Adobe Data Distiller Guide
  • Adobe Data Distiller Guide
  • What is Data Distiller?
  • UNIT 1: GETTING STARTED
    • PREP 100: Why was Data Distiller Built?
    • PREP 200: Data Distiller Use Case & Capability Matrix Guide
    • PREP 300: Adobe Experience Platform & Data Distiller Primers
    • PREP 301: Leveraging Data Loops for Real-Time Personalization
    • PREP 302: Key Topics Overview: Architecture, MDM, Personas
    • PREP 303: What is Data Distiller Business Intelligence?
    • PREP 304: The Human Element in Customer Experience Management
    • PREP 305: Driving Transformation in Customer Experience: Leadership Lessons Inspired by Lee Iacocca
    • PREP 400: DBVisualizer SQL Editor Setup for Data Distiller
  • PREP 500: Ingesting CSV Data into Adobe Experience Platform
  • PREP 501: Ingesting JSON Test Data into Adobe Experience Platform
  • PREP 600: Rules vs. AI with Data Distiller: When to Apply, When to Rely, Let ROI Decide
  • Prep 601: Breaking Down B2B Data Silos: Transform Marketing, Sales & Customer Success into a Revenue
  • Unit 2: DATA DISTILLER DATA EXPLORATION
    • EXPLORE 100: Data Lake Overview
    • EXPLORE 101: Exploring Ingested Batches in a Dataset with Data Distiller
    • EXPLORE 200: Exploring Behavioral Data with Data Distiller - A Case Study with Adobe Analytics Data
    • EXPLORE 201: Exploring Web Analytics Data with Data Distiller
    • EXPLORE 202: Exploring Product Analytics with Data Distiller
    • EXPLORE 300: Exploring Adobe Journey Optimizer System Datasets with Data Distiller
    • EXPLORE 400: Exploring Offer Decisioning Datasets with Data Distiller
    • EXPLORE 500: Incremental Data Extraction with Data Distiller Cursors
  • UNIT 3: DATA DISTILLER ETL (EXTRACT, TRANSFORM, LOAD)
    • ETL 200: Chaining of Data Distiller Jobs
    • ETL 300: Incremental Processing Using Checkpoint Tables in Data Distiller
    • [DRAFT]ETL 400: Attribute-Level Change Detection in Profile Snapshot Data
  • UNIT 4: DATA DISTILLER DATA ENRICHMENT
    • ENRICH 100: Real-Time Customer Profile Overview
    • ENRICH 101: Behavior-Based Personalization with Data Distiller: A Movie Genre Case Study
    • ENRICH 200: Decile-Based Audiences with Data Distiller
    • ENRICH 300: Recency, Frequency, Monetary (RFM) Modeling for Personalization with Data Distiller
    • ENRICH 400: Net Promoter Scores (NPS) for Enhanced Customer Satisfaction with Data Distiller
  • Unit 5: DATA DISTILLER IDENTITY RESOLUTION
    • IDR 100: Identity Graph Overview
    • IDR 200: Extracting Identity Graph from Profile Attribute Snapshot Data with Data Distiller
    • IDR 300: Understanding and Mitigating Profile Collapse in Identity Resolution with Data Distiller
    • IDR 301: Using Levenshtein Distance for Fuzzy Matching in Identity Resolution with Data Distiller
    • IDR 302: Algorithmic Approaches to B2B Contacts - Unifying and Standardizing Across Sales Orgs
  • Unit 6: DATA DISTILLER AUDIENCES
    • DDA 100: Audiences Overview
    • DDA 200: Build Data Distiller Audiences on Data Lake Using SQL
    • DDA 300: Audience Overlaps with Data Distiller
  • Unit 7: DATA DISTILLER BUSINESS INTELLIGENCE
    • BI 100: Data Distiller Business Intelligence: A Complete Feature Overview
    • BI 200: Create Your First Data Model in the Data Distiller Warehouse for Dashboarding
    • BI 300: Dashboard Authoring with Data Distiller Query Pro Mode
    • BI 400: Subscription Analytics for Growth-Focused Products using Data Distiller
    • BI 500: Optimizing Omnichannel Marketing Spend Using Marginal Return Analysis
  • Unit 8: DATA DISTILLER STATISTICS & MACHINE LEARNING
    • STATSML 100: Python & JupyterLab Setup for Data Distiller
    • STATSML 101: Learn Basic Python Online
    • STATSML 200: Unlock Dataset Metadata Insights via Adobe Experience Platform APIs and Python
    • STATSML 201: Securing Data Distiller Access with Robust IP Whitelisting
    • STATSML 300: AI & Machine Learning: Basic Concepts for Data Distiller Users
    • STATSML 301: A Concept Course on Language Models
    • STATSML 302: A Concept Course on Feature Engineering Techniques for Machine Learning
    • STATSML 400: Data Distiller Basic Statistics Functions
    • STATSML 500: Generative SQL with Microsoft GitHub Copilot, Visual Studio Code and Data Distiller
    • STATSML 600: Data Distiller Advanced Statistics & Machine Learning Models
    • STATSML 601: Building a Period-to-Period Customer Retention Model Using Logistics Regression
    • STATSML 602: Techniques for Bot Detection in Data Distiller
    • STATSML 603: Predicting Customer Conversion Scores Using Random Forest in Data Distiller
    • STATSML 604: Car Loan Propensity Prediction using Logistic Regression
    • STATSML 700: Sentiment-Aware Product Review Search with Retrieval Augmented Generation (RAG)
    • STATSML 800: Turbocharging Insights with Data Distiller: A Hypercube Approach to Big Data Analytics
  • UNIT 9: DATA DISTILLER ACTIVATION & DATA EXPORT
    • ACT 100: Dataset Activation with Data Distiller
    • ACT 200: Dataset Activation: Anonymization, Masking & Differential Privacy Techniques
    • ACT 300: Functions and Techniques for Handling Sensitive Data with Data Distiller
    • ACT 400: AES Data Encryption & Decryption with Data Distiller
  • UNIT 9: DATA DISTILLER FUNCTIONS & EXTENSIONS
    • FUNC 300: Privacy Functions in Data Distiller
    • FUNC 400: Statistics Functions in Data Distiller
    • FUNC 500: Lambda Functions in Data Distiller: Exploring Similarity Joins
    • FUNC 600: Advanced Statistics & Machine Learning Functions
  • About the Authors
Powered by GitBook
On this page
  • Prerequisites
  • Introduction
  • Data Distiller Accelerated Store
  • CRUD Support in Accelerated Store
  • REST API Support in Accelerated Store
  • Data Distiller Data Models
  • Data Distiller Data Views
  • Data Distiller Charts
  • Download as PDF
  • ViewMore and ViewSQL
  • Data Distiller Drilthroughs
  • SQL Authoring of Charts
  • Data Distiller Global Filters
  • Data Distiller Date Filters
  • Pushdown Filters in Drillthroughs
  • BI Connectivity to Accelerated Store
  1. Unit 7: DATA DISTILLER BUSINESS INTELLIGENCE

