Data quality refers to the degree of accuracy, consistency, relevance and timeliness of the data. High-quality data is not only error-free, but also complete, up-to-date and in a format that is useful for the task at hand. It enables companies to recognize patterns, make forecasts and make informed decisions.
Data quality - the gold of the 21st century
In a world where data is often referred to as the "gold of the 21st century", its quality has reached unparalleled value. The importance of data spans all industries and business areas, from financial accounting to marketing. Every process, every decision and every business success depends on data. But what does data quality actually mean?
In the digital age, in which business models are increasingly data-driven, high-quality data forms the basis for competitive advantages. It enables precise analyses, promotes innovation and improves the customer experience. Without high-quality data, companies cannot grow efficiently or adapt to changing market conditions.
However, the path to data quality is not always easy. It requires the right tools, processes and a culture that prioritizes data. But the reward for the effort? A company that is more competitive, more efficient and more future-proof. A worthwhile endeavor that every forward-thinking company should tackle.
The fundamental importance of data quality
It's now a recognized fact in the business world: data is the backbone of modern companies. Here we dive deeper into the "why" behind the importance of data quality and how it directly impacts business success.
Why data quality is more than accuracy
While accuracy is a critical factor, data quality is about much more. It's about ensuring that data is up-to-date, relevant, complete and consistent. Data that meets these criteria enables companies to respond quickly to market changes, make accurate forecasts and better serve their customers.
Data as fuel for business decisions
High-quality data provides a clear view of the market, customer behavior and internal processes. It enables decision-makers to identify opportunities and risks and act proactively. Without a solid data basis, decisions can be based on assumptions or outdated information, which can lead to suboptimal results.
Read more about this in our blog post "How data management leads to better business results".
Vision, innovation and growth with data
In a time of rapid technological developments, companies that maximize their data quality are better positioned to develop innovative solutions and tap into new business areas. They can predict market trends, anticipate customer needs and react quickly to changes.
Operational efficiency and cost savings
Incorrect or inconsistent data can lead to inefficient processes, wrong decisions and unnecessary costs. High data quality, on the other hand, optimizes business processes and reduces the risk of costly errors. For companies that want to be successful in a data-driven world, maintaining and improving data quality should be a top priority.
Read more about this in our article "Successful production planning and production control with excellent master data".
Consequences of poor data quality
While high quality data provides a solid foundation for growth and innovation, poor quality data can have a significant negative impact. Let's look at the possible consequences of poor data quality.
Business decisions at risk Imagine navigating through a dense forest with a map that is inaccurate and out of date. It is equally risky to make business decisions based on incorrect or inconsistent data. Incorrect information can lead to bad decisions that can have serious financial and operational consequences.
Biased business analysis and forecasting Data is often used as the basis for forecasting, market analysis and business models. If this data is inaccurate, the resulting forecasts and models can be misleading, leading to bad investments or missed opportunities.
Communication and team dynamics Inconsistent or contradictory data can lead to communication problems between departments and teams. If different teams cannot access the same data source or interpret the data differently, this can lead to friction and misunderstandings.
Customer satisfaction and brand image Poor data quality can have a direct impact on the customer experience. Whether it's incorrect product information, inconsistent communication or errors in order processing, poor data can annoy customers and undermine trust in the brand.
Increased operating costs Correcting errors caused by poor data quality can be time-consuming and costly. Time and manpower are spent fixing problems that could have been avoided through preventative data maintenance.
Legal and compliance risks Strict data management regulations apply in certain industries and regions. Inadequate data quality can lead to companies violating these regulations and exposing themselves to legal consequences or penalties.
Read more about this in our blog post "Supply Chain Act - Automated master data management for compliance".
Inefficiencies in the supply chain Incorrect data can lead to delays in the supply chain, inaccurate inventory records or misunderstandings with suppliers. This can lead to shortages, overstocking and other costly problems.
Read more about data quality in the manufacturing industry and the optimization of master data in SAP variant configuration.
Quantify costs: The economic consequences of poor data quality
Poor data quality not only impairs operational processes, but also has a tangible financial impact. In reality, however, hardly any company can prove the costs of poor data quality with hard facts. The costs of poor data can vary greatly and range from a few hundred to several thousand euros.
However, let's try to categorize the type of costs at this point:
- Direct costs: These are the obvious financial expenses caused by poor data quality. Examples include costs for data corrections, additional expenses due to incorrect deliveries or the need to collect missing data.
- Indirect costs: These are more difficult to quantify, but can have far-reaching consequences. These include missed business opportunities, loss of image, declining employee satisfaction or inefficient use of resources.
Examples of companies with data problems
Example 1: A retail company suffered significant sales losses due to inconsistent inventory data. Products were listed as available but were out of stock, leading to disappointed customers and missed sales opportunities.
Example 2: A pharmaceutical company had to carry out an expensive product recall because the master data was not up to date. This led to incorrect information on the packaging and thus to health risks.
