PRE 101: Data Distiller Use Case & Capability Matrix Guide
Navigate your data journey with precision—empower every decision with the Data Distiller Use Case & Capability Matrix
Overview
The Data Distiller Use Case & Capability Matrix serves as a comprehensive guide to understanding how various capabilities of Data Distiller can be leveraged to meet critical business objectives. This framework outlines key use cases such as customer data onboarding, ETL (extract, transform, load) operations, and batch audience segmentation. Each use case is paired with descriptions, benefits, and core functionalities that enhance the efficiency of data-driven processes. By utilizing these capabilities, organizations can improve consistency in marketing efforts, streamline data transformations within their data lakes, and drive large-scale audience segmentation with actionable insights. This matrix provides a clear path to unlocking the power of data through tailored solutions and features that address specific data challenges.
How to Use the Data Distiller Use Case & Capability Matrix
To effectively use the Data Distiller Use Case & Capability Matrix, start by identifying the primary business goals your team is looking to achieve, whether it's customer data onboarding, data transformation, or audience segmentation. For each goal, review the corresponding use case in the matrix to understand the relevant capabilities and their benefits. This will guide you in selecting the appropriate Data Distiller functionalities, such as exploration tools, batch engines, or orchestration frameworks, to meet your needs. It's important to assess how each capability aligns with your specific marketing objectives and technical infrastructure. While the matrix simplifies decision-making, successful implementation requires close collaboration between your marketing and data teams. The data team must be actively involved to ensure the right data is available, properly transformed, and integrated into your workflows, enabling marketing to make data-driven decisions that are accurate and actionable.
Data Distiller Use Case & Capability Matrix
The Key Capabilities column outlines a subset of core features you’ll likely use, but you may find yourself leveraging many additional functionalities. Treat these as a starting point rather than an exhaustive list. For instance, it doesn’t mention Data Distiller Query Pro Mode, the advanced SQL editor used to author SQL for all the use cases listed below.
Check the comprehensive Data Distiller Capabillity Matrix below.
The following use case list represents over six years of Data Distiller implementations across various industry verticals and organizations of all sizes.
| Use Case | Description | Benefits | Key Capabilities |
|---|---|---|---|
Customer Data Onboarding & Activation | Onboard offline customer data and activate it across online platforms for more comprehensive retargeting. | Improve consistency and reach across marketing channels. | Data Distiller Exploration, Data Distiller Functions & Extensions, Data Distiller Orchestration |
ETL and Data Transformation | Perform data extraction, transformation, and loading (ETL) tasks within AEP data lake. | Streamline data transformation directly in the AEP data lake, reducing the need for external ETL tools. | Data Distiller Exploration, Data Distiller Functions & Extensions, Data Distiller Orchestration |
Batch Audience Segmentation | Periodically process customer data in batches to create audience segments based on purchase behavior, demographics, or engagement levels. Activate these audiences in Adobe Real-Time CDP and Adobe Journey Optimizer | Enables large-scale audience segmentation and provides marketers with up-to-date, actionable customer lists for targeted campaigns. | Data Distiller Audiences, Data Distiller Orchestration |
Real-Time Personalization & Offers | Deliver dynamic, personalized offers in Adobe Real-Time CDP, Adobe Target and Adobe Journey Optimizer based on real-time customer interactions. | Increase engagement and conversion rates through timely, relevant content. | Data Distiller Derived Attributes, Data Distiller Orchestration |
Content and Offer Recommendations at Scale | Batch-process customer interaction and purchase history data to generate personalized content or product recommendations. For example, nightly batch jobs can update recommendation models for email campaigns, ensuring that the right products or offers are surfaced. | Enhances customer engagement by delivering relevant recommendations at scale, personalized based on the latest customer data. | Data Distiller Derived Attributes, Data Distiller Orchestration, Data Distiller Insights |
Batch Data Integration for Customer 360 Profiles | Batch-process data from multiple sources (CRM, social media, transactional data, web analytics) to periodically update complete customer profiles in the data lake. These profiles can be used to deliver personalized experiences and communications across channels. | Ensures that customer profiles remain up to date and comprehensive, enhancing personalization efforts. | Data Distiller Derived Attributes, Data Distiller Orchestration, Data Distiller Insights |
