Poor data quality costs companies an average of USD 12.9 million per year. This alarming figure, which comes from a Study by Gartner shows the enormous costs that incorrect data can cause. IBM even estimates that in the USA alone, USD 3.1 trillion is lost every year due to bad data. Data expert Thomas Redman adds that companies have to spend up to 25 % of their annual turnover on fixing data-related problems. But the consequences of poor data quality go far beyond costs.
This article highlights all aspects and measures relating to data quality that are relevant to your business.
Definition - What is data quality?
Data quality describes how well information is suitable for the intended purpose. It is the basis for decisions and resource-saving work. Your data must meet certain criteria: It should be up-to-date, complete, valid and consistent.
Incorrect data, on the other hand, impairs efficiency and therefore leads to financial burdens. For example, incorrect information in customer data records such as incorrect addresses or telephone numbers can render a marketing campaign worthless - your target group is not reached and human resources are not used wisely. This shows how essential the Data integrity is the highest quality criterion for data.
Good data quality management aims to ensure that your data is not only correct, but also fit for purpose. Companies can only guarantee accuracy and consistency if they systematically focus on high-quality information.
Positive effects of high-quality data
Information quality has a direct impact on governance, efficiency and accuracy within your organization. It's not just about avoiding incorrect information, but also about providing a sound basis for data-driven business decisions, smooth processes and stronger customer loyalty. High-quality data is a strategic advantage for your company:
- Better decisions - Reliable data increases the accuracy and security of your strategic and operational decisions.
- Released resources - Precise and consistent data enables smooth automation and relieves your employees of routine tasks.
- Higher customer satisfaction - With complete and valid data, you can reliably provide personalized offers and services.
In a digital environment in which data forms the backbone of every decision-making process, the importance of data quality management is becoming increasingly clear. Find out below how you can use a strategic approach and technology to reduce the potential for errors, ensure that your data records are up to date and benefit from sustainable data quality in the long term.
9 Principles and criteria for measuring data quality
The quality of your data can be assessed using nine dimensions. Many companies use tried-and-tested frameworks for this, such as those developed by the BARC proposed approaches.
- Completeness - Is all the necessary information available?
- Uniqueness - Are there redundancies or duplicates in the data?
- Validity - Are standards such as formats, value ranges or business rules adhered to?
- Actuality - How up-to-date is the stored information?
- Accuracy - Does the data correspond to reality?
- Consistency - Is the data standardized across all systems?
- Practicality - Is the data relevant and useful for the respective use case?
- Transparency - Are errors in the data visible and traceable?
- Sustainability - Is the data quality maintained over time?
Solutions such as Konfuzio ensure completeness in data collection and support your data quality and governance. In this context, the latter defines the systematic management, monitoring and control of data and its quality within a company.
By using the Document AI automatically extracts, categorizes and structures the required information from documents, images and scans, it reduces potential sources of manual error and at the same time frees up human resources for strategic tasks.
Determine and improve data quality in 5 steps
A systematic approach is the basic prerequisite for achieving high data quality. These five steps will help you lay the foundation:
1. recognize common problems and errors
Start by identifying typical weak points - for example, inaccurate values or data silos that generate inconsistent information.
2. develop a strategy for data quality
Create a clear procedure. Use a Checklistas provided for download by the Federal Office for Digital and Transport, for example, to gain an overview of the most important points. The future-proof establishment of a solid data quality management system should always be the focus.
3. carry out regular measurements
Use a constant method of measurement to gain control over data quality management. This allows you to constantly monitor the quality of your data sets and obtain realistic empirical values and data KPIs for continuous improvement. Regular measurement and feedback on the status quo also enables early detection of inconsistencies in almost all work steps.
4. clarify responsibilities
Establish clear roles within governance and management so that all departments are actively involved in optimizing data quality. This is also an important component of successful data quality management and should not be neglected under any circumstances.
5. use technologies and positively influence progress
Modern solutions such as Konfuzio automate and simplify time-consuming data collection and provision processes. You define individual criteria and the information to be read out in advance together with the Konfuzio experts and train the artificial intelligence (AI) with just a handful of training documents - supported by our support team at any time if required. With ongoing AI training, you can achieve up to 99% accuracy in your data sets.
With Konfuzio Chat, you can also bundle information from internal primary sources and overcome data silos. This allows your teams to work across departments and locations in a data-driven way and access the same consistent and centralized quality of information.
How Konfuzio helps to achieve data quality
Thanks to a web-based interface (REST API) and numerous integrations, Konfuzio is a solution that you can use both in the cloud and on your own servers (on-premises) to obtain high-quality data.
Automation of routines
Means OCR Technology and AI for Data extraction as well as Document splitting repetitive tasks such as processing invoices, contracts or multi-page documents can be fully automated. This reduces sources of error, ensures data completeness and frees up resources within the company.
Synchronization of information
Konfuzio Chat enables the collection and synchronization of information within cross-functional teams. Employees receive information through a AI chatbot Access to all data that you have previously released by feeding the knowledge base - for maximum data security and protection against unauthorized access. Chatting with "documents" is a milestone on the way to the paperless office.
Extension of existing data
With chatbots from Konfuzio, you can either selectively expand an existing database, which enables dynamic data governance, or generate completely new data. The controlled connection of selected internal sources gives you customized usability and scalability of your data management.
Conclusion - data quality is your competitive advantage
The financial impact of poor data quality is clearly demonstrated by numerous figures and analyses - reliable data, on the other hand, minimizes these costs, ensures more efficient processes and creates free capacity. Highest precision in data collection, relevance and timeliness as well as continuous monitoring - always taking into account BARC's proven principles - help you to make well-founded and data-driven decisions for your business.
With modern tools and solutions such as Konfuzio, you can rely on automation, data security and collaboration with experts to get the best out of your data records and reduce potential sources of error - for future-proof processes.
"There is a wide range of deficiencies in data quality, from formal errors to inadequate accessibility and incorrect values."
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