The Data Dilemma: Shifting From Collected to Connected Data

Data is the fuel of today’s digital world. Across all verticals, data is highly regarded as a commodity that can be leveraged for actionable insights. With the pace at which the world is digitally transforming, the amount of data we have generated has grown exponentially. The rapid development of the digital world has undoubtedly changed the definition of the word data in our minds to fit the context of today. 

Now, data generation and collection are nothing new. Since the day we were born, we have been generating data while organisations have been collecting and storing that generated data. Collected data is precisely what the word entails. It is the data collected either from an organisation’s customer base or data bought from other parties. This collected data resides in your company’s databases until you decide to do something about it. Business leaders have been so enthralled with the idea of big data and insights that they have invested a lot of money to funnel data into their data lakes while questioning why there have been no good outcomes. 

In its basic form, collected data is just data. It brings no added value to organisations in any shape or form as it gathers dust and increases your data storage costs. But, if you were to find somehow a logical way to establish the relationship between data points, you are on your way to reaping the benefits of big data. 

Let me explain this with an analogy. In the gold mining business, one must mine the metal before processing it and turning it into jewellery for people to adorn their bodies. It undergoes a process to get to the end stage in which it is desirable for consumers. Side by side with the data process, the mining and attainment of the raw material can be compared to data generation and data collection. When it gets to the end stage, that is what connected data is—data that adds value to your organisation. 

Definitively, connected data is the relationships between data elements. Now it’s not simply the relationships that you can establish using JOIN tables, no. Connected data means the ongoing maintenance of relationships without the need for queries to be made. Why is connected data so important? It must be stated that the value of data does not rely on large amounts of disparate data but the meaningful relationships between said data. 

This leads us to the question of how. While beneficial to address the issue of codifying table forms, relational databases (RDBMS) are ill-equipped to handle our data relationship needs due to their rigidity. With the amount of time, effort and resources it takes to create JOIN tables and queries, relational databases are not practical. Then you have NoSQL databases that, while are a step above relational databases, are still too complicated and non-cost effective to create data relationships as they focus more on data collection and aggregation. With the current fast-paced environment of today’s ecosystem, organisations cannot afford to depend on rigid solutions that are not equipped to adapt to changing conditions. 

Where ever shall we find the right type of database to fulfil our needs? 

Enter the graph database. In contrast to other forms of databases, the graph database focuses on data relationships and presents them in the form of connected data. In a graph database, connected data is modelled as a property graph that connects nodes (data element) containing attributes and properties through relationships. A significant aspect of this type of database is its ability to adapt to the changes within data relationships without much of a hassle. With an excellent overview of data connections, organisations can finally leverage the power of data. Using a graph data platform means you no longer need to worry about disparate data in your data silos going untouched.

Graph databases are not particularly new inventions in databases but there has been an increasing interest in them since the uses of data have expanded. Among the well-developed graph data platforms available on the market is Neo4j which goes beyond a primary graph database to providing a full suite of tools and applications to make sense of data. 

In your journey of shifting from collected to connected data, you need a graph-native database that can connect data as it’s stored with the ability to handle queries at fast speeds that Neo4j can provide. 

What we’ve discussed in this piece has merely scratched the surface of graph databases. Stay tuned for more articles on the ins and outs of graph databases.
 

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