Understand how graph analytics can help your business solve the challenge of finding meaningful insights within today’s immense amounts of data.
Everything is connected these days. Take social media, for example. You can “like” your neighbor’s cat photo or “friend” a colleague. As you interact with people on this application, graph analytics are commonly employed to find relationships within that activity. You and John both liked a mutual friend’s photos from vacation and also follow a number of similar rock bands—now John is a suggested friend. In this example, as the relationships expand (posts, likes, messages, hashtags) the resulting graph visualization is a massive interconnected network of information.
Graph analytics determine the strength and direction of relationships between objects (or nodes) in a graph. It is based on graph theory and consists of nodes, edges, and properties. Let’s take a minute to define everything.
Graph—a mathematical term that represents relationships between entities. Two elements make up a graph: nodes (representing entities) and edges (representing relationships).
Graph Analytics—also called network analysis, is the analysis of relations among entities such as customers, products, operations, and devices.
Graphical representations of graph analytics are made up of a few components:
Nodes—represent entities to be tracked (such as people, businesses, accounts, etc.).
Edges—these are the lines that link relationships between nodes (an example of an edge would be “liking” your neighbor’s cat photo, or “following” your favorite blues musician).
Properties—is the specific identifier connected to nodes (if “people” are a version of node, “Nancy” could be an example of a property).
Why graph analytics?
To understand why graph analytics is transforming the data analytics game, we need to fuel up the flux capacitor and travel to a time when Back to the Future ruled the big screen. Back in the 1980s, many companies used relational databases in order to generate reports of data via the process of tabular organization. These relationships were stored in separate tables and then joined with the process of table joins. But as the number of connected devices continued to grow, the increase in data required more complex analytics—all while demanding faster response times. The hyper-connected world we live in requires a shift in the way insights are found from data relationships.
Graph databases remove the time-consuming process of table joins, as these databases store data as pre-connected entities. The graph database model is an inherently indexed data structure that doesn’t need to load unrelated data for a given query—making it a fantastic solution for real-time big data analytical queries. This means that graph databases can be up to 1000X faster than relational databases! These pre-connected entities mean that analytics can go far deeper too; finding insights from data relationships that were previously unknown. These are big results that can translate to improved efficiency and productivity, as well as accelerated innovation.
The popularity of graph analytics disrupts many workloads, such as supply chain management, e-commerce recommendations, cybersecurity, fraud detection, and many other areas in advanced data analytics. In fact, Gartner believes that “By 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision making across the enterprise.”1
The cost of doing nothing
While analytics has taken on an increasingly significant role in all industries, many enterprises still struggle to make full use of their data without robust infrastructure and analytics tools. According to a 2021 Dataversity report2, 99% of data collected is never analyzed. And 89% of enterprises believe they risk losing market share and momentum by not adopting a data and analytics strategy.
Despite this, 69% of these companies have not created a data-driven organization, and 52% are not competing on data and analytics. Only 14% of enterprises make analytics broadly accessible to their workforce. All of these numbers mean that it is near impossible to meet the digital transformation efforts that are necessary for a data-driven business if you choose to do nothing—meaning lost value, lost insights, and ultimately lost revenue.
Gain back your competitive edge and create a comprehensive strategy for database analytics through HPE Pointnext Services. Our experts work with you to define business goals, identify possible roadblocks, and select the technologies that fit your business. Beyond traditional deployment and financing options, HPE GreenLake also offers compute, analytics capabilities, and support for this solution—all as a service. This consumption-based model means you only pay for what you use and can plan ahead for changes in capacity to avoid overprovisioning. With metering and capacity management, the resources required for each analytics workload are ready to deploy in minutes, not months.
Learn more from a real-world example
HPE, Xilinx®, and TigerGraph have partnered to obtain faster, deeper, and wider insights on connected data using HPE ProLiant DL385 Gen10 Plus v2 servers with 3rd generation AMD EPYC™ processors and Xilinx Alveo U50 Data Center PCIe accelerator cards. By utilizing TigerGraph’s massively parallel graph database, this solution is purpose-built for real-time analysis of data at an enterprise scale. It supports both transactional and analytical workloads, is ACID compliant, and scales up and out with automatic data partitioning. Many top companies are using TigerGraph today for fraud detection, customer360, supply chain optimization, and other applications.
Customers spanning healthcare, financial services, retail, and manufacturing are now able to leverage hardware accelerated graph and machine learning, technology that was previously limited to hyperscale technology companies.
To find out how this joint solution performs up to 48X faster than leading graph analytics solutions check out the following resources:
Infographic: Insight and Analytics to Transform Your Business
Solution overview: Accelerating Every Business with Insight and Analytics
Reference architecture: HPE Reference Architecture for Accelerated Graph Analytics on HPE ProLiant DL385 Gen10 Plus v2 Server
Tyler Hofstede
Hewlett Packard Enterprise
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1. Garner, Top Trends in Data and Analytics for 2021, 16 February 2021
2. Dataversity, The Role of Big Data in Business Development
Tyler_Hofstede
Tyler is the Compute Product Marketing Manager for HPE ProLiant rack servers. He brings technical and creative marketing ideas to life and has made a career out of translating technical jargon into relatable content.
