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Enterprise Data Management

What is Data Discovery? | Tools and Use Cases - Guide

Chandan Gaur | 30 December 2022

What Is Data Discovery?

Data discovery is an iterative process that explores data through visual tools that can allow business users in every department to understand their customers and operations without IT or expertise.

We collect data from multiple sources and integrate it on a unified platform to discover patterns in it. Visual analysis, such as tables and graphs, plays a vital role. The compiled data is visually presented, enabling users to discover trends, patterns, and inconsistencies in the data in a quick glimpse. It is a method rather than a tool. This process aims to support users in making strategic decisions by using interactive and mandate discoverable visual data.

Why data discovery? 

Data discovery platforms find data faster if a new joiner within the organization requires data for analysis or to create a machine learning system. How would the intern discover the proper tables and columns to use? How would the intern quickly assess the organization's suitability?

ElixirData - Data Discovery Platform helps organizations understand where corporate data is located and who can access it. Book a demo today 

How does Data Discovery work?

Three fundamental principles govern the process. The three fundamental components are data preparation, data visualization, and guided advanced analytics. A brief review of the fundamental data discovery principles will aid in understanding how it works.

Data Preparation

Using error-ridden raw data leads to erroneous insights in the data analysis step. And for this reason, it makes data preparation a critical component of data discovery.

Data preparation gathers information from several sources and stores it in a single spot. Data profiling, also known as data source identification, can help you save time and enhance accuracy. Disqualifying data sources can help filter out a lot of the noise when cleaning data.

Data Visualization

Visualization is an essential aspect of it. Visual data patterns can be identified using heat maps, charts, graphs, and scatter plots.

Guided Advanced Analytics

Guided analytics is created by developers based on a particular business requirement. It helps automate expert data skills, helps learners convert data into information and knowledge, and reduce customer churn.

What are the Use-Cases of Data Discovery?

The best Use-Cases are listed below:

Strategic dashboards 

They are built with a focus on in-depth analysis. KPI metrics, such as revenues from the previous quarter, may be included in this study. Because of their design, strategic dashboards are excellent for presenting information to department heads and C-Suite executives.

Analytical dashboards

On the other hand, they are more common. These dashboards allow users to research and experiment with data quickly. These dashboards are famous among IT and development teams.

Advanced Analytics

Data analysis is a time-consuming task. On the other hand, the result is often worth the effort. A successful analysis begins with a particular problem that you want to solve with data. This method prevents data exploration from becoming aimless.

Advanced business intelligence technologies make extracting more information from enormous data easier. Predictive analytics, for example, can model future trends and provide valuable insight into what may occur. Acting on the insights is the only way to maximize the impact of advanced analytics.

Big Data Discovery is the logical combination of Big Data and Data Science. Each of these areas is in explosive growth. Big Data Discovery is a Hadoop-native end-to-end solution for visual Big Data analysis. Know how Big Data Discovery is being using in Blockchain 

Why is data discovery creating so much buzz? 

Business intelligence is a subcategory of data-guided analytics is created by developers based on a particular business requirement. It helps automate expert data skills, helps learners convert data into information and knowledge, and reduce customer churn. Discovery. It is the operation of integrating data from different databases into a particular source so that trends can be investigated and detected more smoothly. The following are the five benefits of it for businesses today.

  1. A comprehensive view of the company data
  2. Democratized decision-making and insight
  3. Enhanced risk management and compliance
  4. Automatic context-based data classification
  5. Controls real-time data

Let us discuss each one in length. 

A comprehensive view of the company data

Data discovery provides businesses with a comprehensive view of their organization's numerous data streams, enabling them to merge them in their analyses and develop well-rounded workarounds to their challenges or consumer demands. A retail bank, for example, can combine customer data from its website, mobile app, social platforms, and ATMs to create a more accurate picture of each person it serves and attempt to understand its behavior.

Democratized decision-making and insight

IT and data skills should not be required to gain business insight. Data discovery makes data analysis easier for all organizational stakeholders, regardless of data literacy. Sales teams can see how their strategies influence or discourage leads throughout the sales funnel. Marketers can link data from various customer touchpoints to see how activities correlate with sales success. In short, it offer an almost infinite number of applications to meet the needs of various business teams.

Enhanced risk management and compliance


Risk management and compounded analytics are created by developers based on a particular business requirement. It helps automate expert data skills, helps learners convert data into information and knowledge, and reduce customer churn. The license has risen to the top of corporate agendas as data volumes increase and governments invest more in data protection. Data discovery assists businesses in identifying outliers and potential threats in their data, allowing them to manage them more proactively.

Companies can also stress-test their data management practices to ensure compliance with regulations such as the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR) (GDPR).