Customer Lifetime Value (CLV) Modeling | Model the long-term value of customers using transactional and behavioral data. | Focus marketing spend on high-value customers and optimize retention efforts. | Data Distiller Derived Attributes, Data Distiller Orchestration, Data Distiller Statistics |
Compliance Audits and Data Governance | Data Distiller can run batch processes to audit marketing data for compliance with regulations like GDPR, CCPA, or other data privacy standards. This could include identifying and anonymizing sensitive data, tracking opt-ins and opt-outs, and ensuring data usage aligns with legal requirements. | Ensures that marketing activities remain compliant with privacy regulations, reducing the risk of penalties and enhancing customer trust. | Data Distiller Exploration, Data Distiller Orchestration |
Cross-Sell and Upsell Opportunity Identification | Use batch processing to analyze customer purchase history and identify cross-sell and upsell opportunities. For instance, weekly batch jobs can surface customers who recently purchased complementary products, allowing marketers to target them with relevant offers. | Drives additional revenue by identifying and capitalizing on opportunities for cross-selling and upselling. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Insights |
Long-term Customer Retention and Loyalty Program Analysis | Batch-process customer loyalty and retention data to analyze trends and the effectiveness of retention strategies. For example, monthly batch jobs can evaluate the success of loyalty programs, discount campaigns, and re-engagement efforts. | Helps refine retention strategies by providing regular, data-driven insights into what drives customer loyalty. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Insights |
Customer Migration Analysis | Batch-process historical customer data to analyze patterns of customer migration between segments (e.g., frequent buyers to inactive customers). This analysis helps identify why customers move between different value segments and can trigger retention or re-engagement campaigns. | Reduces churn and increases customer lifetime value by identifying early signals of customer migration. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Insights |
Competitive Benchmarking & Market Analysis | Batch-process data on competitor marketing efforts (e.g., social media activity, ad campaigns) and compare it to your own. This data can be collected from third-party services or public sources and analyzed to understand market positioning and identify competitive gaps. | Helps marketers adjust their campaigns based on competitor strategies and market trends. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Insights |
Historical Campaign Performance Benchmarking | Run batch jobs to process historical campaign data and create benchmarks for marketing performance (e.g., click-through rates, conversion rates) across various channels and periods. This allows marketers to measure current campaigns against historical benchmarks. | Provides context for campaign performance by offering benchmarks based on past results, enabling better goal setting and evaluation. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Insights |
Attribution Reporting Over Long Periods | Run batch jobs to compute marketing attribution for extended periods (e.g., quarterly or yearly). This can involve processing massive datasets from multiple campaigns, touchpoints, and channels to calculate performance metrics using various attribution models (e.g., multi-touch, first-touch, last-touch). | Provides a holistic, long-term view of campaign effectiveness and helps allocate future marketing budgets based on historical performance. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Insights |
Sales & Marketing Alignment | Unify marketing and sales data to provide a complete view of the customer journey from lead to conversion. | Improve collaboration and drive revenue growth by identifying effective strategies. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Activation, Data Distiller Insights |
Batch Processing for Lead Scoring | Run batch jobs to score leads based on historical interaction data (e.g., email opens, clicks, form submissions) and assign predictive lead scores. This scoring can be refreshed daily or weekly, helping sales and marketing teams focus on high-potential prospects. | Improves lead prioritization by automating the lead scoring process based on batch-analyzed historical data. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Statistics, Data Distiller Insights |
Data Driven Budget Optimization | Leverage Data Distiller’s SQL capabilities to analyze historical campaign performance and allocate future marketing budgets based on the best-performing channels and segments. | Maximize ROI by focusing spend on the best-performing channels and segments. Ensure optimal use of marketing budgets by focusing spend on strategies that deliver the highest returns. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Insights |
Campaign Data Cleanup and Standardization | Periodically run batch processes to clean, standardize, and enrich marketing data from disparate sources (e.g., social media, CRM, and transactional data). This includes removing duplicates, filling in missing data, and ensuring consistency across datasets before further analysis. | Improves data quality, leading to more accurate analytics, reporting, and decision-making. | Data Distiller Exploration, Data Distiller Orchestration |
