At DinMo, our mission is to democratize data usage for non-technical users. We believe data engineers should focus on their core tasks without being distracted by marketers' needs. Meanwhile, marketers should have the tools to leverage data autonomously, without relying on data experts.
The DinMo’s Knowledge Store
Last year, we released our first Data Model update with that direction in mind. The idea was to get round the rigid data models of traditional CDPs by offering something flexible and adapted to any company. Our previous Knowledge Store was developed keeping the modern data stack in mind, following a domain-specific data mesh approach, seamlessly integrating with the existing data infrastructure.
During onboarding, data users created Entities once, specifying to DinMo which data should be available to use, how to interpret it, and how to display it to marketing users in a friendly way. After this initial configuration (which takes a maximum of one hour) marketing users would be ready to leverage data autonomously using our Audience Builder, without needing further involvement from the technical team.
Our SQL-free segment builder
The power of Models still lies in the ability to leverage the relationships between them. Built upon your models, segment conditions can take advantage of these relationships, offering a new dimension of insights. Models facilitate the exploration of multi-dimensional data, allowing to delve into advanced segmentation based on the relational aspects of the data.
Expanding our data model to unlock new use cases
In the past months, we have introduced many new integrations, some supporting unique use cases we hadn't anticipated at the time, such as synchronizing company data to HubSpot and product information to Intercom. While our previous data model effectively managed audiences and conversions synchronizations, it wasn't suitable for these new scenarios.
Introducing new types of objects
To address these new use cases, we decided to revamp our data model, by introducing a new data object, Models, which replaces our legacy Entities. This change provides much greater flexibility in the types of data available for activation, while maintaining the powerful segmentation capabilities of our previous system.
Practically, this means DinMo can now handle custom data for specific use cases, beyond just audiences and conversions. This underlines the advantage of using a composable CDP, unlocking new CDP use cases.
New model types in DinMo
Simplifying the activation process
Furthermore, the activation process for non-audiences objects have as well been drastically simplified. Instead of having to segment a parent entity before creating an activation, models can be activated directly in just a few clicks. But of course, users can continue segmenting models using our no-code builder, just as before. What’s new is that Custom segments can now be defined on DinMo, widening our no-code builder segmentation scope.
This simplification is in line with our desire to position ourselves as a Reverse ETL for business teams, with easy-to-use and no-code functionalities.
Managing data alterations
But that's not all. Building on last year's release, we identified a specific scenario where data engineers had to intervene on DinMo post-initial onboarding: data alterations in the source data warehouse. This typically happened when a critical column of a table used by DinMo was deleted or renamed outside of DinMo. Now, Models have been designed with a fixed schema independent of the table in the data warehouse to ensure continuity of service when these cases occur. In practice, this means DinMo directly detects these source issues and allows for correction with just one click within the related Model.
Modification of source column
👉 Read the DinMo documentation for more in-depth information.
Final thoughts
To sum up, DinMo remains committed to its mission of democratizing data usage for non-technical users. As far as we are concerned, we believe that Activating data is key to making informed decisions across all operational teams.
Recognizing the evolving needs of our users, we have enhanced our data model to accommodate a broader range of use cases and provide greater flexibility. And we plan to continue moving in that direction. Stay tuned! 🌟
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