Data requirements are not just growing in volume, but also in the diversity of types and sources of data used in models. Data is no longer limited to structured, transactional, internally generated types. Today, when looking to enable greater use of artificial intelligence, organizations should incorporate machine-generated, unstructured, semi-structured, streaming, external and probabilistic data.
The limits of traditional MDM
As data proliferates and becomes more heterogeneous, business requirements for it are changing. For example:
- Companies most time need to respond to events and opportunities faster, and act proactively to meet customer needs; this can demand continuous, real-time access to comprehensive, current customer data.
- Social network, mobile device, web activity, location and other external context data should be reconciled with internal customer data to help drive marketing activities and customer experience.
- Some time there is a need to maintain dynamically changing, unified data that accounts for the interactions and relationships that exist within data entities across all touch points across the enterprise.
- Increasing regulatory compliance requirements demand greater data management and governance capabilities.
- Companies’ master data systems should include probabilistic data and fuzzy matching algorithms alongside more certain data.
When it comes to integrating these data types, “point-to-point” or “match and merge” approaches of traditional MDM would be too complex and slow; new techniques are required to adequately capture, link and curate these newer types of data.
Expanding and evolving MDM
Instead, a flexible “Digital Master” model is emerging that can be tailormade to deliver new business value from vast amounts of diverse data.
The tasks performed by traditional MDM, focused around identifying missing or erroneous entries in highly structured data sets and eliminating duplicate records, remain vital to modern business requirements. But MDM should be expanded to meet the business requirements described above. The solution is the “Digital Master”: an evolution of traditional MDM, designed to bring data to the heart of the business and organize data at scale, combining data from a wide variety of sources.
The Digital Master helps companies to move beyond having a limited and static "golden record" (of a customer, for example) to having a "golden profile" that not only contains more types of data and insights but can be updated and expanded in real time. (More on Golden Profiles in the full report on page 12.)
Compared with the use of traditional MDM, the Digital Master offers several advantages:
- It can support deeper insights into customer behavior, more specific and accurate predictions, improved customer experience and real-time responses to events and opportunities.
- It can be more business-outcome-focused, insight-driven, adaptable, comprehensive, agile and scalable across the enterprise.
- It can support enhanced business reporting but also drive operations directly by automating many real-time decisions.
- It can put AI and analytics at the center of every business process and any effort to transform customer insights and relationships.
- It can allow organizations to get more from AI and become more data-driven.
For example, we helped a large hospitality company build a digital data platform that allowed it to capture, curate, process and store emerging data types. The company’s objectives included a radical improvement in customer experience and personalization, to help retain loyal customers and increase market share. Now, with its Digital Master in place, the company has a 360-degree view of existing and potential customers—they are able to recognize individuals at any point of interaction across its channels—and it can therefore enrich offers with intelligent insight and get ever closer to its key objectives.
Making the transition
Organizations tend to transition to the new Digital Master in one of two ways:
- Extend. A two-step process that involves maintaining the existing MDM in an integration hub and integrating it with a data lake, which is concurrently expanded to incorporate new data sources and capabilities
- Replace. A greenfield approach in which a robust Digital Master is built entirely around a new data lake.
In both cases, aggregating the organization’s existing data into a data lake is the starting point. This involves bringing together data from past transactions, customer records, activity on the corporate website (including from cookies) and social media channels. To make sense of this basic data, we need to add more meaning to it in the form of contextual insights. For this we can use a knowledge graph. This is flexible enough to work like a bridge between various data types and formats, while at the same time adding descriptions and associations that help machines to discern what the data is all about.
This process helps us to create rich, “golden” profiles of dimensions such as customers, businesses and products. It can also provide us with powerful insights into customer attitudes and behaviors (for example, by identifying correlations between different buyers of the same product).