Batch Omnichannel Campaign Performance Analysis Use Case | Data Distiller enables the aggregation of data from multiple marketing channels, such as email, social media, and paid search, to provide a holistic view of campaign performance. Through batch processing, it delivers a comprehensive analysis of large-scale marketing efforts over time, uncovering trends and optimization opportunities across all touchpoints that single-channel or real-time data may miss. | Deeper insights into marketing effectiveness, enabling strategy adjustments based on historical trends and outcomes. Batch analysis with Data Distiller ensures accurate data, empowering marketers to make informed, data-driven decisions for more effective omnichannel strategies. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Insights |
Time-Series Analysis for Marketing Trends | Periodically process large datasets to identify long-term marketing trends using time-series analysis. For example, a batch process could analyze customer engagement over time to spot seasonal patterns or emerging behavior trends. | Informs long-term marketing strategy by identifying shifts in customer behavior and campaign performance over extended periods. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Insights |
Post-Campaign Analysis | Batch jobs can process data after the completion of marketing campaigns to generate post-campaign reports, including performance metrics, audience engagement, and ROI. This can be run at the end of each campaign cycle. | Provides detailed post-mortem insights into campaign success and areas for improvement, informing future campaigns. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Insights |
Marketing Data Warehouse Updates | Periodically ingest and process marketing performance data from various platforms (Google Ads, Facebook Ads, email platforms) into a unified marketing data warehouse. This allows for scheduled updates to marketing dashboards or reporting systems. | Provides centralized and up-to-date marketing performance insights that are accessible across teams. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Activation |
Customer Journey Stitching Across Channels | Batch jobs can be run to stitch together customer interactions from different channels (e.g., mobile, desktop, in-store). This provides a unified view of the entire customer journey, allowing for deeper insights into how customers interact with various touchpoints before conversion. | Allows marketers to understand how different channels contribute to the overall customer experience, helping refine omnichannel strategies. | Data Distiller Exploration, Data Distiller Orchestration |
Scheduled A/B Test Performance Analysis | Automate the analysis of A/B test results by running batch jobs that process performance data from multiple tests (e.g., different ad creatives or email subject lines). Batch processing allows for timely comparison of performance across test groups. | Automates the evaluation of A/B tests at scale, allowing marketers to quickly identify winning strategies and optimize campaigns. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Insights |
Market Basket Analysis | Use batch processing to analyze large volumes of transaction data to identify which products are commonly purchased together (market basket analysis). This data can then inform product bundling strategies or personalized offers. | Helps optimize merchandising and product recommendations by identifying patterns in customer purchase behavior. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Insights |
Lookback Windows for Event-Based Campaigns | Batch-process data to evaluate customer behavior during specific lookback windows (e.g., 7 days, 30 days). This can be used to trigger event-based campaigns, such as re-engagement emails for customers who haven’t purchased in the last 30 days. | Enables timely, event-based marketing campaigns that are triggered based on customer behavior over specific time windows. | Data Distiller Exploration, Data Distiller Derived Attributes, Data Distiller Orchestration |
Ad Spend Optimization | Run batch jobs to analyze and optimize ad spend across channels. These jobs can look at historical performance data, cost-per-click (CPC), return on ad spend (ROAS), and other metrics to recommend optimal budget allocation. | Maximizes marketing ROI by providing insights into where ad spend is most effective and where adjustments are needed. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Insights |
Customer Feedback Aggregation | Batch-process customer feedback data (e.g., surveys, product reviews) collected across multiple channels to generate insights into customer satisfaction and sentiment. This can be done monthly or quarterly to inform product and marketing strategies. | Helps marketers understand customer sentiment and improve messaging or product offerings based on aggregated feedback. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Statistics |
