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Four MDM Trends From Informatica World

Posted by Mahmood Majeed on Mon, Jun 05, 2017

shutterstock_142657753.jpgAmmar Feroz co-wrote this blog post with Mahmood Majeed.

While data and analytics have been top-of-mind issues in life sciences for many years, master data management, in particular, was one of the most talked-about issues at this year’s Informatica World summit, held May 15-18 in San Francisco.

Although we’ve partnered with Informatica for many years, ZS was a first-time sponsor at this year’s global conference, which invites technology and business users to discuss the hottest topics in data and information sciences. After spending time participating in various sessions and speaking with pharmaceutical attendees and analysts about MDM, we’ve identified four hot topics within the ongoing evolution of MDM:

1. The maturity of MDM: MDM is going beyond its match-and-merge capabilities. Now it’s a key, foundational tool that enables life sciences companies to operate more efficiently, using better analytics and insights to glean a broader, more holistic view of the customer. Companies are becoming more purposeful in how they leverage data to drive business decisions, and the evolution of MDM technology has allowed IT and business users alike to take advantage of it.

Moreover, MDM today is much more agile and cloud-based, allowing companies to collect and mine vast amounts of pertinent information—such as social data, claims data, device data and many others—within the data-saturated healthcare ecosystem. As a result, MDM’s increasing maturity and sophistication are resulting in large-scale operational efficiencies and opportunities to scale. The best part about seeing and participating in the creation of a mature MDM solution is the excitement that users have because they’re now able to quickly and easily understand customers in a single view.

2. Self-learning capabilities: In essence, MDM creates opportunities to make our work better—not only more efficient, but more effective. Data-driven decisions are better aligned with commercial analytics needs and give companies the necessary core foundation for understanding customers. And for those that truly invest in a data strategy, they gain a significant competitive advantage.

These gains in effectiveness are tied to MDM’s increasing “intelligence.” Data science, artificial intelligence and machine learning are on the rise, in both a business capacity and among individuals. It’s no surprise that this shift is impacting a foundational business solution like  MDM, and we predict that these capabilities will become integral to how life sciences companies consume information.

Previously, MDM/technology solutions centered on the organization of data: The focus was on cleaning the data and presenting it in an organized fashion, and on sharing data and insights across organizations. Now companies are moving away from a purely back-end data management solution to a more front-and-center, integrated solution that not only automatically pushes updates, but also offers suggestions and recommendations via self-learning algorithms. The MDM of the future will include more AI automation and less human involvement, and the increasing complex definition of “customers” is demanding this type of automation. The ongoing shifts within the customer landscape are difficult to keep up with, so automation—stemming from business processes—is a necessity for “intelligent MDM.”

3. The chance to improve the customer experience: With better, speedier access to information and vastly improved user interfaces, MDM is helping companies improve their go-to-market processes and, ultimately, improve the customer experience. Technology and opportunities within modern technology, like machine learning, are helping companies pivot to a more customer-centric business model and boost customer satisfaction. Furthermore, companies are seeing less resistance to change and better MDM adoption as solution providers continue to deliver more features that improve end users’ engagement.

Because of these improvements with MDM, users are demanding more industry-specific solutions to create more targeted opportunities. Every industry has nuances when it comes to data, and life sciences is no exception. Life sciences companies have extremely complex, sensitive and often disparate data, so an industry-specific solution that’s built to handle their specific needs is key. It means quicker value creation for companies and an improved user experience for their teams. As our fellow ZS Principal D. Sahay says, “Vertical is the new horizontal.”

4. A proactive data governance process: An underlying theme for MDM success across an organization is strong data governance. Even the ubiquitous “PPT” (people, process, technology) framework shows that technology is a part of the solution, but it’s not the whole solution.

In our experience with life sciences MDM engagements, data governance doesn’t have to be reactive, but it has to be proactive. Data governance needs to align with the business needs, which helps ensure careful prioritization.
Moreover, many companies only use data governance programs to ensure data quality. Often, this leads to a data governance function that’s underutilized and focused on just one aspect of MDM. Data governance programs need to focus on other key elements, such as change management, data acquisition strategies, compliance, data usage and proliferation management. The data governance team needs to address all of these elements in order to bring transformational change to the way that the company uses data.

“Data is the new oil,” as they say, and data governance teams are akin to oil companies bringing the fuel to power the data revolution. 

MDM has evolved significantly as an advanced capability, and it will continue to improve and adapt within the changing healthcare ecosystem. Are you keeping pace with the competition?


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Topics: Master Data Management (MDM), customer data governance, customer experience, ZS Associates, ZS, Pharma, Analytics, data, Mahmood Majeed, MDM, artificial intelligence, biopharmaceuticals, Ammar Feroz, Informatica World, machine learning

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