What is Analytics Architecture?
Analytics architecture refers to the systems, protocols, and technology used to collect, store, and analyze data. The concept is an umbrella term for a variety of technical layers that allow organizations to more effectively collect, organize, and parse the multiple data streams they utilize.
When building analytics architecture, organizations need to consider both the hardware—how data will be physically stored—as well as the software that will be used to manage and process it.
Analytics architecture also focuses on multiple layers, starting with data warehouse architecture, which defines how users in an organization can access and interact with data. Storage is a key aspect of creating a reliable analytics process, as it will establish both how your data is organized, who can access it, and how quickly it can be referenced.
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Structures like data marts, data lakes, and more standard warehouses are all popular foundations for modern analytics architecture. On the user side, creating easier processes for access means including tools like natural language processing and ad-hoc analytics capabilities to reduce the need for specialized workers and wasted resources. When seen as a whole, analytics architecture is a key aspect of business intelligence.
How can I Use Analytics Architecture?
No matter what kind of organization you have, data analytics is becoming a central part of business operations. The fast-rising amount of data your multiple touch points collect means that using a simple spreadsheet is quickly becoming unfeasible.
Analytics architecture helps you not just store your data but plan the optimal flow for data from capture to analysis. Understanding these steps can give you a better idea of your hardware and logistics needs and clue you in on the best tools to use.
One important use for analytics architecture in your organization is the design and construction of your preferred data storage and access mechanism. Many companies prefer a more structured approach, using traditional data warehouses or data mart models to keep data more organized and easily sorted for access later.
Others prefer to keep data in a single storage structure such as a data lake, which comes with its own benefits but makes data slightly less accessible and organized. Regardless, your analytics platform architecture will largely define how your organization interacts with data, as well as how you gain insights from it.