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Why Aren’t Any Business Intelligence Apps Using Graph Data

Business intelligence isn’t always about the facts. Don’t get me wrong, there will always be a place for dashboards and BI reports.

Making business decisions without them is unimaginable so we all appreciate SQL based business intelligence systems with star schemas that point to meaningful fact values. One of the many lessons learned from the social media age is that there is value in relationships. Business value in fact.

What if micro-targeting for example wasn’t just a dark marketing strategy that violated privacy but rather a basic framework for understanding how our master data relates to big data? We could not only find out which products aren’t selling but also why they aren’t selling.

Maybe even connect to that audience we could not reach. This seems all but impossible in a SQL based environment with tables that struggle with large complex data.

There is another way however. Meet graph data, the technical bridge to understanding the why in your master data.

In a graph data model, the value is placed on the relationship rather than the fact. To date, enterprise applications that use a graph data model are primarily customized.

Tier one providers have not developed an intelligence solution that reaps the full benefits of this back end data structure. There are some great open source tools for graph databases like Neo4J.

There are also a few great cloud-based tools that can integrate with traditional IT stacks. Microsoft released Cosmo DB in the Azure cloud last year. Amazon recently launched Neptune this year.

Industries that rely on nonnumerical data are the key to finding where graph databases have the largest impact.

As an example, the following industries could drive a significant amount of value from nonnumerical data:

  • Healthcare
  • Education
  • Life Sciences

The opinions and recommendations of doctors run the day in healthcare. Graph databases can create relationships to these opinions in their diagnosis, treatment and even the demographic details of the patient.

Add shared access through a blockchain and our application is already feeling more intelligent. There is value in the relationships here though that increases over time.

There are plenty of applications for managing students. I’m not suggesting that those applications aren’t adequate. I am saying however that a graph database can add value with respect to the recommendations or opinions of teachers.

Suddenly a student’s past is more than their grades. Likewise, a 504 for example could travel with a student throughout their academic journey and be aligned to performance with adequate guidance from teacher to teacher.

Scientists are smart, some are even geniuses. Scientific fields constantly rely on conclusions that are based on relationships. Sometimes those relationships extend between scientific fields.

The relationship between chemicals and outcomes for example. The relationship between DNA and health, performance, and even risk.

There are a series of outcomes in the life sciences that are buried in the minds of brilliant scientists or written in amazing journals. Graph data can make that knowledge common and accessible for analysis and decision making.

There are other considerations in sectors like government. This was just a sample of how graph data can be used as a form of business intelligence.

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