BI 100: Data Distiller Business Intelligence: A Complete Feature Overview

Unlock insights with Data Distiller dashboards featuring advanced queries, customizable filters, drillthroughs, built-in SQL, and accelerated querying, all integrated seamlessly with BI tools.

Last updated 6 months ago

Prerequisites

Introduction

Data Distiller enable you to create powerful, enterprise-grade dashboards that rival the best-in-class dashboards found in traditional business intelligence (BI) tools. You don't need to rely on expensive BI vendors or invest extra resources for users to build and consume insights effectively. By leveraging SQL as the foundation for creating dashboards, you can unlock the full potential of your data without the need for additional software or tools.

Data Distiller dashboards designed with two key audiences in mind: the data team and the business users. The data team will focus on building foundational charts, ensuring that they include the necessary filters and dimensions to provide flexibility. This allows business users to perform deep, drill-down analyses and gain actionable insights directly from the dashboards. This approach streamlines the workflow, enabling teams to quickly create interactive and insightful dashboards without having to leave the Data Distiller environment.

Data Distiller Dashboards UI and backend have undergone a major revamp.

Data Distiller Accelerated Store

Instead of writing queries directly against the AEP Data Lake, you can use the accelerated store, which features a high-performance engine designed for faster dashboard queries. This engine significantly enhances the speed and efficiency of data retrieval, ensuring that dashboards load quickly without compromising data accuracy. Best of all, this capability is included as part of the Data Distiller license, making it a seamless, cost-effective solution for boosting query performance in your dashboards

CRUD Support in Accelerated Store

MERGE INTO and UPDATE/DELETE syntax enables you to update/delete records between your source table and the target table in the Accelerated Store that allows you to do low volume surgical deletes.

REST API Support in Accelerated Store

You can leverage REST APIs to post queries from any application and retrieve the results as JSON. This allows for seamless integration of Data Distiller’s querying capabilities into your own applications, providing flexible and automated access to data insights for reporting, analysis, or further processing.

Data Distiller Data Models

A Data Distiller Data Model, much like a reporting star schema, organizes customer and campaign data for efficient insights. The fact table can store key metrics like campaign impressions, clicks, or purchases, while dimension tables provide additional context, such as customer demographics, product categories, or marketing channels. This structure enables marketers to quickly analyze performance across various dimensions, track campaign effectiveness, and identify trends, offering a clear view of customer behavior and campaign ROI. The star schema of the data model enhances query performance, making it ideal for marketing dashboards and ad hoc reporting.

