Neo4j help organizations unlock power of deeper context and relationships with knowledge graphs: Rahul Tenglikar



Neo4j enables organizations to unlock the business value of connections, influences and relationships in data: through new applications that adapt to changing business needs, and by enabling existing applications to scale with the business. Headquartered in San Mateo, California, Neo4j has offices in Sweden, Germany, Singapore, and the UK.

Powered by a native graph database, Neo4j stores and manages data in its more natural, connected state, maintaining data relationships that deliver lightning-fast queries, deeper context for analytics, and a pain-free modifiable data model.

Rahul Tenglikar, Regional Director, Neo4j, India, tells us more. Excerpts from an interview:

DQ: How does Neo4j help organizations unlock the power of deeper context and relationships with knowledge graphs?

Rahul Tenglikar: The amount of data that organizations generate and collect in today’s world is humongous. However, collecting this data is not enough to attain maximum advantage in competitive markets. Organizations need to mine this data in a way that enhances their decision-making, supplemented with intelligent insights based on the data collected.

DQI Bureau | DATAQUEST Rahul Tenglikar.

Neo4j’s connected data platforms combine strong graph data science capabilities with a core graph database as well as visualization and management tools. It stores connections in the data natively and this enables faster traversals since there is no need to calculate connections during the query execution time which is typically done by joins in relational databases. This allows organizations to scale both vertically and horizontally by taking the query execution time out of the equation. This is particularly useful in use cases that involve connected data.

Knowledge graphs are of particular interest and are typical use cases for Neo4j. When you store connected data in Neo4j and add context to it, it becomes much more valuable. For ex – take the case of a car mechanic. If he takes time to review the reason for your appointment, studies the past history of the car, servicing timelines, studies the model and everything else related to the car, it becomes easier for him to provide you with contextual feedback on the car and next steps around it.

Knowledge graphs provide deep valuable context and add intelligence to data by leveraging context and relationships, help arrange data in a single location and assist users in finding data that is relevant to them.

DQ: How can knowledge graphs be integrated into existing databases?

Rahul Tenglikar: The purpose of knowledge graphs is to store information without restricting it to a pre-defined model. Graph technology can extract the inherent value of your data, and helps you draw relevant insights and analytics from it. It leverages the relationships between data sets – information which is highly predictive but has historically been challenging to process on a large scale.

Knowledge graphs can identify these relationships, which can be incorporated for data assurance and insights into analytics and machine learning workflows.

Inherently, graph technology takes all your data – structured, unstructured, or semi structured – and helps map it and draw preliminary connections which provides insights and an understanding. Lastly, semantics are applied to provide deeper context to that connected data, giving out powerful, relevant and impactful insights.

Technically, Neo4j’s graph data platform provides integration APIs to most of the standard upstream and downstream applications for integration.

DQ: Can you share some use cases and areas where knowledge graphs can add value?

Rahul Tenglikar: Knowledge graphs are designed to be able to identify relationships between data sets and present them in a simple, understandable model. It is an efficient, seamless way to make sense of large volumes of data with multiple nodes. Any data that involves three or four hops within its data set will become a perfect candidate where knowledge graphs can add value.

Some use cases that help us understand this better:

Financial crimes: Knowledge graphs can detect patterns that are beyond the power of a relational database. This helps enterprise organizations combat a variety of financial crimes including first-party bank fraud, credit card fraud, ecommerce fraud, insurance fraud and money laundering – and all in real time.

Governments: Governments are using graph technology to fight crime, prevent terrorism, improve fiscal responsibility, and provide transparency. These solutions involve connecting data across different applications or repositories, spanning disparate processes and departments.

Telecommunications: Another area that is gaining momentum is telecommunications where graphs are helping manage increasingly complex network structures, ever-more-diverse product lines and bundles, or customer satisfaction and retention in today’s competitive environment.

Retail: Neo4j has enabled retail giants like eBay to transform their businesses, providing their customers with routing recommendations, personalization, product recommendations and promotions, all in real time. It enhances recommendation engines which are the core drivers of both user experience and revenue for any retailer today.

Real-time recommendations require data products that connect complex buyer and product data to gain insight into customer needs and product trends. This cannot be achieved with relational database technology, and this is where knowledge graphs come into the picture.

Drug discovery: Neo4j has enabled companies like Novartis to extract novel insights about relationships between biological and chemical data to accelerate drug discovery. We have also empowered companies like Monsanto to track genetic relationships in corn to breed better crops and feed the world’s growing population.

DQ: What are some of the trends you anticipate on this front, going forward?

Rahul Tenglikar: Organizations have realized how graph data platforms can help them unlock the true potential of their data. Going forward, we will not only see an increasing adoption of this technology, but also its proliferation and adoption across more industries. According to Gartner, 2022 will see almost 100% increase in graph adoption across industries.

Consequently, at Neo4j, we expect to see customers utilizing our wider platform capabilities, like Neo4j Aura DS (our fully managed cloud database as a service along with graph data science libraries on top of it), to identify solutions to some of their pressing challenges and make sense of the huge chunks of data that they sit on.

For complicated decision making or use cases like frauds, traditional approaches don’t prove to be ideal since they are unable to navigate through highly interconnected data problems. This gives graph data platforms a critical advantage and will serve as a key reason behind its speedier adoption across industries.

DQ: How are you doing regarding gathering talent?

Rahul Tenglikar: On the talent front, we anticipate an increase in the number of ’citizen data scientists’ – employees who work with predictive/prescriptive analytics models. Data science is one of the fastest-growing fields. With the current “great reshuffle” in the workforce, organizations will need to make data science more accessible to help fill gaps on their teams.

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