Think about walking into a new home. What do you notice first? Probably its appearance and functionality—those are the characteristics that most directly affect people inside, after all. It’s easy to forget someone had to first conceptualize the entire building, room after room, paying mind to how each part of the structure would eventually work together.
Well, any business intelligence (BI) system is only as solid as its underlying architecture. What’s at stake? The eventual user experience for business users, to start—not to mention data accuracy and trustworthiness. Your business intelligence architecture will also affect how often IT and data teams will have to troubleshoot and execute certain repetitive tasks, drawing them away from higher-level value they could be creating for the company.
Put it this way: When your business intelligence architecture is effective and well-planned, people will hardly notice it. They’ll be too busy seamlessly using BI to answer questions and uncover insights. But if your BI architecture is faulty, users notice the same hiccups and limitations surfacing again and again, causing frustration and wasting resources. Without great architecture, organizations start to miss out on the value their data could be providing them.
Here are five business intelligence architecture red flags to avoid.
Working with Redundant Data
Building BI architecture begins with databases. Companies today have a greater need than ever before to be able to pull data from disparate sources. This data needs to be cleaned up and stored correctly to be usable, too.
- Synchronizing data across multiple databases ends up being difficult
- IT and data teams have to waste time reconciling databases
- Users may get wrong answers depending on which database is queried
Building your BI strategy around redundant data is like building a home on a faulty foundation. Sooner or later, the cracks will begin to show. It pays to have data specialists take the time to organize, clean and sync databases up front so you can trust the data insights produced down the line. It’ll also save your data team time later, not having to manually connect databases and applications.
Inability to Work with Multiple Underlying Databases/Sources
Data comes from all kinds of sources, both internal and external. And all of this data offers potential value, assuming your system can mine it for insights. Business intelligence architecture that fails to work seamlessly with multiple databases and data sources will seriously hinder the insights a company is able to derive from stored data.
Allowing Siloes to Form/Persist
Siloes are a thorn in the side of any business utilizing BI. When information is siloed, it’s not widely available to all those who need it. When business users take a siloed approach to BI, they “prefer to stay in their own comfort zone,” which leads to them missing out on important insights.
How your organization approaches business intelligence architecture will affect the degree to which your data is siloed, and how accessible data insights end up being for employees. Many of the “behind-the-scenes” components end up making or breaking the usability of your BI.
Today, platforms like ThoughtSpot take a ground-up approach to architecture, starting with scalability and performance monitoring via a distributed cluster manager. Then there’s an engine for in-memory calculation, which is the tool capable of crunching billions of data rows in seconds. There’s enterprise-grade governance supporting the entire system, as well as data connections and APIs to bring together multiple data sources. And this is all before you even get to the tools front-end business users work with, like search- and AI-driven analytics, which are powered by an underlying BI & visualization server.
Each part in your architecture plays a role, and these different layers enable the eventual insight-gathering that empowers employees to make better decisions.
Before you start constructing the roof of a home, you need sturdy walls. And before you can erect walls, you need to make sure the ground floor is solid. And underneath the floors, you need a foundation that can support it all. The same goes for BI architecture.