What is Data Discovery?

Data Discovery Meaning

Data discovery is a data analysis technique that involves applying various techniques, such as data mining and interactive visualization, to a company's data with the goal of finding and understanding patterns in the data.

Data discovery has been traditionally associated with descriptive analytics, which is used to provide insights into what happened in the past. However, it has recently become associated with predictive analytics, which is used to determine what will happen in the future.

Data discovery tools help business users (non-technical users) access and analyze complex data sets within their organization. The tools provide visualizations and other pre-built analyses that allow business users to answer specific questions about the data.

What is the goal of Data Discovery?

The goal of data discovery is to help organizations uncover valuable insights from their data sets, allowing them to make more informed business decisions.

In the past, business analysts were required to write specific queries and use specific tools to explore their data. Today, advances in user interfaces (UI) and artificial intelligence (AI) technology have made it possible for users to ask questions of their data using natural language processing (NLP), where they can ask a question using common business terms instead of SQL or other query languages.

Additionally, many software vendors have added machine learning capabilities to their products that can automatically suggest relevant visualizations based on a user's query.

Data discovery is an interactive, iterative process of uncovering and visualizing patterns in data. The goal of data discovery is to reveal trends in the data that were previously unknown, including trends that might be useful for making better business decisions.

Data discovery software generally has a user-friendly interface that allows users to perform free-form exploration of their datasets by dragging and dropping elements, applying filters and sorting data.  Data discovery tools can also include natural language query (NLQ) features that allow users to type in questions or statements and receive visualization of the results.

What's the difference between Data Discovery and BI?

In contrast to traditional business intelligence (BI) tools, data discovery systems do not require the involvement of a technical department for the design and implementation of complex queries or reports. Instead, data discovery tools allow users to explore their organization's data with intuitive navigation and visualization capabilities, which are often drag-and-drop.

A data discovery is an approach to get the information from the unstructured data and structured data. By using this method you can see the hidden patterns in your data.

It works on both structured and unstructured data.

A data discovery is a process of analyzing the data sets to extract meaningful insights.

Data discovery tools are used to simplify the process of identifying interesting patterns, relationships, and trends by applying visual analysis techniques. These tools provide interactive dashboards that allow a user to explore both structured and unstructured data without requiring any knowledge of statistics or programming.

Data discovery is also known as “data exploration” as it involves exploratory analysis of big datasets with no prior knowledge of what results you may expect from the analysis. Data discovery allows you to visually analyze your data, to mine new knowledge from it and answer questions that you might not have thought of before.

Examples

Here are some examples of data discovery use cases that would be relevant to data and analytics engineers:

1. Understanding data sources: engineers need to have a complete understanding of the data sources they are working with to ensure they can extract the right insights from the data. Data discovery can help analytics engineers to discover new data sources, understand their metadata, and assess their quality and relevance.
2. Identifying data lineage:  engineers need to understand the lineage of the data they are working with, including how it has been transformed, integrated, and stored. Data discovery can help analytics engineers to identify the sources and lineage of data, understand the relationships between data assets, and ensure data integrity and accuracy.
3. Finding data anomalies:  engineers need to identify and resolve any data anomalies that may impact the quality of their analytics results. Data discovery can help analytics engineers to identify data anomalies, detect patterns and trends, and ensure that data quality issues are resolved.
4. Improving data governance:  engineers need to ensure that the data they are working with is compliant with regulatory requirements and internal policies. Data discovery can help analytics engineers to discover data assets, classify them based on sensitivity levels, and ensure compliance with regulatory requirements such as GDPR, CCPA, and HIPAA.
5. Discovering new data insights:  engineers need to explore new data insights and discover patterns that can help improve business outcomes. Data discovery can help analytics engineers to discover new data relationships, identify trends, and extract insights that can inform decision-making processes.

Learn more with Secoda

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