What Are Data Discovery Tools?

One of the best practices in business intelligence is the implementation of the right BI tools. Organizations use data discovery software in conjunction with already deployed traditional BI systems to resolve issues with data access, data preparation, and data exploration. Discovery tools are ideal for small businesses that lack the budget for complex data warehouses and the expertise to build them. There are different types of data discovery tools that business users can leverage.

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What Is Data Discovery?

Data discovery is the collection and analysis of data from various data sources to understand trends and patterns. Data discovery tools go hand-in-hand with business intelligence by helping guide data-driven business decisions. Large amounts of different types of data are useless if business users can’t extract insights from them. The data discovery process means merging data sources, cleansing and preparing data, sharing data throughout the entire organization, and running data analytics to gain valuable business insights.

Business analysts rely on data exploration and visual analytics to uncover valuable insights into data. Exploration and visual analytics allow the use of analytics and pattern recognition software for visualization to make data easy to digest and understand. The main benefit of data discovery is the actionable insights that highlight business opportunities, improve workflow, and streamline business processes.

Data Discovery Elements

There are several components of the data discovery process. Data exploration is like data discovery, though it functions on a broader scale. Data exploration happens before business users have specific questions to answers or known data problems to solve. Data exploration allows business users to see what’s available in data sets and the context to ask the right questions.

Data preparation is the organization and delivery of unstructured data for discovery and analysis. Smart data discovery happens when artificial intelligence and machine learning algorithms comb through data sets to find patterns and prepare them for visualization. Smart data discovery improves business intelligence potential and requires less expertise in data discovery processes.

Benefits of Data Discovery

Implementing the right sensitive data discovery tools is one of the best practices of modern business intelligence. It takes a lot of data from different data sources to perform customer and behavioral analytics, given the need to gain insights into purchase histories and customer service inquiries, basic demographics, and online behaviors. Data discovery is a great way to uncover relevant customer data across company databases.

Sensitive data discovery plays an important role in the full life cycle of data. Business data moves through multiple points through the supply chain life cycle. The value and attributes of products may change throughout this life cycle, and these changes can be stored in separate databases. Separating such details makes it more challenging for business analysts’ to track the performance of the supply chain. Data discovery makes it possible to see all data locations and know which questions to answer to understand the supply chain.

Discovery tools enable data visualization so that data can be summarized in a digestible, easy-to-understand way. Data discovery helps business users understand which sensitive data to visualize and how to connect them. Data discovery makes creating meaningful visualizations less time-consuming. Data analysts rely on data discovery when delving into business intelligence data.

Discovery tools help business users maximize business intelligence data to gain a competitive advantage and set tangible business goals. Business analysts can leverage impactful predictive analytics to gain valuable insights into business performance and how to improve metrics. Predictive analytics typically collects data from one database, whereas data discovery provides an opportunity to discover and utilize better-integrated data for predictive analytics.

Business analytics are essential to improving business intelligence and aiding business decisions and goals. Data discovery makes a time-consuming process more streamlined by delving into various data types and using data visualizations to answer specific questions.