A Guide to Modern Data Mastery

Data is the kernel of any business in today’s evolving digital landscape, empowering organizations to make better decisions, drive innovation and provide enhanced customer experiences.

However, the digital channel explosion, subsequent proliferation of touchpoints and demand for personalized experiences have resulted in a never-ending data labyrinth. With mergers, acquisitions and evolving regulatory framework, the need for speed is eminent to maintain accurate and consistent data for positively impacting business dynamics. 

The most pressing challenge faced by the top brass in organizations is to handle and manage customer and master data complexities. This article explores data management and attempts to unravel the synergies between customer data platform (CDP) and master data management (MDM). 

Focus and Scope of CDP vs. MDM

CDP has become increasingly relevant in the realm of marketing and customer experience. With the global CDP market estimated to grow at a CAGR of 32.4% by 2027, enterprises are quickly realizing their transformative potential in leveraging customer data. It is worth noting that applications of customer data platforms extend beyond marketing as investments are being made in solutions that offer a more intimate look into the customer journey from lead to conversion. With the growing consumer awareness and demand for improved data protection and privacy, enterprises seek sustainable data handling and distribution practices. 

Additionally, businesses favor a versatile CDP — capable of managing vast data sets and facilitating seamless connections between source and destination systems. Take, for instance, a CDP that facilitates data exchange and management between the IT department of any brand to its operations, manufacturing and vendor liaison systems. 

Coming to MDM, modern data management strategies are unimaginable without the same. 

This particularly stands true for industries where data accuracy and consistency are paramount, such as healthcare, finance and manufacturing. Emerging approaches like agile MDM or hybrid MDM are gaining traction as organizations adapt to evolving needs. The accelerated deployment of artificial intelligence (AI) and machine learning (ML) algorithms enables businesses to automate data governance processes. This, in turn, is easing off the burden on IT teams while improving data accuracy. 

The global MDM market size is expected to grow at a CAGR of 16.85 % by 2030. These numbers demonstrate the growing significance of MDM in enabling efficient data management and governance across various industries. 

Related Article: Customer Data Management Is the Key to Consumer Trust, Profitability

Core Capabilities of CDP vs. MDM

While CDP and MDM seamlessly integrate into operational systems and assist with data extraction and management, certain key capabilities set them apart. Let’s have a look.

CDP capabilities at the core are to collect, unify, analyze and activate data, including:

• Data collection: User data is collected from various online and offline resources (data browsing, cookies, names, demographic data, emails, device addresses, page visits, purchase history, etc.) in real-time.
• Profile unification: Duplicate profiles of customers are matched up. Sometimes devices, email IDs of the same customers and even aggregating customers to the same household accounts are also done. 
• Segmentation: AI/ML algorithms are deployed to conduct segment discovery, proclivity models and predictive analytics. They also manage importing and deployment of custom models.
• Activation: Campaigns that include email, mobile messages, advertising, suggested recommendations, dynamic and self-optimization are rolled out and optimized from time to time. 

MDM manages and governs an organization’s critical data assets based on the following core capabilities: 

• Data integration: Gives a unified, consistent and accurate data view by integrating information from various sources. 
• Data quality management: Algorithms and techniques help in data cleansing, matching and maintaining quality metrics. Duplicate and redundant data is eliminated, thus ensuring master data is reliable, accurate and free from errors or inconsistencies.
• Data lifecycle management: Assists in maintaining data through various phases such as creation, validation, archival or retirement. Ensures that master data is up-to-date, relevant and aligned with an organization’s evolving needs.
• Data hierarchy management: Helps enterprises maintain organizational or hierarchical structures in data to support various business requirements or procedures.
• Data governance: Policies, standards and guidelines are defined and enforced to protect master data. This helps in enhancing data security and protection.

Related Article: 3 Things to Consider Before Buying a Customer Data Platform