The incessant talk these days about Big Data in the context of business intelligence is understandable. The unprecedented growth and massive proliferation of mobile, social, website, transactional and other data is having a profound impact on the evolution of data management tools, processes and infrastructures.

To be sure, very large data sets can only be parsed, managed and analyzed using sophisticated solutions designed expressly for that purpose. These solutions have made it possible for thousands of companies, governments and other entities to catapult their BI activities to much higher levels of efficiency and effectiveness. Many organizations would be dead in the water without these solutions.

At the same time, it’s important to realize that most organizations are not managing data at the scale of a behemoth like, say, Exxon Mobil Corporation, Amazon.com or Wal-Mart – which, for its part, logs more than a million customer transactions each hour and feeds information into 2.5 petabyte databases. While the high-volume data processing needs of large companies often demand Big Data BI solutions supported by robust data management and infrastructure, Big Data does not even factor into the equation for the vast majority of midsize organizations.

The BI function is no less critical for managing day-to-day operations and driving success in midsize businesses, including nonprofit organizations and academic institutions. Like their counterparts in the Global 1000, decision makers in these organizations need to be able to access data from multiple sources on an ongoing basis. That’s how they track and measure performance and generate actionable insights that drive year-over-year improvement.

But with databases that likely consist of no more than a few million records, these organizations have no Big Data requirements. Why, then, try to sell them an over-engineered solution? Despite the mantra of solution providers eager to capitalize on all the buzz surrounding Big Data, most modern-day relational databases are perfectly well-suited to handling the volume of data that most organizations process on a daily basis. Running SQL queries against an average-size database can, in fact, yield enormous advantages compared to an ETL approach. Foremost among these advantages are speed and agility.

According to the new Gleanster benchmark report Agile Business Intelligence, almost nothing matters more when it comes to business intelligence than speed. Enabling continuous, on-demand reporting ranks as the number one reason that Top Performers are investing in agile BI solutions in the first place.

Business users want to know what they can do right now to drive increased efficiency and effectiveness, not what they could have done two or three weeks ago, when they first submitted the request to integrate a new data source into the BI system. The acceleration of decision-making cycles speaks to the importance of simpler systems that can run against existing databases without the cost and effort that’s required with a full-scale implementation. Putting data and self-service BI tools in the hands of business users allows them to get immediate answers to their questions and respond more quickly to business change.

Agility is commonly defined as the ability to be quick and nimble. Agility speaks to the fact that business users no longer wish to be merely passive recipients of reporting output. Instead, they want to be able to personally drill into the details of any given report — and also generate new reports on the fly. They want to uncover the underlying trends and identify additional opportunities that may possibly surface through data analysis and insights discovery.

So, while many solution providers are making a lot of noise about scale and size, and for good reason, for midsize organizations it’s not about scale and size. It’s about speed and agility. These organizations might think twice about adopting complexity when all they may need is a relatively simple and straight-forward solution that allows business users to run reports against databases already in use. In other words, there’s no reason to build a rocket ship to Mars when your travel plans call only for a road trip to Chicago.