History of Data Discovery
The history of Data Discovery is closely associated with the development of data analytics. In the 1970s, Business Intelligence became popular, promoting the use of data to support decision-making. The Data Warehouses were developed in the 1990s, which aimed at collecting data for proper analysis. By the time 2000s arrived, technology had developed highly efficient analytical approaches like data mining and predictive modeling. After 2010, data discovery platforms with specific functionality became popular. They offered a full solution for data preparation, visualization, and discovery. This continuation brought in the 2010s with the Big Data era, necessitating such powerful tools and techniques. Today (roughly around the mid-2010 to the present), AI and ML are revolutionary and therefore, by automating insights generation and employing intelligent processes, data discovery is becoming enhanced.
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