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Arpil 26th, 2024  |  4 min read

The Rise of Connected Data and the Connected Enterprise

What's covered in this article?
 

The concept of connected data is not new, but its application as a means of enabling deeper insights and more effective data utilization has yet to be fully realized.  This article explores the transition from traditional relational data structures to more dynamic, interconnected graph-based models.

What is Connected Data?

Connected data refers to an approach where data entities are interlinked with relationships, allowing for complex, network-like data structures. Companies like Google and Facebook utilize graph structures because they enable high levels of data interconnectivity and flexibility, supporting various data types and structures.

The Limitations of Relational Databases

Relational databases, while robust for specific uses, struggle with the complex data interconnectivity that graph databases excel in managing. This limitation arises because relational systems primarily organize data into tables where relationships are maintained through foreign keys and joins. This structure becomes inefficient and cumbersome when dealing with highly interconnected data or when attempting to uncover indirect relationships across large datasets. Graph databases, in contrast, are designed to naturally and efficiently map out complex, dynamic relationships, making them more suitable for scenarios requiring rich interconnectivity. Hence the reason why social media companies err towards graphs as opposed to relational databases.

Challenges in Adopting Connected Data

Despite the advantages, many companies have been reluctant to adopt graph databases due to perceived complexities and the significant shift required from existing data models. Not only does the shift require substantial initial efforts in data cleaning, normalization, and enrichment, but the supposed complexity of graph query languages compared to SQL can also be a barrier for teams accustomed to relational systems. Lastly, switching to graph is not just a technical transition; it also requires a mindset shift that requires rethinking how data is interconnected.

Artificial Intelligence (AI), Machine Learning (ML), and Connected Data

With so many organizations either investigating using AI and ML, or already doing it, the question of which database structure is best suited to developing custom Large Language Models (LLMs) and AI-driven applications arises. It probably comes as no surprise that the suitability of AI and ML to graph versus relational databases depends on the specific data and tasks. Graph databases excel in scenarios where relationships and network connectivity are crucial, such as recommendation systems or social network analysis. They allow for more complex, relational algorithms that dynamically adjust to the data's interconnected nature. Conversely, relational databases are typically better suited for structured data tasks that require high-speed transactions and straightforward query performance.

That said, connected data structures like graphs have an advantage in that they can be transformed or "downcast" to simpler, flatter structures if necessary. Relational structures, on the other hand, cannot be scaled up in a similar way.

The Connected Enterprise

The ultimate goal of connected data is the 'connected enterprise,' where data freely flows across departmental boundaries, accessible and utilizable by various business units without the traditional silos. This approach emphasizes data's role as a shared asset, enhancing collaboration and decision-making.
Adopting connected data practices, particularly graph databases, positions companies to better manage their data landscapes and derive more value from their information assets. While challenges remain, the potential benefits of a fully connected enterprise are significant, and it is our strong belief that this is the future direction most companies will take.

Key take-aways

Understanding Connected Data:
Connected data involves interlinking data entities with relationships, creating network-like structures that enhance data interconnectivity and flexibility.

Limitations of Relational Databases:
Relational databases struggle with managing complex data interconnections efficiently, making them less suitable for scenarios requiring rich interconnectivity compared to graph databases.

Challenges in Adopting Connected Data:
Despite its advantages, the adoption of graph databases faces challenges, including perceived complexity, significant data model shifts, and the need for a mindset change.

AI, ML, and Connected Data:
The suitability of graph versus relational databases for AI and ML applications depends on the specific data and tasks, with graph databases excelling in scenarios where relationships are crucial.

The Connected Enterprise:
The ultimate goal of connected data is to create a connected enterprise where data flows freely across departmental boundaries, enhancing collaboration and decision-making.

Future Outlook:
Adopting connected data practices, particularly through graph databases, positions companies to better manage their data landscapes and derive more value from their information assets, indicating a future trend towards connected enterprises.