Blog: The Critical Role of Data Quality in Securing Trust in Your Supply Chain Tools

By Steve Arvis

In the digital age, data has become the backbone of decision-making; understanding the interplay between clean data and the principle of “Garbage In, Garbage Out” (GIGO), a term coined by George Fuechsel in the early 1960s, is crucial for any business. For example, if a mathematical equation is improperly stated, the answer is unlikely to be correct. Similarly, if incorrect data is input into a supply chain planning solution, the output is unlikely to be as accurate or informative as it should be. The concept of dirty data and the GIGO principle both highlight the same fundamental truth: the quality of output is inextricably linked to the quality of input.

Dirty Data and its Effect on SC Operations

Erroneous or dated data in a supply chain platform can erode the trust supply chain planners have in the reliability and effectiveness of their tools. When planners encounter errors or inconsistencies in the outcomes generated by their platforms, it can lead to skepticism about the tool’s efficacy. However, what’s frequently overlooked is that these errors do not reflect the platform’s computational logic or algorithmic precision but rather a consequence of importing dirty data.

The misalignment between a platform’s expected and actual performance can create a sense of distrust as planners question the reliability of its insights and recommendations. This skepticism can lead to underutilization of the platform’s advanced features, reluctance to integrate the platform fully into strategic decision-making, or even abandonment of the tool altogether.

Ensuring data cleanliness is not just a technical necessity but a critical factor in maintaining the trust and confidence of supply chain planners in their optimization platform, but it can also save you money.  Corporate data grows at a yearly rate of 40 percent. Twenty percent of that data is outright dirty. Data scientists say Supply chain success is highly dependent on accurate and timely data for efficient operation. The impact of GIGO and dirty data on your supply chain operation is significant and multifaceted.

Here are some key areas where GIGO and dirty data can have a substantial impact:

  1. Forecasting and Demand Planning: Dirty data can lead to incorrect forecasts, resulting in overstocking or stockouts. This affects the company’s ability to meet customer demand and increases holding costs or lost sales.
  2. Inventory Management: Dirty data can lead to incorrect inventory records, making tracking and managing inventory levels challenging. This can result in excess or insufficient inventory, which is costly for the business.
  3. Risk Management: Risk management decisions are often based on historical and real-time data. Inaccurate data can fail to identify potential risks, thereby increasing the supply chain’s vulnerability.

Supply chain leaders must prioritize data quality, ensuring accurate, timely, and relevant data to maintain supply chain processes that deliver the required outcomes.

The Causes of Dirty Data

Many factors contribute to the creation of dirty data; below are some of the most common.

  1. Failure to Maintain Data: Often, data entered correctly at one point becomes outdated due to changes over time. This is particularly common with dynamic variables like lead times, costs, vendor constraints, and market conditions. Regular updates are crucial to keep this data relevant and accurate.
  2. Human Error: Perhaps the most common cause is when individuals enter data incorrectly. This could be due to typos, misinterpretation, or simply entering data in the wrong fields.
  3. Manual Data Entry and Transcription: Manual processes are inherently error-prone, especially in repetitive tasks.
  4. Lack of Standardization: Without standardized formats for data entry, variations can lead to inconsistencies. For example, different formats for dates (DD/MM/YYYY vs. MM/DD/YYYY).
  5. Incomplete Data Entry: Sometimes, data entry processes are interrupted, or certain fields are overlooked, leading to incomplete data sets.
  6. Duplicate Data: This occurs when the same data is entered into the system multiple times.

How to Combat Dirty Data

Reports reveal that companies believe at least 26% of their data is dirty and incur enormous losses. Defining what constitutes clean, relevant data is the first step in preventing GIGO. This involves setting benchmarks for data accuracy, completeness, and timeliness, ensuring that the data entering the system is high quality.

  • Standardizing Data Entry and Validation Standardized data entry protocols are essential. This strategy aligns with the GIGO principle by emphasizing the importance of accurate and relevant data input. Regular validation processes further ensure that incoming data adheres to quality standards.
  • Employing Efficient Data Cleaning Methods Cleaning dirty data can be approached in various ways, each with its strengths and limitations. From manual cleaning for immediate, small-scale issues to employing sophisticated software for large datasets, the choice of method depends on the volume and complexity of the data.
  • Outlier Detection: Incorporating outlier detection algorithms is essential in identifying data anomalies that may indicate errors or unusual changes in the supply chain. This technique helps pinpoint inaccuracies that might otherwise skew analysis and decision-making processes.
  • Ongoing Database Management Continual database management is vital to maintain data integrity over time. This includes updating outdated information, rectifying inconsistencies, and regularly reviewing security measures.
  • Automated Data Correction: Investing in automated data correction tools can significantly enhance data quality alongside manual and semi-automated data cleaning methods. These tools can rectify common data entry errors, align disparate data formats, and update outdated information.

Integrating Data Cleansing with GAINS Implementation

While it’s true that data quality critically influences the effectiveness of advanced supply chain platforms like GAINS, it’s essential to recognize that data cleansing and the implementation of such a system can, and often should, occur concurrently. Many of our customers have successfully used GAINS as an end solution and as a facilitator in their data-cleansing journey. The GAINS platform offers features like outlier detection and automated rectification, which are instrumental in identifying and correcting data inconsistencies. Also, GAINS is adept at calculating and filling in missing or incomplete parameters, making it a valuable asset in data quality enhancement. This parallel approach allows businesses to move faster and reap the benefits of implementing GAINS sooner while simultaneously improving their data quality, ensuring that they can leverage the full power of the platform more quickly. This method underscores GAINS’ role as a transformative solution and a trusted partner in transforming supply chain operations through improved data management and optimization.

Reclaiming Confidence in Supply Chain Decisions

As supply chain planners navigate the complex landscape of supply chain management, their trust in the reliability of their supply chain optimization platform is paramount. The insights drawn from these advanced tools are critical for making informed decisions that directly impact the company’s financials, service, sustainability commitments, and resilience.

When planners recognize that the discrepancies and errors they encounter in the outputs of their optimization platforms stem from the quality of the data inputted, not from the platforms themselves, corrective actions can be taken.

Before losing faith in these powerful solutions, supply chain planners should turn a critical eye toward the cleanliness of their data. In doing so, they will restore and reinforce their trust in the optimization platforms and unlock even better decision-making across their business. Clean, accurate, and timely data will enable supply chain optimization platforms to generate more reliable forecasts, manage inventory more effectively, and identify risks more accurately. This proactive approach to data management will transform supply chain operations, leading to increased efficiency, reduced costs, and enhanced decision-making capabilities.

By ensuring the quality of their data, they can fully leverage the power of their supply chain optimization platforms, driving their businesses towards greater outcomes and rapid results.

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