Take a look around you and you’ll realize that we live in a relational world. You surf the web based on the connections between the different sites, you bring specific ingredients together to create savory meals, you get friends recommendations based on how their profile relates to yours. We continuously interact with relational databases.
Everything is relational … shouldn’t your analysis also be?
Organizations are collecting vast quantities of data from a multitude of sources and store them in databases (or equivalent format datasets). Data provides information, but it is the data structure that puts it into context and generate its real value.
Understanding interconnectedness, or how data is related with one another, significantly enriches your analysis because data can be described by their properties, as much as the properties of the data is it linked to. Consequently, analysis of raw data taken in isolation, that is without their context, is likely to lead to incomplete or misleading conclusions.
This is the reason why specialized tools or methods are developed to identify those patterns and trends adding context and depth to the knowledge extraction.
The main reflex associated to the analysis of relational dataset is called ‘flattening’, i.e. merging the data from different sources into a single table or dataset based on which analysis will be performed. In fact, this is what many of today’s analytical methods are expecting of you. It can be seen as the design of a view resulting from join operations.
Regrettably, this flattening of the data often results in a denormalized, wide structure. While this makes data analysis simpler, it can lead to data redundancy. One method addressing this redundancy is the use of aggregation operations, such as average, count, sum, min, max, etc.
However, in both cases, there is some form of information loss, and most importantly, context.
The value-added proposition of embracing relational datasets
Working with your original file structures not only remove the potential bias and error that can be injected in your model by manipulating data, it allow you to easily dynamically grow your models to follow your investigation paths.
Using relational data models enable you to take advantage of the knowledge you put into organizing your data the way you did in separate files and tables. Not only is it essential for effective data management, but it also ensures that the data is logically organized.
By ensuring data integrity, facilitating complex queries, and offering scalability and flexibility, relational data models provide valuable benefits in obtaining findings that are more significant to you. It infuses and enrich your findings with invaluable context while boosting the power of the knowledge extraction process.
Finally, embracing relational models enables you to perform multi-dimensional analysis by detecting associations from data included in different tables. The types of calculations involved quickly become extremely complex to achieve using traditional methods, while readily achievable in Peekaoo.ai therefore adding more depth to you analysis.
At peekaboo.ai, we are using state-of-the-art algorithms to handle relational datasets. Our platform leverages association rules combined with first-order logic and recursiveness to address the relational aspect of data, offering you unparalleled edge, such as:
No Flattening, Therefore Preservation of Maximum Information
By avoiding data flattening and join operations, peekaboo.ai ensures that your data remains rich and intact. This means that you can extract the maximum information without compromising on efficiency or sacrificing relational context.
Arbitrary Depth of Analysis
Not only Peekaboos.ai provides the links between the data included in each individual table, it goes further by enabling the discovery of associations between different tables, down into their associated tables, regardless of how nested they are. It is this depth of analysis that enriches your discoveries beyond what the traditional methods can currently provide.
Flexibility in Links Handling
Most of the data mining methods are focusing on a binary view of the links between data (if it exists or not), with Peekaboo.ai you can handle conditional and more fine-grained links easily.