The rise of data collaboration

Show Notes

• 02:14
  Current trends in data collaboration

  Data collaboration is experiencing a surge of new players, established players like Lotame, and major tech companies such as Google and Amazon offering solutions. This evolution enables marketers to understand buying behavior by seamlessly merging multiple datasets.
• 03:02
  Challenges in implementing data collaboration initiatives

  Businesses struggle to unify data from different internal systems and combine their first-party data with external partners' data for a holistic customer view. This is particularly challenging as traditional methods for measurement, like third-party cookies, are disappearing.
• 05:23
  Use cases for data collaboration

  Beyond customer insights, businesses can use it to improve media buying by identifying overlap between their target audience and a media outlet's consumers. In addition, complementary collaborations leverage shared customer bases for co-marketing opportunities.
• 07:15
  Foundations for data collaboration success

  Effective data collaboration requires preparation and alignment on a common ID to securely combine data sets without exposing personal or proprietary information. This facilitates the merging of first-party data sets, maximizing the value and insights derived.
• 09:01
  Innovation through complementary data collaborations

  Collaboration on common data sets enables brands to move away from third-party data, using first and second-party data for targeted marketing. For example, a hotel company sharing its strong signal of intent to book a room in Miami allows an airline to target Miami deals to a high-intent audience.
• 10:27
  The evolution of data collaboration

  As cleanroom and data collaboration environments become more prevalent, larger organizations are increasingly comfortable with sharing data to unlock collaborative opportunities. These environments mitigate the risk of data exposure and misuse.
• 11:08
  Data collaboration protections

  Data collaboration platforms offer features like limitations on data visibility (minimum overlap thresholds), and data manipulation (adding noise) to prevent misuse. Choosing the right collaboration partner depends on specific use cases and desired levels of data protection.
• 12:23
  Leveraging data sharing for enhanced customer insights

  Data collaboration integrates diverse datasets beyond conventional sources like Google and partnerships, sustaining functionality despite declining third-party cookies. By Leveraging common IDs, data collaborations ensure ongoing insight into customer behavior.
• 13:30
  Data collaboration approaches for large vs. small businesses

  Large companies with the resources typically leverage collaborations for partner data to augment their first-party data for enablement. On the other hand, smaller businesses prefer user-friendly, one-stop-shop solutions for data enrichment, activation, and modeling.
• 15:05
  Data collaboration and cleanrooms vs. data warehouses

  Data collaboration complements existing data infrastructure like customer data warehouses. These platforms are now incorporating permissioned data-sharing functionalities to enable collaboration among their customers.
• 16:08
  Data collaboration for multinational marketing

  Organizations face uneven regulatory environments for data on multiple continents, and data permissioning can help weave through these inconsistencies. Data collaboration and cleanrooms enable brands to continue leveraging their data assets comfortably and securely.
• 17:22
  Future trends in data collaboration

  Agencies will likely establish preferred publisher networks, committing funds for ad inventory in exchange for first-party data access. While direct brand-to-publisher collaborations are also emerging, the agency-led model appears to be the stronger and more scalable approach.