Seasonal Trend Analysis | Data Distiller can run batch jobs to analyze sales and customer engagement data over multiple years to detect seasonal trends and predict future demand. This helps marketers adjust inventory, promotions, and campaign timing based on historical trends. | Optimizes seasonal marketing efforts by aligning promotions with peak demand periods based on historical data. | Data Distiller Exploration, Data Distiller Orchestration |
Batch Data Anonymization for Data Sharing | For data sharing across partners or for data collaboration in a clean room, Data Distiller can regularly anonymize large datasets through batch processing. This could include hashing, tokenization, or other privacy-preserving techniques before sharing data with external partners. | Enables privacy-compliant data sharing for joint marketing activities or external analysis, while protecting individual customer data. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Activation |
Batch-Powered Dynamic Pricing | Data Distiller can be used to run batch jobs that analyze pricing data in combination with competitive data, demand trends, and customer behavior. Based on the results, dynamic pricing models can be adjusted periodically to optimize pricing strategies for promotions or specific customer segments. | Increases revenue by optimizing prices based on real-time market conditions and customer willingness to pay. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Statistics, Data Distiller Insights |
Post-Purchase Experience Optimization | Batch-process customer feedback, return data, and post-purchase behavior to identify friction points in the post-purchase experience (e.g., product returns, negative feedback). This analysis can lead to improved communication strategies, such as targeted post-purchase emails or customer support outreach. | Enhances customer satisfaction by proactively addressing post-purchase issues, leading to improved customer retention. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Statistics, Data Distiller Insights |
Inventory-Based Marketing Automation | Batch jobs can process inventory data to adjust marketing campaigns in real-time. If certain products are low in stock or overstocked, marketing campaigns can be adjusted to feature promotions or highlight alternative products. | Aligns marketing efforts with current inventory levels, ensuring customers are shown relevant products and preventing the promotion of out-of-stock items. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Statistics, Data Distiller Insights |
Regional Campaign Analysis for Global Brands | Data Distiller can process data in batches to compare marketing performance across different regions or markets. This could include understanding which messaging, products, or channels work best in each region, allowing global brands to localize their campaigns more effectively. | Increases marketing effectiveness by tailoring strategies to the specific needs and behaviors of customers in different regions. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Statistics, Data Distiller Insights |
Behavioral Retargeting Analysis | Batch jobs can analyze browsing behavior, cart abandonment, or product interactions to power retargeting campaigns. Data Distiller can process this data to identify customers who have interacted with certain products but haven’t purchased, allowing for targeted remarketing campaigns | Increases sales by identifying potential buyers based on their browsing behavior and targeting them with relevant offers. | Data Distiller Exploration, Data Distiller Derived Attributes, Data Distiller Orchestration |
Batch Processing for Predictive Maintenance of Marketing Campaigns | Analyze historical campaign data in batches to predict when ongoing campaigns may require updates, changes in creative, or shifts in messaging. This could help marketing teams adjust campaigns before performance declines. | Maintains the effectiveness of long-running campaigns by proactively adjusting strategies based on predictive insights. | Data Distiller Exploration, Data Distiller Derived Attributes, Data Distiller Orchestration |
Influencer Marketing Performance Analysis | Batch-process data from influencer campaigns (e.g., social media engagement, conversion rates) to analyze their effectiveness. This could be used to identify which influencers drive the most conversions and engagement, allowing marketers to refine their influencer partnerships. | Optimizes influencer marketing spend by focusing on partnerships that deliver the best ROI. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Insights |
Personalization Attributes for Campaign Activation | Batch processing to join and manipulate data from multiple sources like analytics, product pricing, and customer profiles to derive personalized fields. | Enables personalized emails based on customer behavior (e.g., abandoned cart). | Data Distiller Exploration, Data Distiller Derived Attributes, Data Distiller Orchestration |
Merging & Pivoting Data from Multiple Brands for CLV (Customer Lifetime Value) | Combine and standardize sales data from different departments, clean it for inconsistencies, and calculate custom CLV through batch processing. | Provides unified sales data for better insights and personalized customer profiles. | Data Distiller Exploration, Data Distiller Derived Attributes, Data Distiller Orchestration, Data Distiller Insights |
