Shrek 2” was the top-grossing movie, “The Da Vinci Code” the best-selling book, and Janet Jackson suffered an infamous “wardrobe malfunction” at the Super Bowl. The year was 2004, and it’s also when a new term – Master Data Management – first entered the IT industry lexicon.
Within a couple of years, Master Data Management, or MDM, was being hailed as one of the hottest trends in enterprise IT, with vendors releasing products meant to address it and developers building and incorporating application-specific MDM. Early-adopter companies proclaimed MDM’s benefits.
More than a decade after its birth, MDM continues to be a mainstay of the data strategies at many companies. But MDM hasn’t lived up to its promise – whether its the single version of the truth or 360 degree view of data. Moreover questions have arisen about how MDM fits in today’s world of social media, cloud computing and Big Data and how, in this new environment, organizations see a return on their MDM investment and gain competitive advantage in the marketplace.
The questions shouldn’t be surprising. Many MDM systems were designed at a time when no one could have envisioned that feedback about a company’s product on Twitter or other social channels would become relevant information. MDM systems struggle to ingest structured and unstructured data from a variety of channels, including social. They’re also ill-equipped to help organizations derive insights from this information in real time and make them available to transactional applications.
From the beginning of MDM’s existence, one of the most daunting challenges in building these systems has been the inherent complexity of the master data sets themselves – highly related data, often duplicated and spread over multiple sources such as CRM and ERP applications.
Toss in today’s newer forms of information and you have a recipe for chaos and lost opportunities to gain business insights from data.
A Re-imagined MDM
That’s why MDM needs to be re-imagined. Current MDM solutions typically store their data in an Relational Database Management System (RDBMS) which makes it hard to see relationships within the data and leverage those insights in real time for competitive advantage. The new MDM needs to use a different type of data store optimized to quickly discover new insights in existing data, provide a 360-degree view of master data, answer questions about data relationships in real time.
The good news is that this is not only possible but happening today, thanks to new technologies and approaches that transform the concept and execution of MDM to enable companies to consolidate data from many channels into one and offer a highly related, true view of this data.
The new MDM derives value from data and its relationships to other data. It’s about supplying consistent, meaningful views of master data.
While traditional MDM emphasized the processes, policies, standards and tools for providing a single point of reference for data, the new MDM recognizes that data wants to tell a story: Which products tend to be purchased together? Which products tend to be purchased by the same customer and the members of their household on a regular basis? What about groups of customers? What other factors might come in to play for how people make purchase decisions?
New Tool for the Re-imagined MDM
Graph databases are one of the new technologies fostering a re-imagined MDM. These databases, which contain data structures that describe both data and their relationships, are a powerful way of discovering, capturing and making sense of complex interdependencies and relationships.
Remember what I said about how the new MDM recognizes that data wants to tell a story? Consider questions we might want to ask of this data: What sorts of items tend to be purchased together by a category of customer? What about over time? Which members of a household tend to make which types of purchases on which days? How does this vary by distance from a store?
With graph databases, businesses can ask questions in real time about the data relationships in their master data that they might not even know they have, driving new business insights. Graph databases can even be used to identify potential duplicates in an MDM system based not just on matching data elements, but by inferring relationships between instances of master data. The result is true unification of data in a single system – fulfilling the promise of MDM in ways never thought possible.
For example, retail shoppers these days expect finely tuned, highly personalized recommendations and react less well to one-size-fits-all suggestions. But achieving this requires data products that can connect masses of complex buyer and product data to gain insight into customer needs and product trends very quickly. Major retailers now use graph databases to understand the behavior and preferences of online buyers with enough speed and in enough depth to make real-time, personalized, ‘you may also like’ recommendations.
Old-school MDM isn’t up to such a task. They simply don’t handle data relationships well, nor are they well-equipped to handle data that’s always changing, such as streams of new information from social media channels.
The re-imagined MDM, with its ability to discover, capture and make sense of complex interdependencies and relationships, is a winning formula for using data to its full advantage in today’s changing business world.
Emil Eifrem is CEO of Neo Technology, creator of graph database Neo4j.
Keywords: emil eifrem • graph database • MDM