The Commercial Side of Graph Analytics: Big Uses, Big Mistakes, Big Opportunities

A. Hodler
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Abstract

Connectivity is the cornerstone of our contemporary world, permeating various sectors like retail, communications, biology, and finance. Although this inherent interconnectedness holds substantial meaning and predictive power, harnessing it for practical use in the business realm often proves challenging. In this presentation, we will delve into the commercial applications of graph analytics, highlighting both common pitfalls to avoid and promising opportunities to explore. To begin, we will explore the prevalent use cases of graph analytics, encompassing areas such as fraud detection, supply chain optimization, data management, and recommendations. We'll also shed light on why many teams tend to deploy only a limited set of graph algorithms. Additionally, we will examine how the COVID-19 pandemic has impacted the utilization of graphs in business settings. Next, we will venture into the major mistakes that businesses often make when implementing graph analytics. These blunders range from technical hurdles like scalability issues and handling tricky data types to human challenges such as fostering a graph-thinking mindset and avoiding excessive perfectionism. Moreover, you will gain quick tips to help teams secure funding for graph projects. Lastly, we will delve into some of the most significant prospects within the commercial space. We will address enduring challenges, such as transforming business data into a graph format and ensuring interoperability with production processes. We will also dedicate time to exploring the rising interest in combining graphs with AI systems, particularly the recent buzz surrounding combining graphs with generative AI. While this particular trend garners attention, we will look at other promising opportunities that it may overshadow. By the end of this talk, you will have gained a comprehensive understanding of the practical applications of graph analytics in business contexts. Furthermore, you'll gain valuable knowledge about pitfalls to avoid, strategies for securing funding, and a forward-looking perspective on emerging possibilities in this dynamic field.
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图表分析的商业面:大用途,大错误,大机会
互联互通是当今世界的基石,已渗透到零售、通信、生物、金融等各个领域。尽管这种内在的互联性具有实质性的意义和预测能力,但将其用于商业领域的实际应用通常是具有挑战性的。在本次演讲中,我们将深入探讨图形分析的商业应用,强调要避免的常见陷阱和有希望探索的机会。首先,我们将探讨图形分析的流行用例,包括欺诈检测、供应链优化、数据管理和建议等领域。我们还将阐明为什么许多团队倾向于只部署一组有限的图算法。此外,我们将研究COVID-19大流行如何影响商业环境中图形的使用。接下来,我们将探讨企业在实施图形分析时经常犯的主要错误。这些错误包括技术障碍,如可扩展性问题和处理棘手的数据类型,以及人类挑战,如培养图形思维心态和避免过度的完美主义。此外,您将获得快速提示,以帮助团队获得图形项目的资金。最后,我们将深入探讨商业领域中一些最重要的前景。我们将解决长期存在的挑战,例如将业务数据转换为图形格式,并确保与生产流程的互操作性。我们还将花时间探讨将图与人工智能系统相结合的兴趣,特别是最近围绕将图与生成式人工智能相结合的嗡嗡声。虽然这种特殊的趋势引起了人们的注意,但我们将关注它可能掩盖的其他有希望的机会。在本讲座结束时,您将对图形分析在商业环境中的实际应用有一个全面的了解。此外,您将获得有关避免陷阱的宝贵知识、获得资金的策略以及对这个动态领域中出现的可能性的前瞻性观点。
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