How Data Observability Platform Can Save Over $100M Annually for Supply Chain Reconciliation

Why Data Observability is Critical in Supply Chain Management

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Organizations rely on consistent, reliable data to manage inventories, estimate demand, and assure timely delivery of goods. A slight data error can result in costly delays, compromising production schedules, customer satisfaction, and profitability. This is where data observability comes in.

Data observability platforms can help businesses prevent adverse outcomes by providing real-time insights into data quality, lineage, and system health, assuring the smooth running of their supply chains.

Unity Software's $100M Loss

According to an IBM blog, Unity Software lost $110M after discovering it had been ingesting bad data from a large customer.

This mistake led to a 30% plunge in the company’s stock. The lesson: bad data erodes the trust of your leadership, board members, shareholders, partners, and customers.

How data observability platform could have averted this disaster

A data observability platform typically involves an annual investment for midsized companies, could have detected Unity Software's data error and prevented such a catastrophic outcome. The data observability platform allows organizations to "shift left," identifying discrepancies and errors in advance, thus saving costs and minimizing the damage.

What Is a Data Observability Platform?

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Data observability platform is an end-to-end tool that tracks the integrity of your data — from source to production — in all data pipelines. It aims to create enough transparency in order to ensure accurate data-driven decision-making. The platform provides the following benefits:

1. Auto-discovery and categorization of data and data assets: The platform automatically identifies and classifies various data sources and assets within the supply chain ecosystem, ensuring comprehensive coverage based on data reconciliation meaning.
2. Real-time monitoring: It continuously monitors data pipelines and inventory levels, allowing for immediate detection of discrepancies between physical counts and recorded data.
3. Automated alerts: The platform generates alerts for expected and unexpected data events, enabling proactive identification of issues that could impact the data reconciliation framework.
4. Error detection and analysis: It automates the detection of errors and anomalies in data, facilitating quicker resolution of discrepancies during the data reconciliation testing.
5. Data tracking: The platform tracks data lineage and changes, providing insights into how data moves through the system. This plays a crucial role in understanding and reconciling inventory records.
6. Data lineage: The platform offers comprehensive data lineage tracking, allowing users to trace the origin, transformation, and movement of data throughout the supply chain. This feature enables a better understanding of data relationships, root-cause analysis of discrepancies, and data integrity in the reconciliation process.
7. Root-cause analysis: Data observability platform offers tools that analyze the root cause of discrepancies, allowing teams to address underlying issues rather than the mere symptoms.
8. Documentation and reporting: The platform records events and changes in a standardized format, aiding in the documentation of the reconciliation process for audit purposes.
9. Continuous improvement: The platform analyzes trends and patterns in data quality to help organizations refine their data reconciliation in the data warehouse and improve overall data reliability.

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Role of Data Observability in Supply Chain Reconciliation

Data observability plays a key role in optimizing supply chain reconciliation. The platform consolidates relevant data from various sources, including inventory records, sales data, and supplier information. It then compares physical inventory counts with recorded levels to identify discrepancies. The system analyzes these mismatches to uncover potential errors in data entry, theft, or miscalculations. Based on the findings, it suggests necessary adjustments to inventory records to ensure their accuracy.

The platform automatically documents all changes throughout the reconciliation process to maintain a clear audit trail. It reviews the reconciled data to validate its accuracy and completeness. Finally, it identifies opportunities for improvement to minimize future discrepancies and enhance accuracy in subsequent reconciliations. This systematic approach helps maintain the integrity of supply chain data and operations.

Optimizing overall supply chain management with a data observability platform

A data observability platform plays a vital role in optimizing supply chain management. It provides visibility across transactional systems, checks data quality, integrates data from multiple sources, monitors equipment status, and tracks cargo conditions. The platform also provides alerts for anomalies and uses predictive models to identify potential issues before they impact operations.

Such a platform also uncovers inefficiencies, thus optimizing processes and reducing costs. It identifies trends and opportunities through data-driven insights. Organizations can achieve faster deployment, gain deeper insights, and make more informed decisions by using pre-built assets and advanced analytics, including machine learning. This would help enhance the overall supply chain management process.

Real-life Supply Chain Disasters That Could Have Been Prevented with Data Observability Platform

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The Unity Software incident is not an isolated case. Here are some other examples of how a data observability platform might have helped avert a supply chain disaster:

Data quality: Nike's $100M lesson in supply chain management

Nike's supply chain disaster in 2000-2001 highlighted the importance of data quality. Its legacy software was fragmented into 27 different systems with inconsistent data formats, leading to critical errors. The result? A $100M loss in sales!

A data observability platform could have prevented this costly mistake by identifying and flagging inconsistencies across systems, helping the company remediate the errors and prevent a catastrophic revenue loss.

A simple data error sinks Spain's €1B military submarine

The Spanish Navy's ambitious submarine project encountered a major setback when the Isaac Peral sub was found to be 75-100 metric tons overweight due to an engineering miscalculation. This critical error, which compromised the submarine's ability to submerge and resurface, delayed delivery by up to two years and incurred significant additional costs. The project was a disaster: at €2.2 B for four subs, it was already one of the most expensive in the Spanish military history.

Data observability platform could have flagged data discrepancy in time and prevented the catastrophic outcome by allowing engineers to make necessary adjustments prior to the commencement of manufacturing operations.

The Lehman Brothers' 2008 financial global market collapse

The 2008 collapse of Lehman Brothers, triggered by inadequate data quality and risk assessment, led to the loss of $691B in assets. This failure to accurately value assets and liabilities contributed significantly to the global financial crisis that followed.

A data observability platform could have flagged critical data quality issues, potentially averting this economic catastrophe.

The Acceldata Advantage

Acceldata's data observability platform has been recognized as the category leader in the Everest Group Data Observability Technology Provider PEAK Matrix® Assessment 2024. The platform offers the following advantages over its competitors:

1. Comprehensive data observability: Acceldata's multidimensional data observability platform provides insights across various layers of data architecture, including infrastructure, applications, and endpoints.
2. Cloud-agnostic solutions: Acceldata's platform is designed to support enterprises regardless of their data journey stage—be it cloud-native, multi-cloud, hybrid, or on-premises.
3. AI automation: The platform leverages AI to filter out noise from vast amounts of data, providing actionable insights and helping teams focus on optimization rather than reactive problem-solving.
4. Enhanced data reliability and quality: Acceldata focuses on not just monitoring but also repairing and preventing data quality issues, which is increasingly important as organizations move to complex, distributed data architecture.
5. Scalability and performance: Acceldata is built to handle large-scale data operations, supporting enterprises with extensive data needs, such as those generating billions of daily impressions.

Power of Data Observability in Modern Supply Chains

A data observability platform offers a comprehensive solution for preventing costly supply chain disasters by providing real-time monitoring, automated alerts, and advanced analytics capabilities. It can identify critical data quality issues and inconsistencies before they escalate into major financial or operational crises, as demonstrated by numerous real-world examples across various industries.

By leveraging AI-driven automation, cloud-agnostic solutions, and scalable performance, data observability tools enhance data reliability and quality across complex, distributed data architecture, enabling organizations to optimize their supply chain management and avoid potentially catastrophic errors.

Request a demo of Acceldata and discover how an advanced data observability platform can transform your organization's supply chain management.

Summary

Data observability platform is crucial for supply chain management, as it can prevent costly errors and optimize operations. By continuously monitoring data pipelines and identifying discrepancies, the platform helps maintain data accuracy, improve decision-making, and avoid catastrophic failures. Acceldata's data observability platform offers comprehensive solutions for various industries to ensure data reliability and drive business success.