A normalized relational data model is optimized for representing data in a compact and efficient way by minimizing redundancy and organizing information across multiple related tables. Each piece of data is stored only once, reducing storage needs and avoiding duplication. This design allows for updates, like changes in a dimension (e.g., customer data), to be made in one place without reprocessing the entire dataset. As a result, updates are more efficient, saving processing time and ensuring consistency across related data in different tables.

Data Distiller Data Views

Data Distiller Data Views addresses the usability challenges of normalized data models by enabling data engineers to create flat views tailored for marketing users. These flat views present the underlying data in a simplified format, making it easy for users to build dashboards or perform ad hoc analysis without dealing with the complexity of the normalized model. This approach ensures that marketing teams can work with familiar, user-friendly data structures, streamlining their analysis while maintaining the benefits of the underlying model's efficiency and flexibility.

Data Distiller Charts

A Data Distiller Dashboard typically includes several key visualizations: Big Numbers (KPIs) to highlight critical metrics like total web traffic, Line Charts to track trends over time (e.g., traffic or sales), Bar Charts to compare categories (like products or regions), Donut Charts to show proportions of a total, and Tables to present raw, detailed data for deeper analysis. These elements together provide a comprehensive view of performance and insights, enabling quick decision-making and further investigation

Download as PDF

You can easily download the entire dashboard as a single-page PDF, making it convenient to share with stakeholders. This feature is especially useful for presenting key insights in a visually organized format, allowing stakeholders to review and understand the data without needing access to the dashboard itself. It ensures consistency in reporting and facilitates clear communication, whether for meetings, presentations, or email sharing.

ViewMore and ViewSQL

In a Data Distiller Dashboard, each chart can be expanded to view the underlying data, displayed in a table format with pagination, making it easy to explore like an Excel file. You can also download the data as a CSV for further analysis. The ViewSQL feature reveals the SQL query used to generate the chart, allowing users to reuse the query in other charts or add it to their private LLM for advanced personalization and modeling. This flexibility enhances both data exploration and customization.

Data Distiller Drilthroughs

Data Distiller Drillthroughs are a feature that allows users to explore data more deeply by clicking on a chart within a dashboard and being directed to another detailed report or dashboard. The purpose of a drillthrough is to provide context and further insights without overwhelming the primary dashboard with excessive details.

For example, if a user clicks on a marketing leads figure in a regional dashboard, a drillthrough might show detailed transaction records or performance metrics specific to that region, helping users investigate trends or anomalies efficiently.

SQL Authoring of Charts

One of the main challenges with using drag-and-drop interfaces to create charts is the lack of flexibility when it comes to defining custom metrics. While these interfaces are convenient, they often fall short in handling more complex calculations. On the other hand, SQL provides unmatched flexibility in metric definition. For instance, if you want to visualize a trailing 30-day average using a rolling window for each date, achieving this in a typical drag-and-drop dashboard interface would be nearly impossible. You would likely need to recompute the entire metric at the ETL layer instead.

Our goal of building this feature was to unleash the flexibility of SQL at the chart authoring layer.

Data Distiller Global Filters

We’ve adopted an innovative approach to filter design by allowing filters to be created at the dashboard level while giving you full control over how they’re applied within individual charts. This flexibility enables sophisticated filter logic, where both local filters and the chart’s context work together to determine how the filter impacts the data displayed. This advanced filter design ensures greater customization and precision, allowing you to tailor the behavior of each chart based on specific business needs.

Data Distiller Date Filters

An advanced date picker-style filter, offering both date range selection and preset options, can be applied to charts within the dashboard. This feature allows users to quickly customize date-based filters, enhancing flexibility and precision in data analysis.

Pushdown Filters in Drillthroughs

When a drillthrough is applied on a dashboard, the global filter can also be applied to the child elements, provided they are connected to the same filter. This ensures that the global filter will influence all related child charts and reports within the dashboard, even when navigating deeper into the data through drillthrough actions. This setup maintains consistency across visualizations and enhances the interactivity of the dashboard.

BI Connectivity to Accelerated Store

You can integrate your preferred BI tool with the data models stored in the Accelerated Store, enabling seamless access to high-performance, optimized data. This allows users to leverage the power of the Accelerated Store's query engine while continuing to work within familiar BI environments for dashboard creation, reporting, and analysis. The flexibility of this integration ensures that businesses can take advantage of both their existing BI tools and the advanced capabilities of the Accelerated Store without sacrificing speed or efficiency.

Audience analysis including overlaps and identity composition
Conceptual mock of a Data Distiller Data Model
PREP 303: What is Data Distiller Business Intelligence?
BI Dashboards built with Data Distiller
Page cover image