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
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  1. UNIT 1: GETTING STARTED

PREP 100: Why was Data Distiller Built?

Last updated 8 months ago

Before you start, you need to understand the philosophical underpinnings of why Data Distiller exists as a product in the first place. The guide contains a lot of examples of how to use the product. The product capabilities are very powerful and are evolving fast.

If you focus on this as a tool to get by, you will completely miss the point. The value is not what the product can do and its capabilities but how it fits as a key component in their overall experience delivery strategy. Here is how that argument goes.

I am going to assume that the reason why you are reading this documentation is because:

  1. You or your organization wants to make a positive impact in the world by changing things for the better.

  2. You want to make this impact via the delivery of products or services.

  3. The unit of how your users/customers/world will experience what you will offer is an "experience"

The fundamental equation in delivering an experience is:

Experience = Product/Service/Offer + Engagement + Data

Your products or services ultimately define what you have to offer to your customers. This is the reason why you exist in the first place. But that is not going to cut it because you need to engage with them i.e. a means by which they experience the product from awareness to becoming a champion of it. If you have the greatest product but cannot engage- be ready to fail.

Engagement is dependent on when and how you talk to them. The place where you engage becomes the channel, the style of communication, and the content you use (text, images, video). The sum total packaging of these elements and how it manifests when delivered is also critical. But if you do not understand where, who, when, and what to engage with, you are still going to fail.

You will have to work with the data to figure that out. In fact, you have to use the same data to design the product. No. You will create products and engagements that will generate the data so that you can serve your customers better.

If you cannot do this, there are not enough good reasons as to why you should exist as a business.

Reality Check: For as long as we have collected data as a civilization, data has been messy. The messiness is just a projection of the complexity of how we operate as agents. As we build more complex systems, the data we will need to collect about them will increase and become messier.

Corollary: Just having a ton of data and analyzing that data all day is no good either. You will know a lot about how the world works but to make an impact, you need a product.

Data Distiller is one of the data products in the Adobe Experience Platform that is architected to solve your data problems so that you are empowered to deliver the best experience.

Customers experience what you offer in chunks called as an "experience" that is powered by the 3 elements.
All components such as.your offering, engagement and data need to be managed well enough to deliver an exceptional experience.
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