Data-driven Business Decisions – Database Structure Can Harm or Help

Data-driven Business Insights

Having multiple data sources to access and work from can be frustrating and time-consuming. For many companies, this abundance of information needs to be accessed individually, which is also known as the point-to-point model.

Furthermore, if you haven’t consolidated your data, you won’t be able to scale as you increase the data in your company, for fear of putting your existing foundation at risk.

Here are some models that can help you understanding the value of consolidating your data to gain the best insights and streamline your business processes.

Point-to-Point Model

The point-to-point model is a simple layout in which multiple tools share information between them. This is a typical model for startups and businesses in their early development stages.

Point-to-point is competent on a small scale, but as you increase your data diversity, it gets more complicated and tedious for employees to request data from each individual source.

Hub-and-Spoke Model

The hub-and-spoke model uses an approach in which all your data sources are centralized, so you have the data consolidated in one central location for multifaceted data management. This model simplifies the connection process and is easier to scale.

Here are some advantages with the hub-and-spoke model:

  • Data visibility: A centralized location provides easy viewing of all your data.
  • Efficient: With a central location, you’ll be able to find the data you need quickly, rather than hunting for it from multiple locations.
  • Scalable: The centralized structure of hub-and-spoke is much simpler to scale than point-to-point, which only becomes more complex.
  • Big-picture insight: Having all your data in one place gives you a full view of your data picture for more in-depth insights.
  • Security: The centralized connection point makes it easier to monitor who has access to your data, and when.
  • BI tool integration: You can connect data sources in real-time for use with reporting and analytics tools.

Here are some examples of hub-and-spoke models that can be used to consolidate your data sources.

On-Premise Data Centers

On-premise data centers, or a private cloud, work well for organizations with sensitive data that need more control over access. This option is more secure, since data isn’t shared with other companies, and it’s customizable to your exact needs.

On-premise data centers can also be combined with cloud services to have the advantages of control, flexibility, cost-effectiveness and ease of transition.

Public Cloud

Public cloud vendors can consolidate your data as well, but they do so without the hardware setup costs. Public clouds let you pay for only the service you use, and the service provider does the maintenance. They’re also more scalable and reliable.

Data Storage Solutions

There are two popular data storage solutions:

  • Data Lake: A data lake is a cloud solution for stored data that connects and aggregates different data sets. Because you can store structured, semi-structured and unstructured data, this option is much more flexible than other options.
  • Data Warehouse: A data warehouse is a database system that stores all your data and consolidates the data in a central location, which can then be processed by a BI tool. A data warehouse needs a schema model in advance, which offers its own benefits to performance and efficiency.

Conclusion

Data consolidation, from point-to-point to hub-and-spoke, is vital to increasing your data volume and diversity in the future. Regardless of which option you choose, data consolidation will help your business gain better insights to make informed, data-driven business decisions.

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