Automatic context-based data classification

Businesses collect more data from more sources and in new formats every day. Data discovery enables accurate classification of this information based on the channel, conditions, and context in which it is collected. Retailers, for example, can distinguish between customer data collected by their marketing, sales, and service teams to assess their entire customer experience rather than a single particular time.

Controls real-time data

Companies can apply specific actions to the data they collect in real-time using predefined controls or situational factors, ensuring that it is properly stored and analyzed and that data practices are secure and compliant. Data discovery is necessary to attain this degree of control.

Next-generation Data Discovery Capability is a must

Whereas business intelligence aids in reporting and performance analysis, data discovery aims to take BI analyses to another level by assisting corporate leaders and their teams across all departments in finding and transforming data into actionable insight. It empowers leaders to thoroughly investigate their processes and business models at a fine-grained level and make advancements that add significant returns for their organizations to make better decision-making.

What is Governed Data Discovery?

Data governance and data discovery go hand in hand. Data governance contributes to the first category of data discovery: data preparation because it creates the policies and processes that ensure the quality of a business's data. Because most data is already aligned with organization-wide definitions and formats, strong data governance can help streamline and shorten the data preparation phase.

Furthermore, centralizing data storage, perhaps in a data warehouse, is a crucial tenet of most data governance strategies.

Data governance and data discovery go hand in hand. Data governance contributes to the first category of data discovery: data preparation because it creates the policies and processes that ensure the quality of a business's data. Because most data is already aligned with organization-wide definitions and formats, strong data governance can help streamline and shorten the data preparation phase.

Furthermore, centralizing data storage, perhaps in a data warehouse, is a crucial tenet of most data governance strategies, assisting compliance. Centralization is also required for effective data discovery. Data centrally located can be more easily accessed, shared, and modified, ensuring that every team works with the most up-to-date information.

What are the different types of Data Discovery tools?

An increasing number of data discovery tools are available, generally included as part of a business intelligence solution, and cover at least one of the three main data discovery categories: data preparation, visual analysis, and guided advanced analytics.

These tools, which have nearly unlimited applications throughout most industries, open the way for all levels of personnel, many of whom lack the skills to access complex data sets from various sources and collect the information they require on their own.

Advanced solutions, readily available in the cloud, enable simple data navigation and search functionality. They should organize and prepare data, including straightforward visualizations, for analyses that can be shared across the entire organization.

What are the Attributes of Data Discovery Tools?

The main attributes that need to be considered are mentioned below:

Currently, tools are available in business intelligence software – platforms designed to improve conventional BI capabilities.

We previously discussed the three steps of data discovery: data preparation, visual analysis, and guided advanced analytics. The software you use to explore your data should lay the foundation for these three crucial phases. Here is a list of significant attributes that these types of tools should have for an effective data discovery process:

Be User-friendly

Traditionally, professional analysts or IT experts handled data discovery and analysis.It has changed as a result of modern technologies and Data democratization. A user-friendly interface is one of the guiding principles of any modern data discovery tool. It will enable everyone to take advantage of the information and tools at their disposal.

Be Quick

The market's offering of tools has grown in tandem with data growth in recent years. With so many businesses benefiting from these solutions, you must ensure that you invest in technologies that give your company a competitive advantage. And in a fast-paced industry, time is of the essence.

Work efficiently with large amounts of data

Visual discovery is assisting in the expansion of traditional business intelligence. When you have multiple databases from various touchpoints, look for a tool that allows data integration regardless of the amount of data you include. Aside from connecting the data, the discovery tool you select should also allow you to work with vast amounts of data.

Have interactive visualizations available

A good data discovery tool should include powerful and interactive data visualizations. It provides a dashboard creator with a large number of predefined templates. The image below shows an example of a finance dashboard that displays the key metrics needed to understand a company's financial health at a glance.

Have enhanced graph choices available

Aside from interactive visualizations, well-designed software must include advanced chart options for providing quality analysis and extracting the most value from your data. 

Some of the advanced features that should be included are:

  • It can combine multiple chart types into a single one.
  • Create a secondary axis to allow for more advanced comparison and analysis.
  • Use different trend indicators, such as colors to indicate positive or negative outcomes.
  • Allow conditional formatting to highlight out-of-the-ordinary values and uncover hidden trends or patterns.
  • Use the comparison period features to compare data from two periods and draw conclusions based on performance and goals.

Custom fields option should be available.

Custom fields are another essential feature in professional data discovery tools. They enable users to add individual calculated fields and combine multiple category aspects into a merged field using various join types. Custom fields are available in many professional dashboard tools in the market and can be easily created with a drag-and-drop function.

It can be easily shared

Collaboration is essential for effective data management. People should be able to read and understand data patterns across all departments with the help of an ideal data discovery tool. These tools enable you to give stakeholders different access levels based on their roles and the data they need to use. When investing in a data discovery platform, one of the features you should look for is sharing data.

