are also included on the list. In 2019, graph analysis also showed a trend of topicality and contention in China. How to understand this technology, how to integrate it into the current architecture, how to choose among many graph analysis products, and what are the landing scenarios for graph analysis ? Based on the perspective of TigerGraph, an enterprise-level graph analysis platform, this online seminar will provide you with some directions from the fog, and share TigerGraph's experience and views in the past ten years with you.
Introduction- Understand the top ten trends of Gartner data analysis;
- Understand the current situation of the graph analysis market;
- Understand what features and architecture positioning the enterprise-level graph analysis platform should include;
- Learn about TigerGraph product features and application scenarios.