FORTUNE — Customer satisfaction is the number one metric organizations need to consider when it comes to increasing retention and growing profit margins. Organizations that have dedicated resources to improving the customer experience are beginning to realize the value of the massive amounts of data they are managing. What’s fascinating is that most don’t even realize that they’re dealing with Big Data.

From decoding multiple-choice questionnaires to analyzing sentiment and suggestions in open-ended comments in addition to years upon years of historical customer data, Big Data technologies and tools are essential to improving the customer and partner experience.

The power of Big Data only becomes valuable if the customer sees the benefit as well. Analyzing data in real-time through machine learning and dynamic model construction allows technology companies to begin to predict what happens in customer environments and take action accordingly.

Typical indicators of performance include product quality metrics, such as availability, and service metrics, such as response time and total time to resolution. Other metrics are more complex and result in massive data sets that come from different sources. System reliability is a good example as it involves monitoring every part, in every system, in every customer, in every site across the world on a daily basis.

The ROI from running Big Data analytics on a dataset for better and faster insight into customer quality and experience is absolutely tremendous. Thanks to new Big Data technologies and skills, I’ve dubbed this type of ROI “bathroom ROI,” meaning the amount of time it takes to walk to the bathroom, stop to chat for a few minutes in the hallway and come back to your desk is the amount of time it now takes to run a report for a sales rep who needs to understand system reliability performance of their customer over the last five years. What used to take one person five business days now takes 8 minutes. At 40 hours per week, that is almost 1500 times faster than traditional methods.

When it comes to enhancing the customer experience, managing these disparate data sources, applying data analytics and understanding the findings are what it takes to add value back to the customer.

To enable the monitoring of products and parts at customer sites, customer quality groups partner with engineering, manufacturing and others to help architect systems that take key data elements such as supplier information, shipping details, customer IDs, site IDs, service request data, and the like into account. All this customer support, engineering and manufacturing data comes from different places within the organization that all store data in their own formats. The same can be said for banks that have different relationships with the same customers across many business units: the transactions on a credit card are dealt with differently than approvals for a home loan.

Unless an organization has the technologies and systems to aggregate all of its data in one place and in the same format, it takes an incredible amount of manual labor and time to make those data sets “talk” to one another.

Today, many companies don’t know or understand the value of the data they’re managing. They know what they are looking for and for that, traditional methods suffice. The advantage of Big Data and Big Data analytics is to uncover truths they didn’t even know existed.

Through the management and subsequent analysis of data, companies can detect, solve and even predict an abnormality within a customer’s IT environment before their IT administrators ever knew there was one to begin with.

For example, if a disk drive were to stop working, predictive analytics could forecast the failure six months ahead of when it actually failed and would trigger a service request to the customer.  The vendor could proactively visit the site where everything in question would be checked and replaced before the customer even knew there was a potential for drive failure. As a result, the interactions a customer has with a vendor become more seamless and less intrusive each time there is a problem with a part or feature of the product.  The customer relationship deepens and becomes harder to break. The result is competitive advantage since there are a vast number of players all competing for the attention and dollars of the same customer.

To use Big Data effectively, organizations need to have a vision for what they want to achieve and how they want to become a leader in customer experience. They need to be willing and able to analyze all of the data available no matter how voluminous. The power of Big Data is being able to process large amounts of data in a short period of time.  Organizations need to employ the right people who can merge their data analysis skills with their inherent curiosity and creativity to correlate data sets that seem unrelated to help uncover new insights. Data Scientists are a new breed of professionals who apply advanced analytical tools and algorithms to generate predictive insights and new product innovations as a direct result of the data.

Customers want and expect to have the best possible experience from their vendors. Using Big Data to address customer issues before they become problems is necessary to ensure they stay loyal and continue to increase value for the organization.

Jim Bampos is Vice President of Customer Quality at EMC. Jim has almost 30 years of experience in developing and managing customer experience, quality, and process improvement organizations. Jim’s focus has been on providing leadership in establishing loyalty models, quality methodologies, and leading enterprise organizations in adoption of corporate initiatives based on quality and customer requirements. Jim also leads the Total Customer Experience program, EMC’s company-wide commitment to consistently exceed its customers’ expectations for quality, innovation, and interaction.