When you look at a building, you register the parts you can see and use: its appearance, size, windows and doors, rooms and etc. But there’s a lot more involved than initially meets the eye — like the foundation and the infrastructure holding it up. Architecture must take all these factors into account during the design process — even while people who use the building focus only on what’s visible and relevant to them.
Business intelligence (BI) architecture is similar in this fashion.
Most employees within an organization will interface with specific BI tools — like search- and AI-driven analytics engines. Meanwhile, supporting the front-end functionality of these tools is an entire full-stack architecture.
Here’s more on what business intelligence architecture is and why it matters so much to your data strategy.
BI Architecture Brings Data Sources Together
One challenge businesses face is figuring out how to merge disparate data sources — like a retail company with an Enterprise Resource Planning (ERP) platform, Customer Relationship Management (CRM) platform, marketing platforms, staffing records, social media, and miscellaneous spreadsheets. The goal is to allow users across the organization to query data related to customer behavior, product performance, internal operations, and more.
As one CEO writes for Inc., organizations must “make the transition from spreadsheets and the convoluted web of systems, databases and documents” to cohesive BI architecture if they hope to reap the benefits of efficient data analysis — like speedier, more informed decision-making.
BI platforms like ThoughtSpot featuring full-stack architecture can bring together many distinct data sources — internal and external, cloud or on-premises — into one unified framework through data connectors and application programming interfaces (APIs).
The result? Front-end, self-service BI tools can mine data from these sources. This allows users at every level to access all the data insights they need to make decisions and drive business outcomes. Another benefit is the alleviation of cumbersome data silos and IT reporting backlogs that crop up when systems lack cohesion.
Architecture Affects Data Analytics Scalability & Security
The scalability and security of your data analytics strategy depends greatly on the framework established by the BI architecture.
We discussed earlier how full-stack architecture brings together data from many sources. This is positive, but it also creates a greater need for enterprise-grade governance and security. In terms of security, administrators need a way to control data accessibility throughout the organization —sometimes even down to the granularity of a single data object, row, or column.
Then there’s the matter of how many people can use your analytics tools, and how much data gets incorporated into the system. Scalability (or a lack thereof) starts at the foundation of your architecture. The goal is to seamlessly add users and data sources as you go without introducing hiccups in the user’s experience. Building your BI architecture on a distributed cluster manager scales out your data strategy without manual intervention from administrators.
Powering Your BI Tools
Employees interface with search and AI analytics tools directly; kind of like typing a query into a search engine. What they don’t see are the underlying servers powering those front-end tools.
Say someone on the marketing team enters a query. The results come back in the form of an interactive chart, which this employee can embed into a shared workflow and use to drill into what the data is saying. However, what they may not realize is there is an underlying BI and visualization server supporting this tool — helping them generate queries and charts.
The same principle applies to an AI-driven analytics tool. Employees can click to uncover insights — but the component of the architecture that’s facilitating this query is an in-memory calculation engine that can mine billions of data rows in seconds to identify insights.
Business intelligence architecture important because it is the overall framework that supports data analysis.