Example 3: A financial services provider was fined heavily for failing to meet regulatory requirements due to data inconsistencies.
These case studies show how expensive poor data quality can be. The costs are not only measurable in euros and cents, but also in lost trust, damaged reputation and missed business opportunities.
Challenges in maintaining high master data quality
The quality of master data depends not only on technology, but also on human factors, organizational structures and corporate culture. Human error and technical issues are often the cause of poor data quality, which can be minimized through training, sound IT management and system audits. A proactive corporate culture that values data quality is just as important as overcoming cross-departmental data silos that lead to data inconsistencies.
It is not enough to be aware of the problems; a strategic initiative is needed to ensure data quality. The launch of such an initiative should be well-considered and resource-conscious, as both time and human resources are required. One effective measure is the definition of clear data ownership and data governance structures. Responsibilities for data areas must be clearly assigned to ensure accountability and effective governance. With defined areas of responsibility, data quality standards can be established and maintained.
The next logical step is to initiate a comprehensive data cleansing process. This phase is labor-intensive, but necessary to identify and correct inconsistent, outdated or redundant data. However, it is important to recognize that after such cleansing, the real work begins: the challenge is to maintain a high standard of data quality over the long term. Without continuous efforts and monitoring, data quality could fall again, leading to the so-called "sawtooth effect" - a constant up and down of data quality standards. It is also clear that improving data quality alone will not solve the problem of data silos. An efficient organization will only reach its full potential if data quality is maintained at a consistently high level.
Companies should therefore consider a comprehensive master data management initiative. The journey from data preparation to the creation of a "golden record" (the authoritative, cleansed and unified version of a data set in an organization) is challenging but invaluable. By using dedicated master data management software, organizations can break down data silos, improve data quality and ultimately become more efficient.
Read more about this in our blog post "Data preparation - the basis for meaningful analyses".
How companies benefit from professional master data management
If you want to quantify the benefits of master data management, you have to rely on empirical values. Experts in the field of master data management assume that processing times can be reduced by up to 50 percent through the use of master data management software.
When you consider that it takes a company 70 days to enter a new material into the system, you can imagine the potential of software. It is also reported that error costs can be reduced by 80 to 90 percent. This is due to the fact that master data management ensures clarity throughout the entire process chain and therefore provides an overview of the current status of the master data process at all times. An estimated 53 percent of data can be found more quickly and easily as a result.
So where is the potential for savings?
- Cost savings: Reduced duplication of work, fewer wrong decisions and optimized workflows lead to considerable cost savings.
- More efficient processes: Clearly defined and maintained master data accelerates processes from procurement to production and sales.
- Higher customer satisfaction: Accurate and reliable data enables a more targeted customer approach and better service.
Here are some practical examples:
Company A: By introducing a new master data management system, a discrete manufacturing company was able to optimize its variant configuration and thus make binding statements on prices and delivery dates.
Company B: Thanks to consistent data cleansing, Company B was able to optimize stock levels and avoid overproduction, resulting in six-figure cost savings.
Strategically thought-out master data management can make the difference between stagnation and flourishing business success.
Read more about this in our blog post "Data quality management of master data".
Core components of modern master data management
Several factors are crucial for the successful use of master data management software. These include, in particular, a clear commitment from management, well-structured data governance and continuous process optimization. It is essential to establish internal company guidelines for the careful handling of data. Data governance defines clear rules, processes and responsibilities for the collection, release and maintenance of data, including the measurement of data quality based on specific KPIs. The focus should not only be on central company processes such as purchasing, production or sales, but also on specific master data processes relating to the creation, updating or deletion of data.
The implementation of master data management software can call existing structures and responsibilities into question. Effective change management is therefore essential in order to actively involve all those involved and take them along on the journey to the new data landscape. A high-quality software solution can provide support here, but only if the processes and responsibilities relating to data maintenance and release are clearly defined.
Please also read our blog post "Data governance - processes, people and technologies".
zetVisions in the data-driven CIO ecosystem - case study
In today's data-driven business world, companies are constantly looking for solutions to utilize their data more efficiently. zetVisions is at the forefront of this movement, providing software and services that help companies realize the full value of their data. That's why our motto is:
"We breathe data to fuel your business"
zetVisions GmbH
zetVisions offers customized solutions for the specific challenges of every company in the area of data management. We ensure the seamless integration of data from a wide variety of sources, creating a consistent and uniform database. In addition to state-of-the-art tools, zetVisions also offers training and ongoing support to help companies get the most out of their data.
Success story of our customer Merck KGaA
Merck generates a lot of reference data as part of its group consolidation and reporting, especially financial master data for consolidation and reporting. zetVisions not only offers solutions for master data management, but also supports Merck in successfully handling complex data. Read the full success story of Merck KGaA here.
The next step for your company
If you really want to know what these software solutions look like in practice and what impact they can have on your company, then now is the right time.
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