Unify Datasets under Single Identity for Attribution | Update all datasets with a master identity column for advanced attribution modeling, unifying data under a single customer identity (ECID, AAI ID). | Enhances marketing attribution and allows cross-channel analysis (e.g., click-to-brick behavior). | Data Distiller Exploration, Data Distiller Functions & Extensions, Data Distiller Orchestration |
Segment Sharing in AEP Apps (RTCDP & CJA) | Batch process out-of-box datasets from AEP to create experience event datasets for segment membership reporting in CJA. | Facilitates data sharing between AEP and CJA for marketing performance reporting. | Data Distiller Exploration, Data Distiller Functions & Extensions, Data Distiller Orchestration |
Customer 360 Data Model for Reporting | Combine data from multiple customer touchpoints (transactions, CRM, browsing history) to create a customer-centric data model for BI reporting. | Enables personalized BI dashboards with detailed customer insights (e.g., frequency of visits, spend per customer). | Data Distiller Exploration, Data Distiller Derived Attributes, Data Distiller Orchestration, Data Distiller Insights |
Audience Segmentation for Retargeting | Batch processing to track prospects who searched for ineligible services (e.g., 5G/LTE) and retarget when services become available in their area. | Activates prospects based on real-time changes in service availability. | Data Distiller Exploration, Data Distiller Functions & Extensions, Data Distiller Orchestration |
Suppression for Adobe Journey Optimizer Segments | Extract journey history from logs and create attributes on profiles to suppress over-communication in marketing journeys. | Helps manage communication frequency and avoid customer fatigue. | Data Distiller Exploration, Data Distiller Derived Attributes, Data Distiller Orchestration |
Derived Attributes for Product Recommendations | Batch process browsing and transaction data to create datasets for personalized product recommendations based on customer history and preferences. | Drives upsell and cross-sell opportunities with personalized offers. | Data Distiller Exploration, Data Distiller Derived Attributes, Data Distiller Orchestration |
Bot Detection Pipeline for Ad Campaigns | Identify bot patterns using batch processing of click and interaction data, apply machine learning models for bot filtering, and refine data for reporting. | Increases ad spend efficiency by excluding bot-generated traffic. | Data Distiller Exploration, Data Distiller Statistics, Data Distiller Orchestration |
Consolidated Lookup Tables for Data Transformation | Batch processing to build a master lookup table from multiple sources, ensuring consistency across datasets used in Customer Journey Analytics (CJA). | Improves data accuracy for downstream reporting and analysis. | Data Distiller Exploration, Data Distiller Functions & Extensions, Data Distiller Orchestration |
Identity Graph Segmentation | Explode segment memberships and aggregate data for identity validation, ensuring consistent identity mapping across datasets. | Ensures accurate segmentation and identity consistency for marketing campaigns. | Data Distiller Exploration, Data Distiller Functions & Extensions, Data Distiller Orchestration |
Business Logic on Data Contracts for Campaign Optimization | Explode data from multiple sources, apply business logic (e.g., loyalty status, contract details) and use window functions to prepare datasets for profile-based campaigns. | Optimizes campaigns with personalized messaging based on business-specific rules. | Data Distiller Exploration, Data Distiller Functions & Extensions, Data Distiller Orchestration |
Next Best Offer Using Derived Attributes | Batch process browsing and purchase history to generate next best offer datasets, used for personalized email and product recommendations. | Drives conversion by delivering timely, relevant offers to customers. | Data Distiller Exploration, Data Distiller Derived Attributes, Data Distiller Orchestration |
Sales & Marketing Insights Reporting | Ingest data from multiple sources (3rd party services, Adobe Real Time CDP & Marketo), process through batch operations, and generate insights dashboards for sales and marketing. | Provides real-time insights into sales and marketing performance across regions and channels. | Data Distiller Exploration, Data Distiller Derived Attributes, Data Distiller Orchestration, Data Distiller Insights |
Flattening Nested Data for Customer Journey Analytics | Flatten highly nested retail or event data to prepare it for ingestion into analytics platforms like CJA for detailed customer interaction analysis. | Simplifies complex data structures for better analytics and reporting. | Data Distiller Exploration, Data Distiller Functions & Extensions, Data Distiller Orchestration |
Derived Attributes for Customer Churn Prediction | Batch process customer interaction data to identify churn risks and create datasets for retention campaign activation. | Reduces churn by targeting at-risk customers with proactive retention offers. | Data Distiller Exploration, Data Distiller Derived Attributes, Data Distiller Orchestration |
