Every business has moving parts. It does not the matter if you’re a one-person operation or a multinational conglomerate — there’s probably more happening than you can keep track of on your own. Thankfully, we have data — and analysis tools— to help us fill in the gaps where knowledge and expertise don’t quite cut it.

Business intelligence (BI) tools have played a huge role in helping organizations get more value from data. Having the best business intelligence puts you at a distinct advantage over your competition. However, the data itself is only one aspect of this equation. Beyond the conceptual and collection phases, data still needs to be stored, manipulated, and put into a useful form.

This is where business intelligence architecture comes into play.


What Is Business Intelligence Architecture?

In its most basic terms, it’s like a blueprint for how data travels through the various phases from collection all the way to being part of a report. A superior BI architecture allows organizations to reach actionable insights in an accurate, efficient way. It should go without saying a poor business intelligence architecture will do the opposite of this. Thus, it’s essential for enterprises to get BI architecture right.

Here are some tips to consider for your organization’s business intelligence architecture:

Connection With Cloud Data Stores –
In the past, enterprises didn’t need to think about cloud compatibility. However, this is a necessary consideration for businesses today. An enterprise architecture of business intelligence is going to be inefficient — and maybe unusable — if it can’t scale into the cloud. Technology and data storage are far more compatible with- and are being designed specifically for the cloud. Relying too heavily on a legacy, on-premise architecture is setting an organization up for failure as applications and usability continually migrate to a cloud-first world. Make sure your architecture — from sourcing to application layers — can scale into the cloud, also provide you with the ability to directly connect to your data in the cloud .

Security and Governance –
These should baked in at every level. Ideally, you should be able to provide interactive data and customized reporting while maintaining the form of your architecture. Further, you should not have to choose between making data widely available and overseeing a complex set of security management protocols.  Governed usage should be ensured across your organization through a centralized governance framework. Ultimately, your architecture should be capable of providing fine-grained permissions for millions of users and hundreds of thousands of security groups, in order to ensure ready accessibility, while also safeguarding your files such that only allowed users have access to the right set of data.

Scale To New Use Cases Seamlessly –
The architecture shouldn't require rebuilding when you want to launch a new use case or expand your use..

Encourage Data Democratization – Data democratization is the idea that more than just specialized data workers should have access to analytics tools. The benefits of this can be profound. In addition to relieving analysts and scientists from fielding few low-lever requests, democratization can create a data culture within an enterprise. Fostering this is valuable because it moves an organization toward actions based on measurement in every possible circumstance. When things are measured more thoroughly, they can be studied and improved more readily. Spreading this capability throughout your business will lead to radical improvements in operating efficiencies across the board.

Overstating the importance of a solid business intelligence architecture is nearly impossible. It is the backbone of your data program.

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