Customizable Insights

Create bespoke reporting data models to extract deeper insights, optimize strategies, and adapt analytics to meet specific business needs with Data Distiller’s Customizable Insights. Use the Customizable Insights capability to enhance transparency and gain operational insights from your Adobe Experience Platform data across dimensions such as profiles, audiences, campaigns, journeys, entitlements, and consent. This capability provides a versatile, adaptive solution to tailor your organization’s reporting data models to align with your specific business needs.

This document covers the use cases, essential capabilities, and required steps to develop a customizable insights dashboard with Data Distiller.

Prerequisites

This tutorial uses user-defined dashboards to visualize data from your custom data model within the Platform UI. See the user-defined dashboards documentation to learn more about this feature.

Getting started

The Data Distiller SKU is required to build a custom data model for your reporting insights and to extend the Real-Time CDP data models that hold enriched Platform data. See the packaging, guardrails, and licensing documentation that relates to the Data Distiller SKU. If you do not have the Data Distiller SKU, contact your Adobe customer service representative for more information.

Customizable Insights use cases use-cases

Below are common use cases that can be effectively addressed through Customizable Insights in Data Distiller.

Profile and audience usage transparency usage-transparency

Challenge: How to break down Key Performance Indicators (KPIs) by specific criteria like business units, loyalty status, or Customer Lifetime Value (CLTV).

Customizable Insights Solution: Data Distiller enables the extension of reporting data models in Adobe Experience Platform, facilitating the addition of custom profile attributes such as CLTV or loyalty status.

Challenge: How to apply audience overlap and size trendline reports to customized consent attributes for channels like email, SMS, and phone.

Customizable Insights Solution: The reporting data model can be extended to track changes in consent preferences over time. This involves building additional fact and dimension tables to trend consent preferences and scheduling incremental data refresh.

Optimize audience segmentation strategy optimize-audience-segmentation-strategy

Challenge: How to integrate Machine Learning (ML) model-generated propensity scores into their audience KPI reports.

Customizable Insights Solution: Data Distiller allows the inclusion of propensity scores from custom ML models, facilitating the calculation of aggregate scores at the audience level. This data can then be reported alongside standard KPIs.

Audience expansion audience-expansion

Challenge: How to acquire more than just profile counts in audience overlap reports and attain additional demographic data or preferences to guide audience expansion strategies.

Customizable Insights Solution: By extending the reporting data model, users can incorporate additional profile attributes, enriching the audience overlap report with relevant demographic data and preferences.

Key capabilities for generating Customizable Insights key-capabilities

The illustration below highlights several essential capabilities for generating Customizable Insights. These capabilities include:

  1. Data visualizations: Incorporating visual elements such as trends and bar charts for a comprehensive view of data trends.
  2. Dashboard authoring: Enabling the creation of custom dashboards tailored to specific use cases, providing a more personalized and targeted analytics experience.
  3. Flexible SQL data modeling: Use a versatile SQL data modeling approach that allows users to seamlessly combine and manipulate different datasets, enhancing adaptability, and analytical depth.
  4. Accelerated store: Implementing an accelerated store mechanism to efficiently serve aggregated insights through SQL, ensuring streamlined and rapid access to valuable information.
  5. BI connectivity: Facilitating seamless integration with popular Business Intelligence (BI) tools, including Power BI, Tableau, Looker, and Apache Superset. This connectivity ensures compatibility with diverse BI environments, offering users the flexibility to use their tool of choice for in-depth analysis and reporting.

Visual representations of the key capabilities of Data Distiller's Customizable Insights.

Steps to create Customizable Insights steps-to-create

To develop a Customizable Insights dashboard within Data Distiller, follow the step-by-step instructions below.

  1. Ad hoc query exploration: Begin by executing ad hoc SELECT queries to explore raw data on the data lake. This allows for on-the-fly, exploratory data analysis to experiment, and validates data where the results of the queries are not stored in the data lake.
  2. Batch query utilization: Use batch queries to create scheduled jobs for generating insights aggregate tables, ensuring a systematic and automated approach to data processing. Batch queries execute INSERT TABLE AS SELECT and CREATE TABLE AS SELECT queries to clean, shape, manipulate, and enrich data. The results of these queries are stored on the data lake.
  3. Aggregated insights loading: Load the generated aggregated insights into the accelerated store and use SQL to test queries, and ensure the accuracy and efficiency of data retrieval. To learn how to make stateless queries to the accelerated store, see the documentation.
  4. Access and integration: Access the insights stored in the accelerated store seamlessly by integrating with Adobe Experience Platform User-defined Dashboards or other preferred Business Intelligence (BI) tools. These integrations with third-party clients facilitate a cohesive and intuitive experience for users.

An infographic illustrating the four steps to Customizable Insights in Data Distiller.

Next Steps

By reading this document, you now have a better understanding of the use cases, essential capabilities, and necessary steps to develop a customizable insights dashboard with Data Distiller. To continue learning about creating bespoke reporting data models, see the reporting insights data model guide.

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