Data Exploration

In industries ranging from finance to entertainment, firms are collecting vast amounts of data—and from a wide array of sources—that they hope will provide their decision-makers and other stakeholders with valuable insights into their business. However, despite leveraging advanced technology to collect and store data, many firms are struggling to drive value from their data stores. As a result, there has been a rising demand for easy-to-use data exploration and data management tools that help firms extract timely and actionable intelligence from their datasets.

Modern BI and analytics tools democratize data, putting the power of data exploration in the hands of business users of all skill levels. They help users manipulate, make sense of, and best utilize data in their day-to-day workflows.

What data exploration is and why it’s important

Data exploration is the process through which a data analyst investigates the characteristics of a dataset to better understand the data contained within and to define basic metadata before building a data model*. Data exploration helps the analyst choose the most appropriate tool for data processing and analysis, and leverages the innate human ability to recognize patterns in data that may not be captured by analytics tools.

The analyst uses manual methods and/or automated data exploration tools to determine the size, structure, completeness, and correctness of a dataset and to identify potential relationships among elements in the data. Statistical software and data profiling and visualization tools are used to find anomalies and meaningful trends and patterns in the data. Data visualization software makes it easy for non-technical users to quickly grasp and identify the most relevant features of a dataset. For everyday business users, data exploration is a way of extracting knowledge from organizational data sets. It can be used to help them better understand a situation or issue, answer specific business questions, and support the decision-making process.

*What is data modeling? It is a formal representation of the content, structure, operations, and constraints of your data. It is used to organize data elements, standardize how the elements relate, and determine how data is exposed to the end-user.

Data discovery solutions: Software used for data exploration

There is a range of software solutions and services on the market that enable business users to implement data exploration with minimal to no involvement from IT. These include data exploration and data cataloging tools, data management services, AI-powered data discovery tools, self-service BI and analytics tools, enterprise analytics platforms, and data integration and data discovery platforms. These tools provide users with simple, intuitive ways to explore datasets and look for meaningful patterns and connections. They enable users to interact with data visually, via mobile devices, and even conversationally, depending on the tool’s features.

With the right data exploration tool, users can gain business-critical insights faster, sharing their insights via compelling reports and interactive dashboards, thereby fostering collaboration and data-driven decision making across the organization. If the tool or platform supports big data and AI, they can even create intelligent decision support systems leveraging vast quantities of data and augmented analytics to accelerate strategic decision making in highly competitive or volatile markets.

Features to look for in a data exploration solution

Due to the number of products and services available today, it can be hard to decide on a tool to experiment with. Here are some key features you may want to consider when comparing data exploration solutions:

  • Connectivity: Does the tool provide quick and easy access to your databases, applications, spreadsheets, websites, data lakes, and custom data sources?
  • Data discovery: Does the tool let non-technical users easily find the information they need through search capabilities? Through smart visualizations? Via mobile device?
  • Powerful, non-standard features: Does the tool support big data analytics and other advanced analytics? Does it incorporate AI technologies like machine learning and natural language processing?

Qlik Sense®: Data exploration without boundaries

Qlik Sense is a modern enterprise analytics platform from the pioneer in user-driven business intelligence - Qlik. It supports the broadest range of analytics use cases—from self-service data visualization and data exploration to interactive dashboards, to embedded analytics, mobile analytics, and reporting including KPI reporting and publishing—all within a governed, multi-cloud architecture.

Running on a unique Associative Engine, Qlik Sense offers unprecedented freedom of exploration, enabling users of all skill levels to load, combine, and manipulate data without the limitations of query-based tools. With Smart Search, users can search across an entire application or all their data using keywords to refine context and reveal important connections. Because Qlik indexes and understands all relationships in the data, Qlik Sense not only shows the user related values but also the unrelated, hidden insights that exist across all their data sources.

And with Qlik’s Cognitive Engine, Qlik Sense further accelerates and augments the analytics process, offering users AI-fueled insight suggestions, automation, and natural language interaction via chatbot or digital personal assistant – Qlik Insight Bot. Qlik Sense generates smart visualizations that show users the overall shape of their data set, exposing outliers and hotspots and allowing them to quickly identify significant patterns and discover valuable insights.


What are data visualization tools?

They are software applications using visual elements such as scatter plots and histograms to display data. Representing data graphically helps users quickly ascertain the characteristics of their datasets and more easily find meaningful patterns, trends, and outliers.

What is the role of data visualization in data exploration?

By enabling users to view their data graphically and interact with datasets visually, data visualization makes it easy for non-technical business users to explore, manipulate, find important patterns and trends in, and obtain actionable insights from large, complex datasets.

What are the modes of data exploration?

Data can be explored (1) by query, or through the repeated querying of a dataset (2) by example, or through the exploration of similar data items, (3) by facet, or through the exploration of different attributes of data, or (4) by analytics, or by looking for meaningful patterns and trends in data.

Learn more about Qlik’s modern analytics platform for the enterprise.

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