Graph Data Modeling

Organizations today collect a large amount of data from ?many different sources. However, raw data is not enough. We need to analyze data for actionable insights that can guide us to make profitable business decisions. Accurate data analysis needs efficient data collection, storage, and processing. There are several database technologies and data processing tools, and different datasets require different tools for efficient analysis.

Data modeling gives us a chance to ??understand our data and make the right technology choices to store and manage this data. This is similar to how an architect designs ?a blueprint before constructing a house. In data world, business stakeholders design a data model before they engineer database solutions for their organization.

What is Data Modeling?

Every organized data set has a data model, even if it’s not explicitly defined. Every database has its building blocks (i.e. data objects, their associations, their governing rules) and a way of organizing them, like mapping out a diagram of how the data will be stored and associated with one another; this is the essence of data modeling. In other words,? it is a way to organize and define our data and the relationships in between, by giving it a structure. Ultimately, the creation of an abstract model like such provides guidance on how the actual database will be built.

The process of creating a model for the storage of data in a database is termed as data modeling. It is a theoretical presentation of data objects and associations among various data objects.??? Data modeling?is a process of formulating data in an information system in a structured format.

Benefits of data model include

? Reduces errors in database software development

? Facilitates speed and efficiency of database design and creation

? Creates consistency in data documentation and system design across the organization

?? Facilitates communication between data engineers and business intelligence teams

Data modeling occurs at three levels— conceptual, logical and physical

?? A?conceptual model?identifies the high-level, user view of data.

??? A?logical data model?sits between the physical and conceptual levels and allows for the logical representation of data to be separate from its physical storage.

??? A?physical model?is a schema or framework for how data is physically stored in a database.

Data Model as an ER Diagram

Data modeling in smaller terms is a ER diagram , entity relationship diagram. They way in which tables are connected in Schema terms. Or the way in which facts and dimension tables are connected with key constraints in data warehousing terms.

Here is an example of Movies and Person ER (Entity Relationship) Diagram

Data Modeling is not a one-off activity

Change is inescapable. Users’ needs change and so do business requirements– businesses typically ??do not abide by one schema. Depending on which model you are using, addressing those changes can make a significant difference. Using the relational model, a schema migration is performed every time it becomes necessary to update the database’s schema. Given the inevitability of constant changes in user needs, this task can get quite involved and time-consuming. Also, keep in mind that data preservation is not always guaranteed upon making schema changes.?Data modelling disadvantage can be summarized :

??Development of a data model is a very tedious job. One should be aware of the physical characteristics of the data storage.

??This system involves complex application development and knowledge of biographical truth.

?? The model is not quite user-friendly. Small changes induced in the system require major modification in the entire model.?

Why Graph Data Model?

Typically, when designing a data model, people draw example data on the whiteboard and connect it to other data drawn to show how different items connect. The whiteboard model is then re-formatted and structured to fit normalized tables for a relational model. With the advent of the relational model and ??normalization, data modeling became a more ?technical part of software engineering.?

A similar process exists in graph data modeling, as well. However, instead of modifying the data model to fit a normalized table structure, the graph data model stays exactly as it was drawn on the whiteboard. This is where the graph data model gets its name for being ??"whiteboard-friendly".

The ability to easily whiteboard your data model makes the graph data model incredibly simple and visual. There is no need to draw up business model versions or explain ER terms to business users. Instead, the graph data model is easily understood by ?anyone.

So, instead of dealing with the hassle involved with ??schema migrations and a ??rigid schema, why not use a flexible schema that adapts to changes dynamically–a graph data model? After all, they are designed to work with constantly evolving needs while preserving your data and maintaining its integrity– crucial for fraud detection applications.

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What is Graph Data modelling?

Graph data modeling is a technique superior to traditional data modeling for both relational and graph, document, key-value, leveraging cognitive psychology as well as AI to improve data designs.

Let us look at an example to demonstrate this. In the whiteboard drawing below, we have a data set about the movie "The Matrix"

Graph Data Modeling sets a new standard for visualization of data models based on the property graph approach.?Property graphs?are graph data models consisting of?nodes?and?relationships. The?properties?can reside with the nodes and / or the relationships. The Matrix movie Graph model after adding labels and properties looks like this :

This how the knowledge graph visually looks like :

Cognitive computing (including semantics and machine learning) is rapidly evolving, within metadata capture and automated data modeling. This could change the “Analyze” phase from being explorative and people-oriented to be a ??more automated "data preparation" approach. Automated discovery of relationships is the “next big thing” in data modeling, and ?graphs are excellent for visualizing relationships.

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