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Digital After Dark

Digital After Dark

著者： Digital After Dark
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 Two mates talking about all things Digital. Topics can cover Digital Analytics, Data, Transformation, Technology, Concepts and everything inbetween. If it is related to Digital, and we find it interesting, we are going to discuss it.Copyright Digital After Dark マーケティング マーケティング・セールス 出世 就職活動 経済学
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  • DAD009-The Power of Automation

    DAD009-The Power of Automation
    2025/06/05
    In this episode, Matt and Andrew have invited a special guest, Rajesh (from our team at Loop Horizon) to discuss Automation. The points covered on this episode are:

    Common Analytical Issues
    How can Automation resolve many of these issues
    Why is Automation Important
    Our aproach for using Automation
    Wrap-up

    This episode is unedited in any shape or form. The software tool I was using could not handle this episode so well; maybe because we were recording in the same room. Maybe it was because the internet was as good. I am not entirely sure, but I found it impossible to do anything with the recording. The next episode release will be using a new software tool; one I have partially used before.
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    1 時間 15 分
  • AT004: Adobe Summit 2025 Review of 8 Bit Insights: The Customer Journey Analytics Tips and Tricks

    AT004: Adobe Summit 2025 Review of 8 Bit Insights: The Customer Journey Analytics Tips and Tricks
    2025/06/01
    Andrew reviews Adobe Summit 2025 session of 8 Bit Insights: The Customer Journey Analytics Tips and Tricks by Trevor Paulsen. I recently tried a new podcast solution, as I was not the happiest with the previous one due to losing my audio and video. Spoiler warning - while this audio was better, the editing was not great. And the next podcast did not come out great at all. Hence, will be on the hunt for a new solution.

    As such, the AI notes is not as good for this episode. You can watch the session here: https://business.adobe.com/summit/2025/sessions/8bit-insights-the-customer-journey-analytics-s103.html

    Summary Notes
    • Gamified Learning Experience: The session used video game-inspired challenges tied to each of the 12 tips, with prizes like gift cards and an Adobe-branded Nintendo Switch to boost audience engagement.
    • Customer Journey Analytics (CJA) Deep Dive: Covered how CJA unifies data across sources in Adobe Experience Platform, removes traditional data limits, and enables advanced querying with SQL.
    • Three Core Tip Sections: Focused on breaking data limits, using derived fields, and leveraging unique CJA capabilities like smarter bot filtering and AI assistants.
    • New Visualization Tools: Introduced Journey Canvas, Guided Analysis templates, and Content Analytics (a paid add-on) for richer, more intuitive data storytelling.
    • Roadmap Highlights: Previewed upcoming features like real-time panels, B2B support, warehouse mirroring, and a next-gen data feed purpose-built for CJA

    Key Themes
    1. Enhanced Data Flexibility & Integration
      • CJA removes traditional Adobe Analytics limitations (eVars, props, low traffic).
      • Supports SQL queries and real-time data manipulation.
    2. User Empowerment Through Insights & Tools
      • Features like user state change tracking, pathing dimensions, and derived metrics empower deeper behavioral analysis.
      • Internal usage tracking (CJA on CJA) helps identify power users and adoption gaps.
    3. Innovation in Visualization & Automation
      • Journey Canvas and Guided Analysis simplify complex flows and impact analysis.
      • AI assistant and smarter bot filtering improve usability and data hygiene.
    4. Community Engagement & Forward-Looking Development
      • Gamification encouraged participation.
      • Roadmap reveals Adobe’s commitment to evolving CJA with real-time, AI, and cross-platform capabilities.
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    17 分
  • DAD008-Unlocking the Power of Data Layer Schema

    DAD008-Unlocking the Power of Data Layer Schema
    2025/05/27
    In this episode, Andrew and Matt delve into the intricacies of Data Layer Schema, emphasizing its critical role in ensuring accurate data collection and validation in digital analytics. They explore the importance of a well-defined schema, the benefits of using JSON Schema for data validation, and the integration of schema validation with tools like ObservePoint. The conversation highlights the financial implications of poor data quality and advocates for a collaborative approach to defining schemas, ultimately aiming to enhance developer efficiency and data integrity.

    Unfortunately - Andrew's track got corrupted at the end. We did the best we could with the editting, but there was not much we could do. Instead of ending the podcast suddenly, I cut Andrew's track all together. Thankfully, the new Podcasting solution we were using for this episode has been cancelled and we are moving to a another new one to test it out. Thankfully - it was only the last 6 minutes. It was funny seeing that near the end, even Andrew's camera got lost! During the recording, there was no indication that anything was wrong.

    Key Takeaways
    • Data Layer Schema is essential for accurate data collection.
    • A well-defined schema helps developers understand data expectations.
    • JSON Schema provides a code-based approach to data validation.
    • Schema validation can prevent errors before they reach production.
    • Integrating schema validation with tools like ObservePoint enhances data quality.
    • Data quality issues can lead to significant financial losses.
    • A collaborative approach to defining schemas improves outcomes.
    • Using a centralized data layer reduces technical debt.
    • Automation in data validation saves time and resources.
    • Clear delineation of responsibilities improves team efficiency.

    Chapters
    • 00:00 Introduction to Day-Layer Schema
    • 05:01 Defining Day-Layer Schema
    • 09:13 Exploring JSON Schema
    • 13:44 Benefits of Schema Validation
    • 18:08 Types of JSON Schema Validation
    • 25:46 Advanced Schema Features
    • 30:15 Collaborative Development of Data Layers
    • 34:23 Empowering Developers with Data Quality
    • 38:57 Division of Labor in Data Management
    • 44:27 Data Quality and Development Team Dynamics
    • 46:59 Benefits of Implementing a Daily Schema
    • 50:57 Cost of Bad Data and Its Implications
    • 57:35 Integrating Tools for Data Validation
    • 01:07:45 Outro
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    1 時間 8 分

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