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How Comcast Effectv Drives Data Observability with Databricks and Monte Carlo-02


This is Abe from the Lakehouse Department of the GLB Division. I wrote an article summarizing the contents of the session based on the report by Mr. Nagae, who is participating in Data + AI SUMMIT2023 (DAIS) on site.

This article consists of two parts, and this time we will deliver the second part.

Below is the first part of the article.


Part 1 introduced how Comcast Effectv leverages Databricks and Monte Carlo to improve data observability, as well as a data catalog feature focused on data privacy. In Part 2 of this issue, we will explain how to improve the efficiency of incident response using Microsoft Teams and how to maximize advertising effectiveness by integrating impression data and order data.

Let's get straight to the point!

Improving the efficiency of incident response using Microsoft Teams and utilizing Monte Carlo

Comcast Effectv explores ways to leverage Databricks and Monte Carlo to improve data observability. As part of this, we are discussing the efficiency of incident response using Microsoft Teams and the use of Monte Carlo.

Streamline incident response with Microsoft Teams

Microsoft Teams is widely used as a communication tool, but it is also effective for incident response. Here are three benefits of using Microsoft Teams.

  1. Better communication between teams
  2. Real-time sharing of incident progress
  3. Centralized management of related information and materials

This streamlines incident response and reduces time to problem resolution.

Improving data observability with Monte Carlo

Data observability is about monitoring and managing the quality and reliability of data so that it can be used more effectively. Monte Carlo is a data observability platform for improving data quality and reliability. It has the following functions.

  1. Automatically monitor data quality and detect problems
  2. Track data change history to facilitate root cause analysis
  3. Suggest actions to improve data reliability

This will improve the quality and reliability of your data and help you make better business decisions.

Integrate impression and order data to understand client bookings and actual deliveries

Integrating impression and order data allowed Comcast Effectv to understand client bookings and actual deliveries. As a result, the following effects were obtained.

  1. Optimal ad delivery according to client needs becomes possible
  2. Maximize advertising effectiveness
  3. Building a relationship of trust with the client

This case study also demonstrates the business value of improving data observability.

How to improve data observability with Databricks and Monte Carlo

In this talk, we discussed how Comcast Effectv uses Databricks and Monte Carlo to improve data observability.

Plans to expand and improve partnerships between Monte Carlo and users

The talk introduced plans to expand and improve Monte Carlo's partnership with its users over the next 6-12 months. Specifically, the following initiatives are planned.

  1. Improve data quality: Leverage Monte Carlo to identify data quality issues and suggest solutions to improve data quality.
  2. Strengthen data governance: Strengthen data governance by clarifying and complying with rules and policies regarding the use and management of data.
  3. Better communication with our users: We work to improve our services by communicating more closely with our users and accepting their feedback.


Leveraging Databricks and Monte Carlo to improve data observability, Comcast Effectv will be able to improve data quality, data governance, and improve the accuracy of ad measurement and attribution. I believe that we will continue to expand and improve our partnerships with users and continue our efforts to maximize the effects of data utilization.

Translated by Johann