Types of data discovery and accompanying data discovery use cases
Posted: Tue Feb 11, 2025 4:26 am
There are three main types of data discovery practices, and they work in concert to uncover data insights, identify security issues, and provide data analysis via easy-to-understand visual dashboards. When completed and combined with business intelligence (BI) software, these main data discovery categories result in a top-down view of a company's data in a user-friendly format.
Preparation
Data preparation is the cleaning, reformatting and merging of data from different data sources across the organization so it can be analyzed in a consistent format. Steps taken to in the data preparation phase include deduplication, deleting null values, detecting outliers, and generally ensuring only high-quality data is used for business analysis. Technological advancements now allow for much of this work to be done via artificial intelligence.
A financial institution wants to analyze their business models and customer data to identify potential fraud patterns and assess risk. However, the business's data is scattered across multiple denmark whatsapp number data databases, with varying formats and data quality. By using data discovery tools, the institution can connect to these disparate data sources, profile the data to assess its quality, and explore the data to uncover potential fraud indicators, such as unusual transaction patterns or suspicious account activities.
By collecting data via this data discovery process, the institution can better understand the data landscape, identify data inconsistencies, and validate data accuracy. With this knowledge, they can then perform data preparation tasks, such as data cleansing, data enrichment, and data transformation, to ensure that the data is accurate, complete, and ready for analysis.
Data discovery streamlines the data preparation process by providing insights into the data and guiding the institution in transforming the data to a suitable format for analysis, enabling them to effectively detect potential fraud and manage risk in their operations.
Visualization
Data visualization is one of the most effective tools data and business leaders can use to turn their data into knowledge and understand what can be gained from its analysis. Most often presented in the modern enterprise via a data dashboard, visual analytics help non-technical users understand their various data and derive business insights from them.
Data visualization would be useful in the following hypothetical example: A healthcare organization wants to analyze patient data from multiple sources to gain insights into patient outcomes and treatment effectiveness. By using data discovery tools, the organization can connect and explore the data, identifying correlations between patient demographics, treatment protocols, and outcomes.
With these insights, their data teams can create a data visualization such as a scatter plots or heat map to visually represent the relationships between different variables, helping clinicians and researchers identify patterns that can inform clinical decision-making, improve patient care, and drive better health outcomes.
Analysis
Data analysis uses both descriptions and interactive visuals to paint a complete picture of a company's data in a succinct and easily understandable format. Advanced analytics empower business leaders to look beyond the data itself to see the wider implications of their data discovery efforts, uncover deep insights about their organization, and ensure accurate data in crucial business decision-making.
As an example, in a marketing use case, data discovery can help analyze customer data collected to identify trends in customer behavior, preferences, and purchase patterns. This can inform marketing strategies and campaigns, such as targeted promotions, personalized recommendations, and customer segmentation, to drive better customer engagement and improve revenue.
Preparation
Data preparation is the cleaning, reformatting and merging of data from different data sources across the organization so it can be analyzed in a consistent format. Steps taken to in the data preparation phase include deduplication, deleting null values, detecting outliers, and generally ensuring only high-quality data is used for business analysis. Technological advancements now allow for much of this work to be done via artificial intelligence.
A financial institution wants to analyze their business models and customer data to identify potential fraud patterns and assess risk. However, the business's data is scattered across multiple denmark whatsapp number data databases, with varying formats and data quality. By using data discovery tools, the institution can connect to these disparate data sources, profile the data to assess its quality, and explore the data to uncover potential fraud indicators, such as unusual transaction patterns or suspicious account activities.
By collecting data via this data discovery process, the institution can better understand the data landscape, identify data inconsistencies, and validate data accuracy. With this knowledge, they can then perform data preparation tasks, such as data cleansing, data enrichment, and data transformation, to ensure that the data is accurate, complete, and ready for analysis.
Data discovery streamlines the data preparation process by providing insights into the data and guiding the institution in transforming the data to a suitable format for analysis, enabling them to effectively detect potential fraud and manage risk in their operations.
Visualization
Data visualization is one of the most effective tools data and business leaders can use to turn their data into knowledge and understand what can be gained from its analysis. Most often presented in the modern enterprise via a data dashboard, visual analytics help non-technical users understand their various data and derive business insights from them.
Data visualization would be useful in the following hypothetical example: A healthcare organization wants to analyze patient data from multiple sources to gain insights into patient outcomes and treatment effectiveness. By using data discovery tools, the organization can connect and explore the data, identifying correlations between patient demographics, treatment protocols, and outcomes.
With these insights, their data teams can create a data visualization such as a scatter plots or heat map to visually represent the relationships between different variables, helping clinicians and researchers identify patterns that can inform clinical decision-making, improve patient care, and drive better health outcomes.
Analysis
Data analysis uses both descriptions and interactive visuals to paint a complete picture of a company's data in a succinct and easily understandable format. Advanced analytics empower business leaders to look beyond the data itself to see the wider implications of their data discovery efforts, uncover deep insights about their organization, and ensure accurate data in crucial business decision-making.
As an example, in a marketing use case, data discovery can help analyze customer data collected to identify trends in customer behavior, preferences, and purchase patterns. This can inform marketing strategies and campaigns, such as targeted promotions, personalized recommendations, and customer segmentation, to drive better customer engagement and improve revenue.