Data Discovery Definition
Data discovery is the business-user-oriented data science process of visually navigating data and applying advanced analytics in order to detect patterns, gain insight, answer highly specific business questions, and derive value from business data.
What is Data Discovery?
The purpose of data discovery is to reveal relevant data insights, communicate these insights to business users in a way that is accessible to non-technical users, and ultimately improve business processes. The data discovery process is accomplished with visual data discovery tools and business intelligence software that extract data from various sources and consolidate the data into a single location, where users can have a “big picture” view of their data.
The most integral component of data discovery is the interactive visual analysis, which enables users to interactively and clearly view their data from every angle, and drill down to hyper-specific events. Visual data discovery is the subsequent step after data exploration has first refined the data sets.
Data Discovery Categories
The three main categories of visual data discovery include data preparation, data visualization, and advanced analytics and reporting:
- Data Preparation: This preprocessing step uses statistical techniques to merge unstructured, raw data from disparate sources, then clean, transform, and eliminate noise from the data so that quality is consistent and formatting is usable.
- Data Visualization: The human brain instantly recognizes patterns and relationships in visualizations. Data visualization is a critical process for analyzing big data, in which important insights and messages would otherwise be lost, and for displaying the results of machine learning and predictive analytics.
- Advanced Analytics and Reporting: Descriptive statistics organizes, summarizes, and breaks down data into a simple, intelligible report that is easy to understand and helps businesses make data-driven decisions.
Types of Data Discovery
There are two main data discovery processes: Manual and Smart:
- Manual Data Discovery: Manual data discovery is the manual management of data by a highly technical, human data steward. Before advancements in machine learning, data specialists would manually map and prioritize data, monitor and categorize metadata, document rules and standards, and conceptualize all available data using critical thinking.
- Smart Data Discovery: Smart data discovery solutions provided an automated experience. With the advancement of machine learning, smart data discovery software has been developed, using Artificial Intelligence to automate data preparation, conceptualization, integration, and presentation of hidden patterns and insights through data discovery visualizations.
Does OmniSci Offer a Data Discovery Solution?
OmniSci provides an interactive, visual solution to the data discovery challenge. Data scientists can cross-filter on a combination of attributes, allowing them to quickly explore how different features interact, and develop a faster understanding of the data.
OmniSci’s Immerse visualization system can display multiple different datasets from different sources on the same dashboard. Each chart or groups of charts on a dashboard can point to a different table without requiring the merging of underlying tables. This saves time on data preparation and reveals previously unknown relationships that can help you innovate your ML models faster.