Cross-Brand Affinity Scores | Data Distiller batch processes customer's browsing and purchase data across multiple brands to identify cross-brand affinities. By analyzing nteractions (e.g., fashion items from Brand A, beauty products from Brand B), the system generates personalized recommendations that span her interests across these brands, providing a comprehensive view of her preferences. | This approach enhances cross-sell opportunities, boosts customer engagement, and fosters brand loyalty by delivering relevant product suggestions across brands. It also drives higher revenue through personalized recommendations, offering a unified shopping experience tailored to Susan’s cross-brand preferences. | Data Distiller Exploration, Data Distiller Derived Attributes, Data Distiller Orchestration, Data Distiller Insights |
Derived Attributes: Engaged, Re-engaged, Active, Inactive, Return Order Counts, Preferred Brand | Data Distiller derives summary aggregates from cross-brand profiles and behavioral data (e.g., engagement status, return order counts, preferred brands). These aggregates may vary by brand and customer level and are computed as attributes to be ingested into the customer profile. | Provides detailed insights into customer engagement and behavior across multiple brands. Enables personalization and targeted marketing by understanding customer preferences. Supports better decision-making with cross-brand metrics available at both brand and person levels. | Data Distiller Exploration, Data Distiller Derived Attributes, Data Distiller Orchestration, Data Distiller Insights |
Variance of a Derived Attribute | Data Distiller calculates time-series aggregates to capture the variance in computed attributes over time. These aggregates are timestamped with the current date and ingested into an Experience Event Schema for tracking historical changes. | Offers insights into how customer attributes evolve over time. Helps identify trends and patterns by comparing past and present aggregated data. Improves forecasting and marketing strategies by leveraging time-series data for variance analysis. | Data Distiller Exploration, Data Distiller Derived Attributes, Data Distiller Orchestration, Data Distiller Insights |
Derived Attributes from Adobe Analytics Data | Data Distiller processes clickstream events from Adobe Analytics to derive key customer engagement metrics such as the last viewed product, style color, cart ID, and timestamps for critical events (e.g., product views, cart additions, and page views). These attributes are used to track the most recent customer interactions across the site, including product browsing and cart activity. | Provides insights into customer preferences by tracking key engagement metrics, enabling targeted product recommendations and personalized marketing. By identifying abandoned carts and tracking products viewed or added, it supports effective retargeting campaigns to improve conversion rates. Additionally, it enhances the overall user experience by analyzing browsing patterns to optimize product offerings and site navigation based on customer behavior. | Data Distiller Exploration, Data Distiller Derived Attributes, Data Distiller Orchestration, Data Distiller Insights |
Pipeline Management and Forecasting | By analyzing historical sales and opportunity data, Data Distiller can predict future revenue, identify bottlenecks in the pipeline, and provide insights into the likelihood of deals closing. | Provides data-driven forecasting and pipeline management, helping sales teams allocate resources effectively. | Data Distiller Exploration, Data Distiller Statistics, Data Distiller Insights |
B2B Data Quality Management | Data Distiller automates B2B data cleaning, validation, and standardization, addressing duplicates, incomplete records, and inconsistent formats. It enriches datasets with missing information and provides continuous monitoring of data quality metrics like accuracy and completeness. | Enhances decision-making with accurate data, improves customer segmentation for personalized marketing, boosts efficiency by reducing manual corrections, ensures compliance, and optimizes CRM and marketing performance by eliminating data friction. | Data Distiller Exploration, Data Distiller Orchestration, Data Distiller Insights |
Custom Audience Format Export | Data Distiller allows the creation and export of custom audience segments in formats tailored for various marketing platforms, ensuring seamless integration with external systems like CRM, ad platforms, and email tools. | This capability streamlines data sharing, enables precise targeting, and enhances campaign efficiency by delivering well-defined audience segments ready for use across multiple channels. | Data Distiller Activation, Data Distiller Orchestration. |
Data Distiller Capability Matrix
Data Distiller is often referred to as the "Swiss army knife" of the platform due to its extensive feature set, offering incredible flexibility to tackle a wide array of custom use cases tailored to your organization’s unique needs. It is built on a foundation of powerful, massive-scale data processing and analytical engines, making it a versatile and essential tool.
