Let’s start with the obvious: We’re already drowning in data, and it just keeps coming. We need to gather, store, collate and analyze it, because we know it contains intelligence that can guide ongoing and future initiatives. One invaluable tool in that effort is Master Data Management (MDM): It brings together different policies, standards, mandates, processes and even technologies underpinning the core data to provide a single point of reference. That makes it ideal for companies submerged in data and looking to make better use of it.
So why isn’t that happening? Why does MDM seem like a legacy discipline? And what can we do about it?
One obstacle is the sheer dynamism of the industry. Exciting as it is, the constant emergence of new technologies, each enabling more data in different formats, makes it inevitable that some traditional technologies and processes supporting MDM just can’t keep pace. It can actually have the opposite effect: It devours IT resources and hurts other business initiatives. That’s one reason why many organizations have deployed multiple MDM programs, leading to exactly the kind of information silos that this advance was intended to eliminate.
Meanwhile, the data (and the number of data formats) keeps mounting, which means the critical task of developing actionable intelligence from different data sources—such as, for example, identifying key relationships between different customer subsets—gets more difficult each passing day.
Consumer-Enterprise: Bridging the Divide
Here’s a different perspective on the problem, and perhaps a path forward.
MDM originated in the enterprise arena, with extensions for different verticals, like life sciences. This is a world where untangling relationships between healthcare professionals and organizations—each with a complex web of plans and players—can provide a significant competitive advantage. However, mastering that massive dataset to gain a single view of the customer or product just isn’t enough: It requires comprehensive access to data from all the different areas of the organization, and a holistic view of the entire business to support multi-channel or omni-channel strategies. Meanwhile, there are other complications. We’ve got regulatory mandates to worry about, and data assets represent a potential revenue stream, to name two.
This is just one reason why so many MDM-only tools can’t do the job. They need to be supplemented with software programs that better enable data quality, enable governance, ensure self-service BI and analytics, etc. All that is so enterprise, but now consider the consumer angle. It’s easy to dismiss any similarity between the relative trivia of, say, LinkedIn or Facebook and the complexity inherent in industry-specific MDM systems, but that’s really the point.
Those services and others like them provide effortless access to—and management of—all types, not just master data, in the form of profile information, as well as transaction, interaction, and social data, all within the same application.
In fact, they uncover rich relationships and connections across people, products, and organizations, while using the information to predict and recommend the best course of action.
There are no SQL queries, and no need to understand the underlying data model or data structures. Business users get relevant insights, and recommended actions before even asking the question, directly from a single application.
And again, before dismissing it as trivia, let’s remember that these applications blend both analytical and operational capabilities, and continuously scale to handle millions of records in real time. In fact, the data volumes involved are staggering. They also deliver new capabilities seamlessly to improve user experience and productivity on a regular basis across a wide range of devices.
That’s modern data management. It encompasses the traditional role of Master Data Management but also incorporates Big Data, social media, event data and whole lot more.
Back to the Enterprise
This is the future of MDM: It should adhere to the Facebook and LinkedIn paradigm for B2B data management and data-driven applications. It’s a component of a wider modern data management platform, and it’s directly integrated with data-driven applications.
It builds on core MDM strengths such as address cleansing, match, and merge and applies them dynamically to data from internal and external sources, specifically as a component of an enterprise data-driven application. That’s how it can ensure reliable data, governance, role-level security, visibility, and more. Indeed, as with the consumer world, social and collaborative curation and feedback increases the value and use of the data.
Of course, the fundamental purpose or this discipline is to guide business initiatives. Appropriate context must be built in to offer business teams the insights and recommendations they need. In particular scenarios, they can add to data generation by submitting their own updates and ratings. (That, in essence, is the equivalent of a Facebook like or LinkedIn recommendation.)
Visibility—in the literal sense, the key to making the findings easier for more users to understand—is equally important. For example, one common technology thread in those consumer-facing applications is graph databases. Graphs highlight complex and evolving relationships between many different types of entities, and makes them more comprehensible than traditional relational databases ever did. For the record, most off-the-shelf graph databases are unable to handle the data volume, variety and velocity. However, the most scalable enterprise data management platforms use a blend of columnar and graph NoSQL hybrid technology.
Remember, it’s been a long time since MDM entered the mainstream, and it gained attention by promising a 360-degree view of corporate data. Yet after all this time, most of the offerings on the market focus on, and manage, master data only. Many use them for a single customer domain, with product and other entities managed separately, if at all. The creation of a 360-degree view, with the accompanying interpretation of complex relationships and affiliations, remains a separate effort.
Moving forward, MDM can offer tremendous benefits as a component of a modern data management platform, fully integrated with data-driven applications and delivering fast time-to-value across the enterprise. But without a high level of modernization that adapts freely from consumer equivalents, it’s headed toward legacy status.
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