What is Data Discovery?
Data Discovery is the process of identifying patterns, trends, and insights within a meaningful dataset. It includes collecting data from various types of sources and then applying an advanced Data Analytical technique for identifying the patterns and themes within the collected dataset.
It involves examining & analyzing data to uncover the hidden patterns, correlations, connecting patterns and valuable information that can be used for references,decision making & problem solving etc. The main goal of data discovery is to gain a deeper understanding of data, discover new insights and get meaningful and knowledgeable information.
Key Aspects of Data Discovery
- Data Exploration – It includes exploring the dataset to understand its structure, characteristics and relationships between variables in a dataset. It includes the visualizations of data, summary statistics & other data analytical techniques. It includes exploring a large dataset and then finding patterns & meaningful insights in it.
- Recognizing Pattern – Identifying patterns, trends & correlations within a given dataset. It can involve various machine learning algorithms and other data mining techniques to uncover the hidden insights. Recognizing the pattern is very useful as it gives us future insights of a given dataset. The common patterns which are found helps us to understand a given dataset in a very technical way. Therefore, finding a significant pattern and trend is very useful.
- Visualization – Data visualization includes the use of charts, graphs, pictographs and other visual representations to present the data in a very systematic way. Using this visual representation helps to understand, interpret & analyze data in a very effective and easy way. Visualization also helps in spotting down the patterns and trends in the given data graph.
- Interactive Analysis – Interactive analysis enables users to interact with the dataset and modify the variables to gain better perspectives & insights. This often involves use of interactive dashboards and tools that allow users to go deep in specific aspects of a dataset. Interaction of the user with the data helps in better understanding of a dataset.
- Data Profiling – Data Profiling includes examining the quality of dataset, including the missing values, the outliers, the errors & the inconsistencies. Understanding the quality of a given dataset is a crucial factor for accurate data analysis and decision making. Therefore, data profiling is also an important key aspect 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