How to Add Value to Your Data Management Systems

New marketing data sources are ever-growing, and there's an urgent need to harness them, which is greater now than it's ever been. As a result, a number of data management systems (DMS) have emerged to enable easier management of this information. However multiple, siloed data sources inside a DMS mean nothing if they can't be analyzed holistically to give a broader view and scope of your story and performance.

Here are two common data management systems marketers use, and how to maximize each:

1. Data Management Platform (DMP)

A DMP is a very specific type of data management solution for ad data, typically digital display. It collects data for programatic ad targeting, such as cookie IDs or search queries, and sorts this information to help generate audience segments in order to target specific users with online ads. However, a DMP typically does not execute aggregations; it’s usually transactional in nature. As Digiday notes, “without being linked to another technology, a DMP can’t actually do much.” 

Value Booster: A DMP is one source among many that a marketing intelligence platform ingests data from. Using a platform that provides a single view enables marketers to combine ad data with first- and third-party data in order to visualize patterns and insights about media behavior that are not readily seen.  

2. Data Lake

A data lake is a repository for both structured and unstructured data. Essentially, the data is in its “original format.” It is stored here, but not necessarily prepped for analysis — which is fine for IT personnel, but no so much for the business end user. Typically, a data lake can be housed on an internal server or in the cloud (e.g., Amazon Web Services S3). Data lakes are often vulnerable to contamination due to limited oversight in version control. As Gartner notes, “data lakes typically begin as ungoverned data stores,” making it easy to go from a data lake to a data swamp.

Value Booster: Since data lakes are typically IT led, getting value out of the information here remains the responsibility of the end user — you, the marketer. Marketers need access to specialized analysis of data that these lakes cannot perform. However, purpose-built infrastructure, such as marketing intelligence platforms, enable analysis of data that doesn’t require the end user to be a data scientist. Self-service capabilities and configurables analysis and reporting modules make ingestion, management, and analysis of data from the lake (whatever the source type) easier. 

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