Most master data management systems were designed 10-15 years ago, when the requirements weren't as complex as they are today. The top 3 concerns about legacy MDM systems are:
- They are too slow
- They are constrained and lack scale
- They are rigid
Unfortunately, they are not designed to power digital customer experiences.
Legacy MDM systems are not designed to deliver on current business requirements. They are slow. It takes too long to set up a new MDM solution. The upgrades are very time-consuming, really costly, and it is really hard to maintain the systems on an ongoing basis across a large enterprise data network. For example, adding a new data source or adding a new attribute to a customer profile takes months with legacy MDM. So responding to the business needs takes far too much time.
The need for data has grown exponentially and business people need secure access to more than the limited number of attributes that are considered “customer master data.” It’s not enough to just have access to a customer’s first name, last name, e-mail addresses, and phone numbers. It’s valuable to have access to big data interactions and transactional data to deepen customer understanding. And it’s critical to manage the data at scale, with thousands of attributes from hundreds of sources. Legacy MDM systems were not designed to do that. Legacy MDM systems are also not designed to deliver real-time data at scale, which is a requirement for digital and mobile experiences.
Third, legacy MDM systems do not deliver insights-ready data. If the data science or analytics team needs data, they have to spend a lot of time in ETL in doing data cleanup. They are not able to leverage their investments in newer technologies such as AI/ML.
Check out this blog to read more about why companies are replacing Legacy MDM: Say Goodbye to Legacy MDM, Say Hello to Reltio Connected Customer 360 to Power Connected Experiences
Concerns with Legacy MDM
| Slow || Constrained || Rigid |
| Takes too long to setup and go live, upgrades are painful and costly, high TCO || Can't scale to billions of customer profiles || Unable to gain a rich understanding of your customer base for hyper-personalization |
| Slow to respond to business needs because it's difficult to add new data sources, profile attributes and compliance requirements || Can't scale to handle customer profiles with 1000s of attributes, transactions and interactions from 100s of data sources || Stalls your transformation while adding to your legacy burden |
| Slow to turn insights into action || Not designed to manage a network of relationships || Doesn't add value to your ML and AI investments |
| || Cannot operate in real time at scale || |