Once data and analytics leaders come to the conclusion that they are in need of new or replacement business intelligence or data analytics solution, an important decision needs to be made. There are two separate paths to take, and the only way to know which path is the right one is to do a bit of self-reflection. The two common business intelligence deployment methods are the full vendor stack and the best-of-breed approach.
Each has its benefits and drawbacks, but in an ever-evolving market where data continues to grow increasingly vital, choosing the one that fits best within your environment can make all the difference in the world.
Option 1: Vendor Stack
“Stacking” simply means to choose one vendor with which to use for a business intelligence framework. Many organizations choose to take this route due to its simplicity, as it provides familiarity to end-users who use the tool on a day-to-day basis. Another benefit of the vendor stack is that a singular vendor acts as the only point of contact for questions, troubleshooting and other issues that may come up. When we think about stacking, providers we refer to as mega-vendors come to mind, including the likes of Oracle, SAP and Microsoft.
In addition, stacking a single vendor’s tools on top of one another within your organization’s data architecture ensures compatibility in that you can be sure each and every tool will work seamlessly with one another. This is generally seen as one of the biggest benefits, as it limits the possibility that downtime will strike. The businesses that typically choose vendor stacking aren’t necessarily concerned with the more advanced functionality that best-of-breed approaches can offer, and hope to achieve a more general business intelligence platform from which to work from.
The vendor stack is seen by many forward-thinking market analysts as a legacy approach to BI, but there are also many companies who swear by the practice and the simplicity it offers them. Generally speaking, no single vendor can solve the entire equation. However, many enterprises don’t need some of the more advanced features that can be had via a variety of different tools. Vendor lock-in can be come a problem though, with customers becoming dependent on a single provider for products and services, unable to hedge their bets elsewhere without being inundated with unwanted costs and fees.
Option 2: Best-of-Breed
The best-of-breed approach allows data-driven organizations to go a step further in selecting specific tools that work best for their environments. In choosing to forego signing on with one vendor, businesses can choose vendors that may match up best within particular niche markets to solve industry-specific problems. Those who typically choose to go this route need more advanced analytical capabilities than vendor stacking can offer. In this way, many consider a best-of-breed approach a more modern way to do business intelligence, allowing enterprises to deploy the more agile and less bulky solution offerings for real-time and self-service analytics.
Though best-of-breed BI lacks an all-encompassing platform from which to work, they offer a greater degree of efficiency for companies looking to answer specific business questions, gaining the only the tools they need to execute analysis. Since most solution providers only do one thing really well (what we refer to as a vendor’s flagship offering), it just makes sense to a lot of organizations to deploy only the best possible offering for each job. With data sources and types becoming difficult for many legacy analytics tools to handle properly, plenty of companies are reacting and deploying a best-of-breed approach as a result.
Just beware: choosing a best-of-breed BI environment can come with its own headaches, most notable being issues with interoperability. This approach is best suited for experienced BI users and organizations.
The Bottom Line
The question remains how much business intelligence is needed? No vendor can solve the world’s analytics problems on their own, but at the same time, not every organization needs to answer niche questions that exist in only certain verticals. Customizing a data environment can become costly, while at the same time, vendor lock-in with an unwieldy solution provider can prove to be a giant headache. There’s no easy answer, and the only way to know which model best suits your organization is to map out how each route will impact individual users, objectives and business plans.