How to Get Started With Data Discovery?

Organizations must combineClick to explore more about Smart Data discovery data streams from various online and traditional systems. They must collect data about their clients and suppliers and their operations.
Organizations can use these processes to connect data from all sources, prepare it for analysis, share it among internal teams, and support critical decision-making with valuable data-driven insight. Artificial intelligence (AI) has now added a new level of sophistication to data discovery.
Using manual or advanced methodologies generally involves the following steps, with iteration occurring continuously.

  1. Recognize what data is required for the data analysis in question.
  2. Researchers must identify data sources that will provide that information.
  3. Create a search query once you've gathered all of the necessary information.
  4. Based on the preliminary results, your data team will further investigate the data sources' relevance, eradicating insignificant data and refining queries as necessary.
  5. Researchers can assess the quality of their findings and repeat these steps until they are convinced they have good answers.

How to Perform Smart Data Discovery?

Different ways for performing Smart Data discovery are mentioned below:

  • Define your business goals
  • Determine your problem areas
  • Combine various data sources to gain a deeper insight
  • Clean the data extracted
  • Choose the right data discovery tool
  • Automate the process
  • Make the data availablesmart-data-discovery

Define your business goals

The first step in performing intelligent data discovery is defining your business goals to ensure that your process. Smart Data discoveries is focused on accurate information. You can start a conversation with your key stakeholders and team members to define the primary strategic goals for the business and the metrics you will need to track the progress and measure the success.

Determine your problem areas

After defining your goals, it's time to recognize your problem areas or the roadblocks keeping you from becoming a more competent, intelligent business entity. While every organization is unique, and one size does not fit all, several problem areas frequently cross over from one to the next.

Combine various data sources to gain a deeper insight

Collecting data from various sources allows you to gain insights to propel your business forward. Data comes from various structured and unstructured sources. Integrating your data with the data of others will provide you with a greater understanding of what you need to do to accomplish your goals.

Clean the data extracted

Before visualizing your data, please clean it up and extract insights from it. It will also make it easier for BI reporting tools to interact with your data and perform intelligent data discovery. The ultimate goal of the data cleaning stage is to avoid the risks of working with inaccurate data, which can harm your business.

Choose the right data discovery tool

What precisely is an intelligent data discovery model? Fundamentally, this type of model is a strategic approach to data use. Entity-relationship diagrams, data mapping specifications, data matrices, and data flow diagrams are examples of discovery models in terms of approach. Selecting the perfect report tools to do the job is also an important consideration.

Automate the process

With such a large amount of data, preparing it can be time-consuming. As a result, BI solutions with automation technologies offer the benefits of more advanced data classification methods such as catalog-based search or patterns search. Another significant advantage of data discovery automation is real-time decision-making.

Make the data available

When we speak of ease of access, we mean using user-friendly tools that anyone in the organization can use without any tech expertise. The marketing team, for example, should be able to easily access IT metrics to support their e-commerce strategies. You will lay the foundation for the much-desired data-driven culture and empower everyone else in your organization to play an active role.

How does Data Discovery Give Security?

Businesses require advanced security methods to ensure and protect their data, regardless of the source and use, to manage the growing volume and complexity of data they collect. IT security teams prioritize data discovery to safeguard their data and achieve data compliance.

Intelligent data discovery is usually the best option, assisting businesses in detecting anomalies, like accessing specific data and defending themselves and their customers against risk. Artificial intelligence algorithms enable automated data monitoring and the regulation of specific data protection laws. As a result, firms can effectively track, control, and protect themselves from threats, if they are external ransomware attacks or team members' fault.

Data discovery, in particular, assists businesses in better finding and classifying data from various sources, detecting and flagging anomalies, and addressing the issues before they can become threats. With vast amounts of money in fines issued to businesses worldwide for inadequate data privacy and security, the need to strengthen defense mechanisms is operational and a matter of financial viability and reputation in today's data-driven economic system.

What are the best tools for Data Discovery in 2022 ? 

Below is the list of the 12 best Data Discovery Tools in 2022


Data discovery is not a tool. It is a business user-oriented process that evaluates and collects data from different sources and is often used to understand patterns in the data.

A data discovery platform is a set of tools that provides automated features and real-time data access, detects patterns, and those outlier results outside of patterns, in data. Organizations worldwide are digitizing more aspects of operations as consumers increasingly use digital platforms to research, engage with, and purchase the goods and services they require. In today's increasingly data-rich business environment, data discovery fills the gap between companies' priorities and customers' needs. It makes organizations more conscious of how they conduct business while ensuring consumer data is put to much better use — leading to an improved overall experience for all.

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