Data is the cornerstone of informed decision-making in today’s business world, vital for everything from understanding market trends to analyzing customer behavior. However, the sheer volume of data being generated has led to challenges in managing and extracting meaningful insights from it. Despite significant investments in data management, a large portion of this data remains unused or even unnoticed within databases.
One significant hurdle is dealing with unstructured data, which makes up around 80 to 90 percent of all business data. Shockingly, more than half of the data stored by organizations worldwide is considered “dark,” lacking any apparent value, while a third is either outdated or redundant. This leaves only a small fraction, about 15%, as truly valuable for business purposes.
To tackle this issue, data abstraction has become crucial. Data abstraction involves filtering out irrelevant or less important data from databases, presenting a simplified version of the dataset. This not only improves data retrieval efficiency but also makes databases more user-friendly. For example, an HR manager might not need access to a candidate’s medical history but does require information about their work experience. Data abstraction allows for targeted retrieval of relevant information.
Ensuring clean and organized data is vital for informed decision-making at an enterprise level. Data abstraction helps eliminate redundant features, reducing file sizes and enhancing efficiency. With the increasing demand for structured data, various services such as data mining, data cleansing, data conversion, and data abstraction have emerged to make data more accessible and user-friendly. AI technology is at the forefront of revolutionizing these processes.
Data abstraction operates across three levels:
- Physical or Internal Level: This is the lowest level, dealing with the actual storage location of data within the database.
- Logical or Conceptual Level: This intermediate level describes the data stored and the relationships between different data entities.
- View or External Level: This highest level determines how data is presented to end-users through the user interface.
Traditionally, data abstraction processes have been limited in their efficiency. However, with AI augmentation, significant advancements have been made:
- Identification of Necessary Data Entities: AI improves accuracy and efficiency in identifying relevant data entities.
- Identification of Key Properties of Entities: AI accurately identifies and categorizes data entity properties.
- Establishment of Entity Relations: AI simplifies identifying and establishing relationships among data entities.
- Mapping Properties to Entities: AI speeds up property mapping, reducing time and effort required for data transformation.
- Removal or Prevention of Duplicate Data: AI algorithms effectively detect and remove duplicate data entities.
- Validation of Outcome: AI-driven validation ensures data accuracy and quality.
In conclusion, AI-driven data abstraction offers a powerful solution to the challenges posed by large data volumes. By simplifying database management and enhancing data quality, AI-driven data abstraction enables businesses to leverage data as a strategic asset, driving innovation and competitiveness in the digital era. Trusted providers like Outsource BigData offer comprehensive AI-driven data abstraction solutions tailored to meet diverse business needs. Visit their official website to learn more about their services and unleash the power of AI in data abstraction.
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