Customer centric: the value of MDM is measured in customer intelligence

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Discover how to use customer intelligence to create a customer-centric proposal and the most effective way to achieve a unique vision.
Do you know how closely a customer-centric company is connected to its master data? Did you ever imagine that customer intelligence and Master Data Management were complementary?

Master Data Management (MDM) is making a huge impact on delivering real business value. Proper master data management enables companies to unleash the power of information across the enterprise.

Because MDM solutions help business users trust data from all systems in use, they can be applied to many data domains, including customer intelligence (individual or organization), supplier, product, location, asset, and many more perspectives.



Data quality as an essential part of MDM


From customer intelligence to a customer-centric proposal
Master data typically consists of structured attributes that canada number dataset an entity. In the case of customer domains, this can be their name, address, date of birth, contact information, account information, or other specific unique attributes that serve to identify and define them. This is information that is essential to know in order to move towards a customer-centric business model .

In practice, master data residing in different systems may have varying degrees of completeness, timeliness, and correctness. For example, a customer may be represented by first and last name in one system and have a middle initial in another, or one system may have email as the primary contact and another may have a phone number. Because data collection occurs at widely varying times and by a variety of people, standardization becomes more complicated, especially for an organization that has branches, especially when they are located in different countries or regions.

To create a good customer record that is useful and leads to the implementation of a customer-centric model , the first decision to make is whether to join, merge or update the individual attributes of the customer records.

Only in this way is it possible to update source systems to create a trusted and consistent view that is accessible and shared across all enterprise systems, applications, and analytics.

The most effective way to achieve the single vision
Since no single approach to matching can anticipate all data variations, a hybrid of matching algorithms , such as those that mimic a skilled human user, can be applied. These matching algorithms can be:

Heuristics.
Probabilistic.
A combination of some of the above.
In all cases, they can also be combined with other matching, evaluation and search techniques to arrive at the most reliable view, the one that will best support a customer-centric strategy by allowing it to achieve objectives such as:

Optimize the ability to search, evaluate, join and combine records, which is critical to business success.
Minimizing errors and risks in demand planning, customer onboarding, territory allocation, and regulatory compliance is of great importance if you want to increase the efficiency of operational processes.
Deliver a single version of the truth across departments, brands and systems, benefiting multiple marketing activities such as cross-selling, up-selling and campaign management.


Today, with big data and the increasing prevalence of unstructured data sources, a complementary technology has emerged—one focused on increasing companies’ customer understanding and intelligence.