Why Detail Matters for Causal Analytics? Prepping Your Data to Drive Impactful Decisions

Key Takeaways

• Data Detail is Crucial: To perform causal analytics like Marketing Mix Modeling, you must preserve raw, event-level data (joint distributions). Summary reports (marginals) discard the necessary context to attribute success to specific combinations of customer traits.

• Avoid the "Average" Trap: Aggregated data can hide the true story. Two completely different customer behaviors can look identical in summary reports, making accurate attribution impossible without the raw source table.

• Speed through Structure: Modern columnar databases allow analysts to scan millions of rows instantly by reading only the specific columns needed. This "wide table, narrow query" approach perfectly matches how humans ask questions, enabling complex analysis at hardware-limit speeds.

Granularity Drives Causal Insights

For business executives, the goal is clear: understand which actions are truly driving outcomes. Whether you are running a Marketing Mix Model (MMM) or calculating promotional lift attribution, you are engaging in causal analytics—the effort to prove that X caused Y. This level of insight is critical for optimizing budgets, but it requires data that is prepared and structured in a specific way.

It is a common sight in modern businesses: massive data warehouses designed to handle billions of orders or events. A typical fact table might contain tens of millions of rows and thousands of columns (or features) per order. The data volume is immense, but the challenge for causal analysis is not size; it is detail preservation.

The Core Challenge: Distinguishing Detail from Summary

Imagine you want to know the return on investment (ROI) for a specific promotion targeted at a demographic. To answer this, you need to link the action (promotion/ad exposure) to the outcome (purchase/conversion) with all the relevant context (e.g., location, device, time).

In the world of data, this detail is known as joint information.

• Joint Information is the raw, event-level data, where every single row has the full context. This is what allows you to ask, "How many users were Male and in Boston and used an Android device?".

• Marginal Information is aggregated summary data. For instance, a report that says, "10,000 users were Male" and a separate report that says, "9,000 users were in Boston".

  
  

This distinction is crucial for causal analytics because marginal distributions do not determine the joint distribution. If your automated data pipeline discards the raw, row-level data and only keeps "by Gender" and "by City" summaries, then the exact count for any specific combination—say, Male users in Boston—is mathematically underdetermined and impossible to reconstruct. You cannot perform precise attribution without the original, detailed joint data. Refer to Appendix for an illustrating example.

The key takeaway for data preparation is simple: For causal models like MMM, you must ensure your pipeline preserves the raw, joint-level fact table data. Losing this detail is a fundamental loss of information.

The Role of Columnar Storage in High-Speed Analysis

Once the detailed joint data is preserved, the next step is making it accessible for analysis. This is where modern database technology, specifically columnar storage, provides a major competitive edge, perfectly aligning with how marketers analyze data.

The Analytical Query Pattern

Marketers rarely use all 2,000 available data features at once. A typical Online Analytical Processing (OLAP) query involves scanning many rows but only a small subset of columns—perhaps 3 to 10 dimensions—to answer a business question like, “What is last year's average spending for (Male, Boston, Android)?”. This is known as the "wide table, few columns per query" pattern.

Why Columnar Storage Works

Traditional row-oriented databases store an entire row (all 2,000 columns) together on disk. To answer an OLAP query, the system often has to read most of the entire 80 GB table just to access the few columns needed.

A column-oriented store, however, stores each column contiguously as a separate file on disk. This layout is perfectly designed to exploit the analytical query pattern:

1. Minimizing I/O: The system only reads the few columns it needs (e.g., Gender, City, Device, Cost). In a large table, this can reduce the I/O volume from 80 GB to just 160 MB, accelerating query speed dramatically.

   
   

2. Hardware Efficiency: Storing data in long, continuous column arrays allows the database to move data into the CPU's memory caches in a more sequential and efficient manner. This aligns perfectly with modern CPU architecture, specifically Single Instruction, Multiple Data (SIMD), which allows the CPU to operate on multiple data elements simultaneously for fast vectorized scans.

   
   

While simple single-column queries (like SUM(Cost)) are extremely fast, multi-dimensional queries (like the full Male/Boston/Android filter) naturally take longer because they require reading more columns and applying complex filtering masks. Yet, even these complex queries are significantly faster and more scalable in a columnar environment.

Conclusion: Preparation Enables Performance

Causal analytics is an essential driver of marketing effectiveness. The path to precise promotional lift and accurate MMM results is paved by two essential data principles:

1. Preservation of Detail: Do not allow pipelines to discard raw, joint-level data for mere marginal summaries. You cannot compute joint combinations that were discarded.

   
   

2. Optimized Structure: Ensure your analytical database uses columnar storage. This structure is purpose-built to execute the "wide table, narrow query" pattern used by analysts at maximum speed, allowing you to ask complex causal questions and get timely answers.

   
   

By ensuring your data is both detailed and columnar, you empower your analysts to move beyond simple reporting and conduct the sophisticated causal analysis necessary for optimal budget allocation.

Appendix: What are Marginal and Joint Distributions?

In marketing analytics, a joint distribution is what you need for attribution, as it shows the counts for every combination of attributes.

Consider two attributes (Dimensions): Gender and City, and the total number of users is 18,000.

• Gender: Male ($a_1$) = 10,000; Female ($a_2$) = 8,000.

• City: Boston ($b_1$) = 9,000; Seattle ($b_2$) = 9,000.

  
  

These row and column totals are the marginal distributions.

	Boston (b1​)	Seattle (b2​)	Row Sum (Marginal) in 1,000
Male ($a_1$)	?	?	10
Female ($a_2$)	?	?	8
Column Sum (Marginal)	9	9	18

The question is what the counts are for the four cells marked with '?'.

The problem with only having the marginals is that multiple joint distributions can satisfy the same marginals.

Version 1: Concentrated Joint	Boston (b1​)	Seattle (b2​)	Row Sum in 1,000
Male ($a_1$)	9	1	10
Female ($a_2$)	0	8	8
Column Sum	9	9	18

Here, the count for Male and in Boston is 9,000.

Version 2: Balanced Joint	Boston (b1​)	Seattle (b2​)	Row Sum in 1,000
Male ($a_1$)	5	5	10
Female ($a_2$)	4	4	8
Column Sum	9	9	18

Here, the count for Male and in Boston is 5,000.

Both scenarios are consistent with the summary reports. If your system only stored the two summary reports (the marginals) and discarded the raw data, you cannot definitively say whether the number of Male users in Boston is 9,000 or 5,000.

Preserving the raw, joint-level table is the only way to accurately compute the causal attribution and avoid this ambiguity.