In short: MDM increases the value of Big Data analysis. It helps determine what to look for from a limited set of elements in Big Data’s vast expanses. It helps answer questions like “What are the three or four key pieces of information we need to retrieve from millions of samples of this data?”

But, be warned: Connecting unstructured sources of data to your internal, structured and governed data can be daunting. Distilling the data from these sources down to information that’s insightful, applicable, and actionable, as well as connecting it to real customers, demands a true master of data management.

Both Analytical MDM and Operational MDM—on their own or in combination—are integral to turning Big Data into competitive advantages. How can MDM and Big Data analytics work together? Here are two examples.

1. Customer analytics. Analytical MDM helps you mine massive amounts of unstructured information (Big Data) in the comments and sentiment expressed on social media. It helps you learn what customers and prospects really think about your company, products, and competition. Did they retweet your competition’s update? Are they clicking on your links on social media? What are their comments on your LinkedIn page?

By paying attention to the signals of digital body language, you can tune marketing campaigns based on real insights, and in real time.

However, the integrity of data that’s obtained from social networks or sensors in equipment cannot fully measure up to the data created, cleansed, and maintained inside the firewalls of company systems. It’s not as trustworthy. Creating collections of data that have common “bands of trust” is essential for people who manage MDM operations that work with internal and Big Data sources.

Analyzing social media and other unstructured data from external sources can also uncover master data attributes about customer behavior or device usage the companies can’t find in internal systems. Organizations can integrate these elements into the MDM environment to further enrich master data and provide a more robust data set.

Companies need to continue to push the limits to understand and capitalize on the customer’s purchasing journey. Tomorrow’s leaders are the companies that combine social media sentiment analysis with other unstructured data sources (such as product reviews) to unlock customer insights.

2. Preventative and process optimization. No one likes a disaster, especially one that is preventable. Operational and Analytical MDM can help you plan and execute preventive maintenance activities that keep your business running. Once you set the parameters around the mean failure rates of parts in complex, distributed systems, you can then apply the personnel, planning and materials to prevent downtime and disasters before they happen.

The two can also assist you turn terabytes of data produced by sensors and transponders in your equipment—the Internet of Things—into actions that help you optimize processes.

FORMING A BIG DATA STRATEGY IS KEY

Many companies are still trying to figure out how to use Big Data analytics, let alone transition to a data-first culture. Visualizing a strategy that includes MDM is difficult when you haven’t figured out the core element of Big Data.

Big Data applications are, by design, built and thrown away in a short time span. Understanding what master data you company needs for analysis can be a moving target as Big Data use cases evolve. As a result, you can’t approach the business case in quite the same way as traditional applications.

A strategy isn’t an etched-in-stone destination that you follow dogmatically; it needs to be dynamic as it sets parameters as you make decisions along the Big Data journey. Understanding early on that MDM is a key part of that Big Data strategy positions you to maximize the value of your investment. Delay only increases potential cost and complexity.