The business intelligence insights your organization has in all the data it stores can lead to game-changing opportunities–if your analytics system has the power to uncover them. Traditional data analytics are often maxed out by big data, unable to return results in a timely fashion, resulting in missed business opportunities. Business and marketing leaders can’t execute on new ideas to generate more revenue because IT can’t support their requests to add new data sources to existing queries.  

The race to draw more value from rising mountains of data has already begun. And while the marketplace for analytics systems that can handle big data volume is still developing, sitting on the sidelines isn’t an option. These six guidelines can help you find the right big data decision analytics system for your organization:

1.       Run a discrete pilot program. A formal pilot will prove the value of the investment in a powerful way, and let you discover idiosyncrasies in your data or architecture that you’ll need to accommodate before full rollout. The pilot must provide real value in solving a critical data problem without disrupting the day-to-day business.

2.       Concentrate on a single data issue (or a small cluster of issues) that has momentous business impact. Limiting scope to a single problem will let you arrive at the right solution more quickly, and will prevent unproductive complications. Moreover, a high-impact project will help secure enthusiastic support.

3.       Work closely with line of business managers and let them drive the requirements. Rely on business leaders to determine when the new solution is achieving the desired results and performance levels. Tap their insight about what the enterprise needs today, and how they expect those needs to evolve.  

4.       Use live data. It may be a pilot but it is not experimental. Get real results whenever possible so you can see how the solution will perform in production.

5.       Consider scalability issues and infrastructure preferences up front. Architectural flexibility can be critical to an analytics solution. Seasonal data spikes, for example, are a good reason to seek a solution that’s compatible with cloud system architecture or one that can use off-the-shelf hardware.

6.       Examine the complete workflow. Make sure that the solution you choose fits into your existing business intelligence environment and will not require IT or business users to be retrained in the tools they currently use to interact with data.

The massive amount of data that organizations are storing means that each has incredible business intelligence potential. But turning that potential into competitive advantage requires acting now to put next-generation data analytics in place.

This post is adapted from a longer article that appeared in the Discover Performance newsletter. Sign up today to receive more actionable insight that can help you turn IT performance into business success.

To learn more about how next-generation data analysis can be a critical differentiator in today’s fast-paced business world, visit the Vertica site.