Categories of Data Discovery
There are two main categories of data discovery:
- Manual Data Discovery :Manual data discovery is the management of a given dataset manually by a highly technical, human data. In earlier years before advancement in machine learning the technicians and the data specialist would manually map and prioritize data, monitor and categorize the metadata and understand,analyze & conceptualize all given data by critical thinking. Manual data discovery is a comparatively slower process and it has the chances of being inaccurate sometimes.
- Smart Data Discovery :Smart data discovery includes the experience of data discovery in a very automated way. As with the development of machine learning and AI smart data discovery has been developed. As Artificial Intelligence is growing, ways like automated data preparation ,data conceptualization, integration and presentation of hidden patterns and insights can be seen growing. Smart data discovery is a comparatively faster and accurate process of data discovery.
What is Data Discovery?
Data discovery is a pivotal step in the data analysis and business intelligence process, allowing organizations to make informed decisions, achieve dynamic growth, and stay competitive in the marketplace.
Table of Content
- What is Data Discovery?
- Key Aspects of Data Discovery
- Why is Data Discovery important ?
- Categories of Data Discovery
- History of Data Discovery
- How is Data Discovered? – Process
- 1. Define the Subject
- 2. Data Collection
- 3. Data Cleaning and Preparation
- 4. Data Analysis and Exploration
- 5. Communicate Findings and Iterate
- Common Data Discovery Challenges
- How to Overcome Common Data Discovery Challenges?
- Data Discovery Use Cases
- 1. Business Intelligence (BI) and Reporting
- 2. Customer Analytics
- 3. Fraud Detection and Security mechanisms
- 4. Supply Chain Optimization
- 5. Healthcare Analytics
- Conclusion