Data discovery is the process of bringing together disparate datasets and creating new threads of meaning with the data. As data can be spread across multiple databases, data discovery helps to determine which databases should be analyzed together to produce more valuable insights.
Data discovery also works as a precursor for data visualization, allowing individuals to build compelling visual representations out of the various data that has been brought together and analyzed.
Portions of this definition originally appeared on Datamation.com and are excerpted here with permission.
What are the elements of data discovery?
There are three key steps in the data discovery process:
- Data exploration typically happens before data discovery and involves bringing together datasets and determining what questions need to be answered about them.
- Data preparation is the process of organizing and handing over raw data for discovery and analysis. While this process can be done manually, tools for extract, transfer, and load (ETL); data warehousing, and data visualization may be needed for larger datasets.
- Smart data discovery involves using artificial intelligence (AI) to comb data to discover patterns and prepare them for visualization. Companies use smart data discovery for its business intelligence (BI) potential and because it requires fewer experts in data discovery procedures.
Data discovery’s benefits
Customer and Behavior Analysis
Data discovery allows analysts to assess trends in customer behavior based on relevant customer data, such as purchase history, customer service inquiries, basic demographics, and online behaviors associated with a company’s brand.
Full Life Cycle of Data
With data discovery, analysts can look at data in complicated corporate processes, such as supply chain management, wherever it is in the process’s life cycle. It allows them to know what questions need to be answered to better understand the process and how it is performing as a whole.
Enabling Data Visualization
Data discovery helps analysts create meaningful, easily understood visualizations by helping them determine which pieces of data should be visualized and how to connect them.
Business Intelligence Initiatives
Using data discovery, analysts can examine pools of BI data with specific goals about the data they want to discover. With a data discovery mindset and data discovery tools, businesses can maximize their BI data to compare themselves against competitors and to set goals.
Impactful Predictive Analysis
Predictive analytics give companies valuable insight into how they’re performing now and what they may need to do to improve future metrics. By incorporating data discovery, analysts increase their opportunities to find and use more integrated and holistic data.
