Organizations striving to utilize data in their operations increasingly rely on modern technologies and artificial intelligence to optimally leverage their informational assets. However, they consistently encounter data quality issues. Incomplete or inaccurate data, security problems, and hidden data are just some of the challenges they face. Several studies have confirmed that data quality issues cause significant damage across various industries.
Here are some examples of low data quality in business:
Incorrectly recorded customer names leading to missed interaction opportunities and potential revenue loss.
Incomplete or outdated information on regional preferences hindering business expansion into new markets.
Absence or outdated emergency contact information for patients making it impossible to obtain consent for urgent medical care.
Data quality directly impacts an organization’s revenues and operational expenses, resulting in financial losses. It also significantly affects compliance and management efforts, leading to additional expenses and delays. A data quality market study showed that annual financial losses due to poor data quality amount to around $15 million as of 2022. The survey also revealed that the practice of poor data quality undermines digital initiatives, reduces competitiveness, and affects customer trust.
The most common data quality issues serve as a major obstacle to the successful and profitable use of technologies like machine learning. Therefore, it’s crucial to pay attention to data quality and develop effective strategies for improvement.
Here are some of the most common data quality problems and ways to address them.
Duplicated data can have a serious impact on the operation of a system or database. In my practice, I have encountered situations where the same data records were saved in the database multiple times due to various reasons, such as incorrect data merging during integration or re-importation. This can lead to inefficient use of storage space, distortion of analysis results, and worse, loss of the original correct records. To combat this issue, a data management structure is implemented, which includes recommendations for data input and storage, model forms. It is also important to use data validity checks before inputting them into the system and apply unique identifiers to differentiate data elements. Software for detecting and eliminating duplicates can also be helpful.
Irrelevant data. It is important to understand that collecting and storing excessive information can create problems with data quality, as they quickly become outdated. Therefore, it is necessary to determine the data requirements for the project, use filters to remove unnecessary data, and select the right data resources related to the specific project. It is also useful to apply data visualization to identify significant patterns.
Unstructured data, their analysis can present certain difficulties. However, the application of automation and technologies such as artificial intelligence and machine learning can significantly facilitate this process. It is also important to hire and train personnel with the appropriate skills, as well as to establish a data management policy that will determine data management practices within the organization. Otherwise, over time, the human factor will simply destroy any structure, and these errors will backfire.
Data downtime is a serious problem that can negatively affect business and customer experience. To address this issue, it is important to implement backup and failover mechanisms, as well as to conduct regular maintenance and performance monitoring of the data pipeline. Automation of data management processes can also help prevent downtime.
Inconsistent data. Imagine a “mess” of data where each source speaks its own language. One piece of information here, another completely different one there. And when everything is on its own wave, it’s called “inconsistent data”. They arise due to various flaws — from manual typos to ineffective data management. Sometimes, the reasons can be as subtle as units of measurement or language. A simple example. In different places, the date may look different — there it’s April 14, 2023, and here it’s 14.04.2023. And it seems that both are correct, but how do we deal with it later? And different formats make their edits to our database. Of course, one can use technologies like artificial intelligence to automate this process. And, of course, regular data cleaning is necessary — just like in a flat!
Inaccurate data. Sometimes, our information is just made up. This, my friends, is a straight path to wrong decisions and disappointed customers! So, we need to establish some quality standards, apply data cleansing methods, and, once again, regularly check and clean everything.
Hidden data. We may have collected them, but we don’t use them. After all, we can see that they are lurking somewhere! Either use them or get rid of them — the main thing is that they don’t create confidentiality issues.
Outdated data. This is a category of data that no longer reflects reality. It is necessary to update the data regularly, create a management strategy, and possibly use external resources.
The Masa Network project aims to create a decentralized network for managing and exchanging personal data. It allows users to control their data and grant access to it in a secure and transparent environment. Furthermore, it operates on a pay-per-use model, where higher quality data comes at a higher price.
The data web scraper tool in Masa Network enables the collection of data from websites and other sources in a decentralized environment, ensuring accuracy and reliability of the acquired information.
Decentralized delivery (LLM): Masa Network is developing decentralized logistics, enabling more efficient and secure delivery of goods and services within the network.
In reality, this industry is just beginning to take shape and has immense potential.
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