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Data quality management: Neural networks to optimize data quality

Data is the fuel for successful companies. High-quality data is essential for well-founded decisions. That is why strong data quality management (DQM) is a must.

Data quality management is crucial for companies. High-quality data forms the basis for well-founded analyses and business decisions. Data quality measures the accuracy, relevance, and reliability of data in its application context. The requirements vary depending on the company and application. Ensuring high data quality for AI applications is correspondingly complex. 

A good data quality management (DQM) system is essential. It ensures the integrity, plausibility, and reliability of the data. DQM uses customised processes to monitor, evaluate and optimise data quality. Companies can thus ensure the highest quality standards that enable effective AI applications. 

Dynamic data quality filters are crucial in DQM. They recognise and correct data errors in real time. For example, artificial neural networks, such as LSTM networks, are useful for data quality and the standardisation of time series data, especially in the Internet of Things (IoT). They are used as powerful algorithms to effectively solve data quality problems. 

LSTM (Long Short Term Memory) networks store past information over long periods of time and use this to accurately predict future data points. This allows them to interpolate missing data and improve data quality, especially for time series with incomplete values or outliers. The flexibility and performance of LSTMs make them ideal for data quality filters that continuously monitor and improve data. 

An effective data quality management system utilises dynamic data quality filters and technologies such as LSTMs. It provides companies with a competitive advantage by continuously improving data quality. This enables companies to make informed decisions, optimise business processes and increase customer confidence. The integration of such systems into the data workflow makes it possible to optimise the use of data resources and strengthen the competitive position in a data-driven world. M&M Software supports you in developing customised solutions that improve your data quality and promote data-based decisions.

About the author

 

Sharon Kwikiriza is studying International Business Information Systems at Furtwangen University. As part of his internship semester at M&M Software, he is a member of the Data & AI team. He is focusing on the application of various machine learning algorithms for the automated standardisation and improvement of data quality.

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