The following Data Distiller capabilities are required for the above use case implementations and are not included in Adobe Experience Platform applications. They require a separate Data Distiller license:
| Capabiity | Description |
|---|---|
Data Distiller Query Pro Mode | This refers to the advanced SQL Query Editor, offering an object browser for easy exploration, a detailed query log with search capabilities, and full visibility into orchestration jobs and schedules. It includes query-saving functionality and is integrated with Data Distiller Insights for SQL-based chart creation. Additionally, Query Pro Mode allows you to connect third-party editors and BI tools to Data Distiller through IP whitelisting. |
This refers to the capability of querying the AEP Data Lake on massive relational or semi-structured datasets. Data Distiller's engine is highly optimized for querying deeply nested data. Data Distiller's ad hoc query engine dynamically scales (serverless) with user demand, democratizing data exploration. Additionally, it increases the limits on concurrent query execution, ensuring smooth performance even with high system activity. The query timeout in this mode is set to 10 minutes, providing ample time to execute the vast majority of exploratory queries efficiently | |
This capability allows the creation of datasets on the data lake through scheduled batch jobs, which can be chained together, conditionally branched, and processed incrementally. These batch jobs can generate datasets that are ingestible into the Data Distiller Warehouse (Accelerated Store), Real-Time Customer Data Platform, and Customer Journey Analytics. Furthermore, Data Distiller provides visibility into the compute resources used for each job, down to fractional amounts, offering greater transparency and control over resource consumption. This engine delivers performance and scalability on par with leading market solutions, with a 24-hour timeout set for batch jobs to ensure extensive processing capabilities | |
Data Distiller Audiences | This capability enables the creation of batch audiences using SQL on the AEP Data Lake, which are automatically ingested as external audiences into Adobe Real-Time Customer Data Platform and Adobe Journey Optimizer. When combined with Data Distiller Orchestration, it supports both simple and complex audience composition tasks that go beyond the capabilities of most segmentation and campaign tools, allowing for more sophisticated audience targeting and optimization. External audiences in the Real-Time Customer Profile automatically expire after 30 days. |
This capability enables the creation of SQL-based attributes on data stored in the data lake, allowing for the development of more complex attributes that are typically challenging to author in a standard segmentation engine. It supports extended lookback periods and intricate, chained logic with windowing functions. Additionally, these derived attributes can be orchestrated and published to the Real-Time Customer Profile, making them available for segmentation and personalization across various destinations, including Adobe Journey Optimizer. | |
Data Distiller Templates | This capability allows you to create SQL fragments and parameters that can be reused and executed multiple times with different values. Inline templates help modularize your code by enabling the use of SQL code blocks throughout the program. |
Data Distiller Data Models | This capability allows you to structure datasets and views into a star schema format, both in the AEP Data Lake and the Data Distiller Warehouse. It also provides mechanisms to define primary and secondary key relationships between columns, facilitating efficient data organization and querying. |
This capability also known as the Accelerated Store, is an interactive engine designed for low-latency queries, ideal for dashboarding. It enables the creation of reporting star schemas, called Data Distiller Data Models, customized to meet your organization’s specific requirements. | |
This capability integrates with the Data Distiller Warehouse to build reporting star schemas and features Data Distiller Dashboards, offering charting capabilities, global filters, date pickers, and CSV downloads. It also allows for SQL-based authoring of complex charts and filters, surpassing the limitations of standard BI tools. Additionally, the warehouse seamlessly integrates with external BI tools for advanced data analysis. The Data Distiller Warehouse supports up to 4 concurrent queries and provides 500GB of data storage. | |
This capability enables the activation and export of Data Distiller datasets from Adobe Experience Platform to supported cloud storage destinations. Users can set batch schedules and export data in JSON or Parquet formats. Additionally, the system ensures that all data is activated incrementally, streamlining the process and optimizing resource use for efficient data handling. The activation size (GB) limits for the year are decided on your entitlement. | |
Data Distiller HyperCubes | This capability allows you to create cubes based on various slicing dimensions, enabling rapid computation of unique members across those dimensions. You can mix and match different cubes to efficiently obtain incremental counts of unique values, providing flexibility and speed in your data analysis. |
This capability allows you to sample data, generate detailed column statistics, perform approximate SUM and COUNT operations on large datasets, and extract statistical features for use in statistical models. | |
This capability enables the use of specialized functions for ETL transformations, including row-level operations such as array, string, math, and date manipulations, as well as anonymization functions. It also includes tools for attribution, sessionization, and pathing analysis on Adobe Analytics data. Additionally, lambda functions are available for performing more complex, custom operations. Data Distiller extensions, which are enhancements to the SQL syntax, allow for automation of tasks such as enabling datasets for profiles, schema authoring, and creating star schemas (data models) within the Data Distiller Warehouse (Accelerated Store). | |
Data Distiller Accelerators | This capability allows users to configure and execute common Data Distiller tasks and use cases by simply inputting the required parameters. |
Data Distiller Lake Storage | Data Distiller users get additional AEP Data Lake storage based on their entitlement. |
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