The amount and complexity of data generated today is increasing day by day. The diversity of data sources and highly unstructured data exceeds the capabilities of traditional technologies.
For this reason, businesses choose to benefit from artificial intelligence technologies to manage this endless data flow.
Artificial intelligence and data management
According to a survey conducted by US-based consulting firm McKinsey, one-third of respondents use productive artificial intelligence in at least one business function.
While 40% of companies state that they benefit from artificial intelligence, these businesses plan to increase their artificial intelligence expenditures shortly.
It is critical to realize that data needs are changing with the emergence of artificial intelligence in data management.
Data sharing is rapidly spreading throughout society, and institutions want to centralize their data and offer it as a product to their customers.
Additionally, due to the increasing need for data material, the demand for advanced and automated data integration is also increasing.
Artificial intelligence technology can speed up repetitive processes such as clustering, data refinement, anomaly detection and classification, especially by taking advantage of machine learning algorithms .
In addition, thanks to deep learning and natural language processing, operations such as text analysis, sentiment analysis, and image analysis become simpler.
How does artificial intelligence affect data management?
data extraction
The first phase of any data management cycle, data extraction involves extracting and analyzing meaningful information from large data sets.
While in the past data was automatically extracted using template-based techniques to extract data from documents that fit the template, today it is becoming more difficult to process unstructured data sources such as text, PDF, and images with traditional tools.
Artificial intelligence, which facilitates the data extraction process, also eliminates the need for templates to be consistent.
With the help of natural language processing, areas to be extracted from data sources are detected and artificial intelligence-supported data extraction systems are used.
data mapping
After data extraction, the data is mapped to the targeted location.
Formerly a manual procedure in which IT professionals wrote code, data mapping involves organizing data from a data source so that it is stored in a target location or other format.
Today, thanks to the rapid development of codeless data mapping tools, data experts can now perform data mapping with a simple drag-and-drop operation.
Artificial intelligence technologies make it possible to automatically discover data sources, properties and connections.
Machine learning algorithms save time and effort by analyzing current data to find connections and trends. Additionally, artificial intelligence uses semantic analysis and pattern recognition to enable computers to find connections between different schemas, facilitating the schema mapping process.
Data quality
Many businesses have mastered generating large amounts of data but still struggle with data quality. While costs are increasing due to low data quality, it seems that more progress is needed in this area despite the development in data management tools. Artificial intelligence technologies promise unique potential in this field.
Artificial intelligence systems quickly identify and correct errors, inconsistencies, and anomalies in data sets.
Artificial intelligence’s ability to manage incomplete data makes a big difference in data quality. Algorithms detect missing values in the data and complete the data set by filling them with approximate values.
Data analysis
Data analysis, the final stage of the data management process, stands out as where artificial intelligence potentially makes the biggest impact.
Natural language processing integrations in data analytics are becoming more common since OpenAI introduced GPT.
Text-based information obtained from documents, social media and consumer reviews is analyzed using natural language processing algorithms. Artificial intelligence technology uses clustering techniques to combine related data.
There are two basic methods in data analysis: regression analysis and decision trees. Even in multi-dimensional data sets, complex decision trees are easily created with artificial intelligence-supported machine learning algorithms.
