Graph analytics, unlike data analytics, deals with and focuses primarily on analyzing the relationship between entities such as customers, products, and devices rather than digital data. Due to this unique capability, it is turning out to be a crucial driver in fields like social media analysis, fraud and risk management, and supply chain optimization. Also, leading organizations are leveraging graph analytics to drive the effectiveness of their social media and marketing campaigns.
Graph analytics makes it possible to derive actionable insights from increasingly complex data sets, making it an indispensable tool for today’s businesses. Speak to an expert to learn how you can benefit by incorporating graph analytics in your decision-making process.
What is graph analytics, and how is it different when compared to other analytics types?
With unstructured data proliferating across industries, businesses are exposed to different types of digital data sets generated from both internal and external sources. Traditional data querying tools lack the ability to blend these complex data sets at scale. With the help of graph analytics, decision-makers can integrate data from various sources and find correlations between disparate data points to build context and offer insights to make informed business decisions.
Graph analytics is a new area within data analytics. It refers to a combination of analytics methodologies that connect diverse entities like people, places, and things.
Graph analytics offers valuable insights that drive decisions in the following fields:
- Helps identify fraudulent transactions and predict financial crimes
- Prevents cyberattacks on computer networks
- Enhances route optimization across industries as well as for supply distribution chains and logistics
- Aids medical research and treatment analysis
As graph databases evolve with the rise in big data, graph analytics and graph technology have proven to be immensely useful in solving complex relations between entities, finding and connecting data with other entities, and analyzing the quality of relationships. The increased adoption of graph analytics across industries can also be attributed to its ability to find correlations between entities, which are otherwise impossible using traditional tools. Given its wide-spread use case across fields, including social media analysis, fraud detection and prevention, process management, and medical research, more and more organizations are poised to capitalize on this technology to accelerate digital data preparation and insight generation. Quantzig has the expertise and skill it takes to design and build advanced analytic solutions tailored to your business needs. Reach out to our experts for more information on graph analytics and its business benefits.
What are the top uses cases of graph technology and graph analytics?
1. Machine Learning
Machine learning is a common term across industries. Given the wide-spread nature of its use cases in the current business scenario, this technology is more accessible to businesses today. Open-source machine learning and NLP frameworks empower developers and data scientists with the tools and insights required to benefit from deep learning use cases such as image recognition, automated speech recognition, and intelligent chatbots.
Since graph analytics fundamentally represents the connection between entities, it is now being used to power a widely used application of machine learning, i.e., recommendation engines. By tracking and analyzing co-occurrences and frequencies between product data, customer data, and social information, companies can build and use smart, real-time recommendations engines.
2. Fraud Detection and Prevention
With businesses getting accustomed to the shift and industry-wide digital transformations due to COVID-19, most companies face fraud detection and prevention challenges. Though there are several ways through which digital fraud can be prevented, graph analytics is turning out to be extremely useful in helping businesses detect and prevent fraud. As such, businesses are capitalizing on graph technology to analyze large amounts of transactional data and model data relationships at scale with more flexibility.
3. Supply Chain Visibility
Supply chains are now a complex interconnected mesh consisting of various nodes, thanks to rapid globalization and the evolution of digital technologies. With businesses sourcing raw materials and other supplies from different global suppliers, the supply chain network is a complex interconnected system similar to an interconnected graph database.
Graph analytics enables businesses to model complex relationships inherent in modern supply chains while also enhancing the scalability and agility required to adapt to growing networks and perform quick searches on data. The end result is enhanced supply chain visibility and transparency into supply chain functions, which can help fine-tune and streamline supply chain operations.
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As graph databases evolve with the rise in big data, graph analytics and graph technology are becoming imperatives for analyzing complex data sets. The increased adoption of graph analytics across industries can also be attributed to its ability to find correlations between entities, which are otherwise impossible using traditional tools.
Also, with organizations focusing on expanding their data analysis capabilities, the ability to derive insights in increasingly complex ways from graph databases has made graph analytics a key differentiator and a must-have capability for businesses looking to gain a leading edge in the new normal.