In this blog, we will discuss how your BI platform fits within your overall data architecture, as well as five questions you should ask to select the best BI tool for your existing—or desired future-state—environment.

With so many choices available, it can be overwhelming to decide which BI tool is best for your organization—and not just for today, but for the future as your business needs evolve. It takes more than considering which BI tool is the right tool for your analytics needs; you need to look at how the tools fits into your overall data architecture.

Locking yourself into a BI platform that isn’t a clean fit within your data architecture sets up development hurdles before the process even begins.

How Does Your BI Platform Fit Within Your Overall Data Architecture?

Data architecture is the foundation you need to maximize the value that you can get out of data. It guides your organization’s entire data life cycle—including how your data is collected and from where, how it’s integrated, enhanced, stored, and ultimately delivered to business users across your organization.

You should not think of your analytics tool as a standalone component, but rather an integrated part of your architecture.

Analytics is one of many direct, downstream components of data architecture so it is critical to assess how your analytics platform fits in with everything else. You want to ensure that the tool you choose on the front end can seamlessly integrate on the back end to avoid negative performance implications.

Graph representing conceptual data architecture and the relationships between sources systems, ingestions tools, data storage, data transformation tools, and BI tools.

A modern data architecture represents all stages of the data lifecycle, from original data source to reporting—all the way through to analytics.

Five Questions You Should Answer About Data Architecture Before Selecting A BI Tool

1). What available software licensing do you currently have within your data architecture?

Taking full stock of—and fully understanding—your software licenses throughout your entire tech stack is such an important exercise. You should know what you are paying for, exactly what you can get out of your license agreements, and if you’re taking advantage of things already available to you.

For example, if you have a Microsoft E5 license for your enterprise solution, you already have access to Power BI without additional cost. In other cases, the exercise may reveal that your enterprise solution might not have any bearing at all on your BI solution.

What is the downside of not taking stock of your license agreements upfront?

The lack of awareness around what your software licenses provide will cost you time and money down the road.

We worked with a client that had made a sizable enterprise software investment to help optimize their current data infrastructure. It wasn’t until after they were already deep in the implementation that they discovered the vendor licensing they were working toward was already in place in a different business unit of the organization—a costly mistake that could have been avoided.

2.) Does the BI tool you’re interested in play well with the cloud vendor you use (or are moving to)?

If you are building your data stack on Azure, AWS, GCP, or Snowflake, you should think about how your desired BI tool fits into each of those architectures.

Native functionality that you expect from BI tools like Power BI, Qlik, Looker, and Tableau not be available if using a misaligned cloud vendor. Vendors that prefer open-source connectivity likely won’t be problematic, but those that want you to stay within their vendor stack may not make it easy to connect a BI tool that’s not part of that modern data stack.

If you’re already on the cloud, are you happy with what you have, and do you see yourself scaling with that vendor? If so, look at which BI platform will perform best in that environment. If not and you want to start over, then you have a clean slate to work from. Although most BI tools are cloud agnostic, some work more optimally with a specific vendor.

What is the downside of not considering a cloud vendor before selecting a BI platform?

It requires time, effort, and resources to spin up a BI solution and project plan for implementation. Without considering your cloud vendor first, you risk your solution being semi-functional when it’s time to connect to your cloud data warehouse.

We worked with a client that was in the process of migrating from their legacy tool to a modern analytics vendor. Upon discussing their migration plan, we discovered that there was major misalignment with their cloud data warehouse vendor. As a result, they had to select an entirely new BI platform and push their go-live date.

3.) Do you want to take a single vendor or modular approach to building your data stack?

You should decide upfront if you will be taking an all-vendor data stack approach—where every part of your data stack is tied to one cloud vendor, or if you’re considering a modular approach—where you have a multi-cloud environment and each tool serves its own purpose, mutually exclusive to any other tools within your data stack. Answering this question upfront will allow you to better navigate and plan for licensing costs as well as any other costs associated with scaling and using your BI platform as your needs evolve.

What is the downside of not considering your preferred approach to building your data stack (short- and long-term)?

Your implementation is guaranteed to be a disaster. If you’re not taking the time to consider your preferred approach, then you’re better off deferring the project. Tool and software selection can’t be reactive in nature.

4.) Where will your business logic live?

Traditionally, business logic lives somewhere universal and accessible, like a data warehouse, and a BI tool is built on top of that. But not every business has the time, budget, or skillset internally to invest in a data warehouse, and not every use case requires that level of data management. There are instances where you can feasibly build your business logic within your BI tool where this makes sense (think low data volumes, minimal complexity of transformation logic). Determine your approach at the outset to ensure your solution can scale appropriately over time.

Learn How to Get the Most Out of Your Tableau Environment—It Starts with Data Management

What is the downside of not considering where your business logic lives?

This creates an enormous risk for technical debt. As your data volumes and user base expands, scalability becomes a chief concern. If your transformation logic lives entirely within a visualization tool, and that solution scales significantly, eventually it will hit a ceiling of optimal performance. At that point you will be forced to rework your solution to fit to a new architecture to help mitigate the performance degradation. Upfront planning on how your data volumes are expected to scale over time will help to address this before it becomes an issue.

5.) Will your desired BI tool require additional training to use?

Your in-house skillset should be a key consideration in selecting a BI tool. It will determine if you can hit the ground running, or if you’ll need to dedicate resources to training and allow for a learning curve. The more niche the tool you select, the harder it will be to feasibly hire to support development. Broadly accessible, well-established vendors in the analytics space are the safest bet here. Training and support communities are generally well established for these tools if you adopt them, and the skillset required will be significantly easier to hire for.

What is the downside of not considering in-house skillset?

Without proper consideration around your staff’s skillset, you risk adopting a BI tool that is significantly difficult to train on or hire for to support. Remember that this is as much a people and process initiative as it is a technology one. You must have a reasonable degree of confidence that you’re adopting a data stack your team can manage and develop with relative ease. Adopting tools and technology that present a high barrier to entry for your data team will only elongate and delay the development process.

Keep the Basics in Mind with All Your Data and Analytics Needs

As you get ready to adopt a new BI platform, start with the basics. Examine your data strategy and update it to make sure it aligns with your current business objectives. As you’re assessing BI tools, do so side-by-side with the capabilities of your data stack—think about what is needed to not only meet your current needs, but also what you need to do to get to your desired future state. All of this is essential to both selecting the right BI tool for your analytics needs as well as transforming your business with data and analytics.

Kevin Lobo Kevin is our Managing Director of Analytics and is based out of our Chicago office. He leads our Analytics service line, overseeing its strategic direction and messaging, while ensuring delivery of high impact analytics solutions for our clients. Outside of work, Kevin enjoys spending time with his wife and daughter, as well as running and live music.
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