Data Quality Management: how flawed data undermines corporate strategies and how to ensure data quality

In today's world of digitization and automation, businesses rely on data more than ever before. Manufacturing, sales and distribution companies have access to vast amounts of data that allow them to optimize production processes, improve logistics, predict demand or better target marketing campaigns. Data is the basis for decision-making at all levels of the organization, from operational to strategic actions. However, the quality of this data is crucial to the success of these strategies. When data is inaccurate, incomplete or out of date, it can lead to poor decisions that cost a company time, money and sometimes customers.

This article will look at the key aspects of Data Quality Management (DQM), its importance, and the steps manufacturing and commercial companies can take to ensure that the data they work with is reliable and supports their strategic goals.

What does data quality mean?

Data quality is measured by several criteria:

• Accuracy: Data must accurately reflect reality. For example, in a manufacturing company, if systems have incorrect inventory data, this can lead to insufficient or excess inventory.

• Completeness: Missing data can lead to an incomplete view of the situation. For commercial companies, missing data on customer preferences can lead to incorrect marketing targeting.

• Consistency: Data should be consistent across all systems. If a company has multiple systems with different data on the same customer or product, it leads to inconsistencies and decision-making problems.

• Timeliness: outdated data can be just as damaging as erroneous data. For example, data on suppliers or logistics partners that is not updated can cause delays or interruptions in deliveries.

• Understandability: Data must be clear and easy to interpret. If data is too complex or incomprehensible, its value for decision-making is reduced.

How does flawed data undermine corporate strategies?

Companies that ignore data quality can face a number of problems that directly impact the business.

1. Wrong decisions: bad data leads to wrong decisions. For example, in the manufacturing sector, where accurate inventory management and production planning is critical, a poor demand forecast or lack of accurate data on raw material availability can lead to production shortfalls.

2. Cost increases: data errors can increase operating costs. Businesses may buy inventory they don't need or lose customers due to a lack of personalisation. In the manufacturing sector, inaccurate machine and maintenance data can lead to costly breakdowns and unplanned downtime.

3. Deteriorated customer experience: for companies that deal directly with customers, good data is key to delivering a better customer experience. For example, if a business processes an order incorrectly or provides the wrong information to a customer, it can significantly damage its reputation.

4. Inability to leverage advanced technology: Poor data reduces the effectiveness of advanced technologies such as Big Data analytics, predictive models or artificial intelligence. These technologies depend on good data to deliver accurate predictions and enable companies to make informed decisions.

5. Compliance and security disruption: some industries, especially manufacturing, are subject to strict regulations. Inaccurate data can lead to regulatory violations and cause serious financial and legal consequences. Erroneous product traceability data can also risk recalls of defective goods or endanger customers.

How to ensure data quality?

To avoid these negative impacts, companies need to implement a Data Quality Management (DQM) strategy. The following steps will help ensure that the data you work with is reliable and valuable to the business.

1. Define data quality standards

Each business should define its own data quality standards that meet its specific needs. These standards should include metrics to measure accuracy, completeness, consistency, timeliness, and other aspects of data quality. Standardization helps ensure that all systems and processes are working with the same data.

How to do it? Identify the most important areas of data where quality is critical and determine your metrics. Establish a process for regular monitoring and auditing of data quality to identify deficiencies and ensure remediation. Establish responsibilities for teams or staff and train them.

2. Establish processes for data management and cleansing

Regular data cleansing is an indispensable element of Data Quality Management. This process includes removing duplicate, outdated, or incorrect data and ensuring that data is consistent across systems. Automated data management tools can streamline this process.

How to do it? Define goals and expectations. Identify the most common errors and observe trends in the data. Standardize the structure of the process. Eliminate duplicates and unnecessary data. Review data quality metrics. Maintain the process.

3. Implement data governance

Data governance is a framework for managing and controlling data. Companies should clearly define who is responsible for each type of data, how data is collected, stored and used, and how it is ensured that data meets quality standards. This includes data security and the protection of sensitive information.

How to do it? If you don't have sufficient in-house know-how, turn to consultants. Implementing Data governance is a complex and challenging process that interferes with the day-to-day operations of a company.

4. Employee education and engagement

Data quality is not just a technical issue, but also depends on how employees work with data. Companies should train their employees so that they understand the importance of good data quality and can identify potential errors. Engaging people across the company helps create a culture that values accurate and up-to-date data.

How to do it? Educate. Provide employees with internal or external training, and set aside time for self-study. Give them the resources to grow.

5. Automate and leverage advanced tools

Modern data management tools and advanced analytics can help companies maintain high data quality. Data Quality Management software allows you to monitor data quality in real time, identify problems, and provide recommendations for correcting them. In addition, artificial intelligence-based systems can analyze large volumes of data and help prevent errors.

How to do it? Take advantage of the capabilities of modern data tools, especially to support Data Quality Management. Ensure careful implementation hand-in-hand with your internal process.

6. Regular data quality audits

Companies should regularly audit their data quality to assess the effectiveness of their Data Quality Management processes and identify any weaknesses. These audits should include not only a technical review of the data, but also a review of internal processes and employee training.

How to do it? Using the previous points, you should have a means of checking data quality. Alternatively, use an external supplier.

Poor data quality can have serious consequences for a company. Here are specific examples from practice:

1. Incorrect production decisions: The case of Toyota

In 2010, the Toyota car company got into trouble because of poor quality data on some components. Faulty information on the quality of braking systems led to incomplete analyses. As a result, hundreds of thousands of cars were recalled, causing huge financial losses and damage to the company's reputation. This example shows how critical it is to have accurate data when making decisions about product quality and safety.

2. Inefficient inventory management: the case of the Tesco retail chain

Retail giant Tesco had problems with faulty inventory data that led to a large amount of losses due to food spoilage. The systems incorrectly recorded expiry data, resulting in incorrect timing of discounts and product disposal. The company lost millions of dollars unnecessarily. This example illustrates how erroneous data can directly affect operating costs.

3. The case of e-commerce company Amazon

Amazon is known for its advanced logistics and efficient customer service. However, even here there were problems due to data errors. In 2013, several customers received incorrect orders because Amazon's logistics system was working with incorrect inventory data. This led to late deliveries and customer dissatisfaction. Amazon quickly implemented new measures to improve data accuracy and automate logistics to prevent a repeat of the situation.

4. Regulatory non-compliance: the case of a pharmaceutical company

Pharmaceutical companies are under strict regulatory rules and product data must be absolutely accurate. One global pharmaceutical company was having problems with the quality of its manufacturing and traceability data. They were missing important information about the raw materials that went into the manufacturing process. This led to the scrapping of an entire production batch and the subsequent withdrawal of products from the market. The financial losses and damaged reputation are just the tip of the iceberg, with the other risks being primarily to the health of customers.

Conclusion

Data quality is absolutely critical for modern manufacturing and trading companies. Flawed data can undermine even the best strategies, lead to poor decisions, increase costs and reduce efficiency. Companies that invest in robust Data Quality Management have a better chance of succeeding in a competitive environment, making better use of advanced technology and delivering superior service to their customers. Data governance should therefore be an integral part of any business strategy that builds on informed decision making.

If a manufacturing or commercial company focuses on ensuring high data quality, it can optimize its processes more effectively, respond better to market needs and maintain a competitive advantage. In terms of the future, this is not just a question of efficiency, but also a necessity for survival in a digitalized world.