A data management platform (DMP) collects, manages, and analyzes data. This may sound just like a data analytics platform, but a DMP’s scope and purpose are more specific. It gathers audience data which is information about people who respond to advertisements or visit websites or other digital properties.  The DMP uses this data to build anonymized customer profiles that drive targeted digital advertising and personalization.

Using a DMP helps accurately target advertising to the right audience, which results in higher response rates, increased brand recognition, and ultimately, higher conversion rates. But many factors can slow DMP adoption, including:

1. Low Relevancy. Nothing will slow the adoption of a DMP more than data that does not meet users’ business needs. This can happen when data lacks meaning or when data isn’t timely. For example, first-party data (data your company has collected directly from its audience) often requires enrichment to be useful.
2. Bad Data. Lack of quality data is one of the main reasons audience data isn’t used when planning campaigns for digital media. In particular, the reliability of third-party data, information collected by companies that don’t have a direct relationship with consumers, is highly variable. Digital marketers who rely on data to help them make important marketing decisions need to know that they can trust its integrity. If data isn’t accurate, complete, consistent, reliable, and up-to-date, users will lose confidence in the DMP and stop using it.
3. Third-party Cookies. DMPs have historically depended on third-party data. With third-party cookies going away, many are uncertain of the DMP’s future. Some businesses are implementing a zero-party data strategy where a customer intentionally and proactively shares data to fill the third-party data void.
4. Poor Usability. Data analytics users have traditionally been technically savvy data engineers and data scientists who represent a small percentage of an organization’s employees. Organizations struggle to bring in a broader base of business users, such as marketing teams, when the DMP is hard to use.
5. Limited Scalability. Scalability is a critical capability for DMP success, but many platforms are unable to expand with growing data volumes and users.
6. Data Silos. It’s hard to get rid of data silos. When these can’t be integrated with the DMP, it may be difficult for organizations to deliver the complete customer profile data needed for decision-making, which can slow platform adoption.
7. Sourcing From Multiple Vendors. Data integration, data quality, and other management workloads add more costs, and complexity when sourced from multiple vendors. This can limit further investment in the DMP if its costs exceed the business value delivered.

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To overcome these DMP bottlenecks, organizations need a scalable platform that is easy-to-use that can break down data silos. Additionally, businesses need to deliver relevant and trustworthy data. The Avalanche Cloud Data Platform provides data integration, data management, and data analytics in a single solution. This lowers risk, cost, and DMP complexity while allowing easier sharing and reuse across projects than cobbling together point solutions.

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