Our main use case for Monte Carlo is in the energy sector where it has been central to helping us ensure we have trusted and reliable data across our critical operational and business data pipelines. We work in an environment where data drives everything: our network performance reporting, our outage response, regulatory compliance, and data asset management forecasting. For us, data quality is not an option; it is not nice to have, it is a must-have. We have deployed Monte Carlo because we needed to automate our data quality monitoring across our systems such as our data warehouse, our data lake, and our ETL processes. We needed good data quality, even on our demand forecasting models and our asset inspection data. We have set up some automated data quality checks on our critical tables. For example, I want to consider the load volumes from IoT sensors on our poles and our transformers. Anomalies such as missing records, any freshness failures, or some unexpected schema changes—Monte Carlo helps us detect those even before they reach the dashboards or the models. Ultimately, our dashboards and models are used by on-the-ground maintenance crews and planners, so we want any such changes to be detected before they impact the dashboard. Monte Carlo has that capability. It has drastically reduced silent data failures that used to surface only when the stakeholders raised concerns. Monte Carlo automates those data quality checks with capabilities such as machine learning-based anomaly detection, metadata analysis, and end-to-end lineage instead of relying on just manual rules. Earlier, engineers would have to manually write hundreds of rules. Monte Carlo profiles the historical data patterns and applies the ML-based anomaly detection across our entire data pipeline. There are different kinds of categories which can be monitored in Monte Carlo. We can do freshness checks which will tell us when the data has arrived and alert us if any data is late or missing. The second kind of category of check is volume checks. Monte Carlo can learn what the normal row counts or event volumes are, and gives us a flag in case of any unexpected drops or spikes. The third is the distribution checks which detect any changes in the value distributions. The fourth check is the schema changes that help us understand if there are any column level additions or deletions, or changes in the data type. The last check is for field-level anomalies which helps monitor null rates, any zero values, duplicates, or unexpected patterns at the column level. The best part is we can do these checks without having to write any SQL tests. A recent example is when we had smart meter consumption data coming into our data warehouse daily. It feeds our downstream dashboards, our billing validation, and our demand forecasting models. Before our organization got the license for Monte Carlo, our teams would manually do checks; they would do DBT tests, and issues would only be found later when analysts would notice odd trends. When we onboarded Monte Carlo, the tool helped us observe historical patterns, quantifying that there are 200 million meter readings every day. It also observed when the data arrives daily, at 6 AM, taking this baseline learning and observing the average KWH values within a stable range, and noting low null rates for the meter ID and the timestamp. One morning, the data arrived on time, but the total row count dropped by 35%, and the null values in the meter_reading_KWH column increased unexpectedly. In such a scenario, Monte Carlo automatically flags the volume anomaly and the field-level null anomaly, grouping them into a single data incident with no manual rule written for that. Data engineers were not required to do any coding. Using the automated lineage, Monte Carlo helps us go to the root cause, showing us which upstream table had changed and which downstream dashboards and forecasts were impacted. Since the alert fired early, before our business users could see that impact, the forecasting models were paused, operations teams were notified, and the ETL logic was fixed even before the reports were published. That prevented any incorrect load forecasts that could have influenced network planning decisions.
Monte Carlo works as a centralized data tool. We use it for data observability and anomaly detection, which helps identify issues and changes in data flows.
Data Quality solutions ensure accurate, consistent, and reliable data. They monitor, cleanse, and enhance information, allowing organizations to make informed decisions, improve business processes, and increase operational efficiency.Data Quality solutions provide tools to identify discrepancies in datasets, facilitate their correction, and enhance trust in data-driven decisions. They employ advanced algorithms and machine learning models to profile and cleanse data, providing a detailed...
Our main use case for Monte Carlo is in the energy sector where it has been central to helping us ensure we have trusted and reliable data across our critical operational and business data pipelines. We work in an environment where data drives everything: our network performance reporting, our outage response, regulatory compliance, and data asset management forecasting. For us, data quality is not an option; it is not nice to have, it is a must-have. We have deployed Monte Carlo because we needed to automate our data quality monitoring across our systems such as our data warehouse, our data lake, and our ETL processes. We needed good data quality, even on our demand forecasting models and our asset inspection data. We have set up some automated data quality checks on our critical tables. For example, I want to consider the load volumes from IoT sensors on our poles and our transformers. Anomalies such as missing records, any freshness failures, or some unexpected schema changes—Monte Carlo helps us detect those even before they reach the dashboards or the models. Ultimately, our dashboards and models are used by on-the-ground maintenance crews and planners, so we want any such changes to be detected before they impact the dashboard. Monte Carlo has that capability. It has drastically reduced silent data failures that used to surface only when the stakeholders raised concerns. Monte Carlo automates those data quality checks with capabilities such as machine learning-based anomaly detection, metadata analysis, and end-to-end lineage instead of relying on just manual rules. Earlier, engineers would have to manually write hundreds of rules. Monte Carlo profiles the historical data patterns and applies the ML-based anomaly detection across our entire data pipeline. There are different kinds of categories which can be monitored in Monte Carlo. We can do freshness checks which will tell us when the data has arrived and alert us if any data is late or missing. The second kind of category of check is volume checks. Monte Carlo can learn what the normal row counts or event volumes are, and gives us a flag in case of any unexpected drops or spikes. The third is the distribution checks which detect any changes in the value distributions. The fourth check is the schema changes that help us understand if there are any column level additions or deletions, or changes in the data type. The last check is for field-level anomalies which helps monitor null rates, any zero values, duplicates, or unexpected patterns at the column level. The best part is we can do these checks without having to write any SQL tests. A recent example is when we had smart meter consumption data coming into our data warehouse daily. It feeds our downstream dashboards, our billing validation, and our demand forecasting models. Before our organization got the license for Monte Carlo, our teams would manually do checks; they would do DBT tests, and issues would only be found later when analysts would notice odd trends. When we onboarded Monte Carlo, the tool helped us observe historical patterns, quantifying that there are 200 million meter readings every day. It also observed when the data arrives daily, at 6 AM, taking this baseline learning and observing the average KWH values within a stable range, and noting low null rates for the meter ID and the timestamp. One morning, the data arrived on time, but the total row count dropped by 35%, and the null values in the meter_reading_KWH column increased unexpectedly. In such a scenario, Monte Carlo automatically flags the volume anomaly and the field-level null anomaly, grouping them into a single data incident with no manual rule written for that. Data engineers were not required to do any coding. Using the automated lineage, Monte Carlo helps us go to the root cause, showing us which upstream table had changed and which downstream dashboards and forecasts were impacted. Since the alert fired early, before our business users could see that impact, the forecasting models were paused, operations teams were notified, and the ETL logic was fixed even before the reports were published. That prevented any incorrect load forecasts that could have influenced network planning decisions.
Monte Carlo works as a centralized data tool. We use it for data observability and anomaly detection, which helps identify issues and changes in data flows.