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[REPLAY] Product Talk: Using AI to enhance the data marketplace search experience
Watch the replayDatasets often comprise a large number of data points gathered from a range of sources, making it difficult to gain a comprehensive view of what the data contains. Data exploration provides this insight, prior to more detailed analysis.
As it uses data visualization techniques, data exploration outputs (such as charts or graphs) are easier for humans to process, understand and act on.
Data exploration helps identify:
Data exploration provides the foundation for data analysis, enabling:
Data exploration can be carried out through both manual analysis and automated data exploration software solutions. Manual methods include writing queries scripted in languages such as Python, SQL or R, and spreadsheets such as Microsoft Excel, while automated data exploration tools, such as data visualization software and business intelligence software, help speed up and scale the process.
Exploratory Data Analysis (EDA), is a subset of data exploration made up of statistical techniques such as correlation, regression testing,standard deviation, dimensionality reduction, significance testing and principal component analysis, used to analyze data sets for their broad characteristics.
There are three general steps included in data exploration:
While it has similarities with other data techniques, data exploration is a standalone discipline, as the comparisons below show:
Data exploration manually analyzes data, whereas data mining is an automated process that aims to extract useful information and patterns from large datasets. Data exploration typically occurs before data mining in order to understand relationships and thus focus algorithms most effectively.
Data exploration often involves data visualization, helping to understand datasets and find patterns by representing them visually, such as through charts and graphs. However, data visualization has many more uses than solely data exploration – for example, it can be used to visualize datasets on a data portal or data marketplace, as graphs, maps and dashboards, helping make them more understandable and usable to non-specialists.
Data discovery and data exploration are related but different concepts. Data discovery involves the process of helping users to search for and find specific data, such as through a data catalog or data marketplace. It is key to making data assets available and consumable at scale across organizations and ecosystems. Data exploration happens before data discovery, and gives deeper insight into the meaning of a dataset by identifying areas or patterns to dig deeper into.
Organizations now generate an enormous range of data assets across their operations and departments. Harnessing this data successfully starts with understanding what data is available and where it is located through centralized data catalogs. This blog explains what they are and how they can benefit businesses.
Organizational silos prevent data sharing and collaboration, increasing risk and reducing efficiency and innovation. How can companies remove them and ensure that data flows seamlessly around the organization so that it can be used by every employee?