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Data discovery is used to identify, locate, and understand data relevant to an organization’s needs. This is done through data profiling, data analysis, and data visualization techniques.
This creates a comprehensive understanding of an organization's data landscape, including the types of data that are available, structure, relationships, and quality. The information can be used to identify an organization's business needs, reduce time to insights, and allow users to analyze data at a larger scale.
Solving business problems with data discovery is done by identifying a problem or question, and then using data discovery techniques to locate relevant information. The data can then be analyzed to gain insights and identify patterns that inform decision making. Some uses of data discovery include user retention, history, analyzing market trends, identifying user behavior, competitor activity, and finding new opportunities for growth.
Data discovery can be conducted using various technologies and tools, like data profiling, data visualization, and data mining. These tools automate the data discovery process, making it more efficient and cost-effective. It's important to choose the right tools and technologies that align with the organization's data landscape and business requirements.
Data discovery tools and platforms have an increasing focus on automation, machine learning, and artificial intelligence. With the growing volume and complexity of data, manual data discovery processes will become increasingly difficult and time-consuming. Advanced data discovery tools and platforms automate the data discovery process, making it more efficient and cost-effective. Data discovery also integrates machine learning and artificial intelligence to automate data profiling, data analysis, and data visualization tasks. It has also been used in natural language processing and generation tools. Data discovery makes analyzing and understanding large amounts of information more accessible, user-friendly, and cost-effective.
What is data discovery in a data lakehouse?
It is the process of locating and identifying the location, format, schema, and metadata of the stored data, as well as understanding its quality, and relevance to other data assets in the lakehouse.
Why is data discovery important?
It enables organizations to find and leverage valuable data that would otherwise be difficult to access or analyze. This helps organizations improve data quality, enable data governance, and gain insights into customer behavior, market trends, and business performance.
What are some best practices for data discovery?
Establishing a data catalog or data dictionary that documents the location, format, and metadata of data can be helpful in terms of data discovery. Using metadata management tools to automatically capture and organize metadata about the data assets and leveraging data profiling tools to analyze the quality, completeness, and accuracy of the information are also good practices.
How can data discovery be automated in a data lakehouse?
Data discovery is automated by using management and data profiling tools that can automatically capture and organize metadata about the data in the lakehouse. Machine learning algorithms are used to automatically classify and tag data assets based on their content and usage.