As the world becomes more connected and systems more complex, the use of technologies designed to leverage relationships and their dynamic properties is essential. Today’s businesses face extremely complex challenges and opportunities that require more agile and intelligent approaches.
Enterprise Graph Framework for data scientists aims to improve predictions that drive better decisions and innovation. Neo4j for Graph Data Science integrates the predictive power of relationships and network structures into existing data to answer previously unsolvable questions and increase prediction accuracy.
OpenGov Asia had the opportunity to speak to him dr Alicia FrameSenior Director of Product Management Neo4jto gain their insights into Graph Data Science.
Alicia is Neo4j’s Lead for all Graph Data Science – working closely with Engineering to build a world-class connected data science platform, collaborating with clients and practitioners to understand how graphs can be put into practice, and educating the data science community on the power of connections.
Neo4j is a graphics company concerned with discovering connections within data to derive information. Without finding connections, data in and of itself may not have actionable meaning. Organizations need connections to understand otherwise isolated data points.
Alicia distinguishes a database platform from a data science platform. In a database, organizations can store their data and they can query and search for important things. Data science is about harnessing the connections between billions or even trillions of data points. Graph for Data Science uses these connections to find out what is important and meaningful.
Industry requirements for the use of data
There are three main requirements. First, as organizations have more data, the speed of access, retrieval and interpretation of data becomes important; whether it’s the speed of the query or how fast the algorithm works.
The second is expressiveness. The more data there is, the more important it is that the data represent something meaningful. In the context of a chart, companies need to structure data as it is represented in real life.
The last point is, the more data organizations have, the harder it is to know exactly what to look for in a data set. Having the tools to look for important patterns becomes crucial. Therefore, end-users can focus their value on what matters, instead of spending years sifting through useless information.
In OpenGov Asia’s interview with Nik Vora, Vice President, Asia-Pacific, he explains that graph technology is important because it can extract the inherent value of the data itself. The purpose of the technology is to store information without confining it to a predefined model.
Alicia agrees. A Graph Data Platform not only represents individual data points, but all connections between them. Storing data traditionally could lose this critical piece of information, e.g. B. the relationship between two people or objects. Graph Data Platform faithfully represents the data; the relationships and connections are preserved. When organizations access the data through a query or machine learning model, they still get the gist of the meaning without throwing out important information.
Graph Data Science
Graph data science is about letting the connected data speak for itself. An unsupervised method of the graph algorithm could be running to find the signal in the noise. Depending on how the data is connected, these nodes and concepts are most important.
It could also be based on the customer chart to show how the customer community interacts and the information is useful for segmentation.
Companies could go a step further by performing supervised machine learning on the graph. This allows them to predict how the graph will change in the future. With Graph Data Science, organizations can learn from the structure of the graph—not just the people they are connected to, but the graph as a whole. It predicts what relationship will form next. It’s about, from what to know, what to look for, to the emergence of what’s important and unusual, to predicting the future and what will change.
Knowledge Graph in Graph Data Science
dr Maya Natarajan, Senior Director, Knowledge Graphs, Neo4j believes that Knowledge Graphs are of great use to organizations to solve their business challenges. She says that a knowledge graph is unique because of its semantics. Semantics is one of the key components and benefits of knowledge graphs.
The semantics are encoded alongside the data in the chart itself. In this way, knowledge graphs bring intelligence to data and greatly increase its value. Essentially, knowledge graphs add value to data through semantics by adding more context.
Knowledge graphs are often implemented as the first phase in graph data science. Alicia thinks of a knowledge graph as a heterogeneous graph, or a graph that contains different types of nodes, such as people, places, and things.
The first step in performing graph data science is to create a graph. The vast majority of Neo4j’s clients start with a Knowledge Graph to know what information they have, how it relates to other concepts, and how it relates to their business problems.
Once they’ve created a knowledge graph, graph data science is all about figuring out what problems they’re trying to solve, what questions they want to ask, and how to turn everything they know into accurate predictions.
Transition from reactive to predictive models
Companies often start out in their reactive phase. For example, organizations only look for fraud when it has already happened and find out who is committing fraud. Alicia feels this approach is useful but limited as the ultimate goal is to prevent fraud rather than catch the scammers.
When it comes to predictive value, it means learning the kind of patterns that predict a specific outcome. In the future, organizations can know the patterns of certain characteristics to derive accurate predictions.
Alicia offers the example of predictive modeling by mentioning a large global pharmaceutical company. The company has an electronic patient record. For every patient they have data on, they could say that this is the sequence of events they observe on their journey to healthcare. You had all the connected data in one chart.
They are interested in taking this data and learning from this information: Who looks like someone who will benefit from certain interventions? Who Benefits From This Drug? And who would benefit from this drug in the future? Then they know what the chart pattern looks like for someone who will benefit from the drug. You can also find people with similar characteristics and intervene early to improve patient outcomes.
Finally, Alicia says that she has been using Neo4j for over 10 years. Neo4j is the first graph data platform that existed. and without a doubt, Neo4j was the first Graph Data Science Platform. In addition to the strong foundations of a database, there’s a super-powerful, enterprise-scale data science platform.
Neo4j has tested products on tens of billions of nodes to ensure their algorithm is ready, gives the right answer and is easy to use. When companies combine a mature, long-standing database product with innovative data science, they get all the predictive capabilities combined with the ability to process them. Neo4j meets the bar for maturity, scalability, speed and future completeness.
For more information visit https://neo4j.com/product/graph-data-science/
