The application of graph processing and graph databases will grow at 100% annually through 2022 – Gartner
Data by itself has little to no value. Connecting data is essential to provide context, make sense of the underlying implications of data and for analytics to deliver value.
Business users across the globe are demanding more and more complex information across internal and external data, unstructured and structured data and want to blend data from different applications. Traditional query tools and language like SQL can’t match up to analyzing this level of complex data at scale.
With Graph analytics, businesses can construct relationships between disparate data points to build context and offer insights to make better informed business decisions. Graph analytics, an emerging form of data analysis, refers to a combination of analytic methodologies that shows the connection between diverse entities like people, places and things.
Graph analytics offers a comprehensive solution in solving complex relationships, finding distant connections between data and analyzing quality of relationship, with an easy-to-digest visual format. Graph analytics has a wide range of use cases including social media analysis, fraud detection, resource management, genome research and more! Organizations will increasingly use this technology to accelerate data preparation and enable flexible data science.
This article elucidates on some of the popular use cases of Graph Analytics.
By applying information from social networks to Graph Analytics, businesses can identify influencers and decision makers, an important information in sales, needed to maximize sales efforts by holding negotiations with the right people. Social Network Analysis is useful to screen counterintuitive insights that can hasten the decision-making process and engage prospective consumers better to move them through the sales pipeline.
Workforce analytics can also employ social network analytics to identify trendsetters and social influencers who can influence the workforce to adopt initiatives, create impact and even resolve sticky behavioural situations. Ensuring buy-in and engagement of these influencers can lead to a better overall employee engagement as well.
Analysis of fraud involves studying interactions between different actors of a transaction. This reveals entities within a system that are potentially problematic and likely to undergo fraudulent attacks. Graph Analytics helps to identify bad actors well beforehand and instate counteractive measures to stop fraudulent behavior.
Identifying illegal behavior and criminal activity is also possible with Graph analytics. By tracking phone calls, emails, people visiting suspects in specific locations, and network of monetary distribution, law enforcements use graph analysis to interpret data that can identify malignant and benign behavior.
Optimizing the use of system resources and maximizing utilization in computer and communication networks requires balancing of loads. Analyzing the network relationships allows to identify overloaded resources and thereby design reallocation of traffic to reduce risk, and reconfigure the topology to improve operations.
Utility companies that provide basic amenities, such as water, sewage services, electricity, dams, and natural gas can also use network analysis to optimize consumption of their resources and design the delivery of their utilities that can obtain maximum effectiveness and reduce depletion of critical components.
Graph Analytics is also used by logistics companies for optimizing routes. Graph analysis helps to identify the most optimum routes (by evaluating several variables that affect the travel) that can render cost savings and ensure an effective supply chain.
Money laundering involves concealing the source of illegitimate funds by passing it around through a complex sequence of banking transfers or commercial transactions and finally blending it with legitimate funds. With Graph Analytics, you can employ relationship models from graph databases and then use pattern recognition, classification, statistical analysis, and machine learning to these models, to analyze large amounts of data.
This is especially important for case correlation analysis. Whenever regulation authorities detect that some transactions are suspicious, they are passed on to human investigators for a closer look. With graph analytics, inspecting each activity individually can be avoided. Instead suspicious activities can be grouped together using pre-defined connections.
Discovering trends from social networks is invaluable to marketers. Social media data can be hampered by the presence of bots as they skew the data and increase inaccuracies.
Graph analytics can be used to find fake bots in such cases and increase information accuracy.
Businesses employ graph analytics to determine authentic accounts wherein graphs are created between accounts with retweet counts to analyze how many times these accounts are retweeting their neighboring ones.
Accounts that are unnaturally popularized (through bots) exhibit different characteristics from naturally popular accounts with some common unnatural deviations.
Following are the methods used to detect more number of bots that employ graphs and relationships in their working:
Graph analytics is an emerging tool to counter fraudulent activities and stay ahead of fraudsters. Graph analytics and its associated technologies can provide capabilities to analyze data and relationships that are either extremely difficult or impossible using traditional analytics methodologies. Graph analytics and graph techniques have been evolving and becoming a standard tool to analyze complex data relationships. With Graph analytics, it is possible to derive insights in increasingly complex ways, which makes it an indispensable tool for today’s businesses. You might also be interested in exploring our business intelligence and analytics services.
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