Analytics vs (Operational) BI

Many businesses have embraced operational business intelligence, or what most people just call “business intelligence,” to become a more efficient, data-driven organization. For those not familiar with BI, it’s the process of continually providing operational data to all information consumers in your company so that operations can run smoothly, quickly and accurately. Operational data can be anything from sales orders, to production and inventory information, to delivery schedules or customer satisfaction metrics. Any data that is a part of your company’s operations can be used as part of an BI system.

But while many companies have embraced the idea of BI, many of them are also using the wrong category of tools to get the job done, opting for analytical BI software instead of operational ones.

Analytical BI tools, often called “data discovery” or just “analytics applications,” like Tableau and Qlikview are designed for business analysts to analyze data, then share their findings with small groups, typically on an ad-hoc or scheduled basis. These tools lack several key elements that are essential to an effective operational BI system.

In order for operational BI to be successful, it needs to analyze data on a continuous, if not real-time, basis. It also need to be able to take actions autonomously if the data warrants such action. Analytical tools simply don’t offer these automation capabilities. While reports can be generated and sent to key individuals on a periodic basis, there is no ability for analytic software to continually collect or analyze data or take any actions in real-time.

There is also no mechanism for effective information distribution, or [Intelligent Information Broadcasting](, to ensure the right information gets to the right people at the right times. Only with intelligent distribution will your entire organization have the information they need to be successful. Some companies suffer through these limitations by manually intervening in all of these different processes, but in the end, BI is supposed to be working for you, not the other way around.

Finally, there is the issue of licensing. Analytic tools aren’t licensed for enterprise-wide usage or self-service data applications. The more people who use your analytic tools, the more expensive they become. Operational BI systems, by contrast, encourage more people to use them, recognizing that the more prevalent they are in your organization, the more valuable they become. BI platforms not only encourage more users to consume information, but also to create it themselves through self-service capabilities. Fortunately, BI platforms also offer enterprise-caliber performance to make sure these self-service applications can scale up to meet the growing demands.

Data platforms like Qrvey bring BI into the modern age. Qrvey’s all-in-one platform is 100% cloud-based and includes data collection, analytics and automation functions. It allows informed decisions to be made at the speed of thought and is optimized for information distribution and self-service applications.