Features of Good Visual Analytics and Data Discovery Tools

Introduction

Visual Analytics and Data Discovery enable you to analyse large datasets to find valuable insights and information. This is much more than just classic Business Intelligence (BI). See this article for more details and motivation: "Use Visual Analytics to make better decisions: the Exa Death Pill ...". Let's look at the important resources to choose the right tool for your use cases.

Comparison and evaluation of the Visual Analytics tool

• Several tools are available in the market for Visual Analytics and Data Discovery. Three of the best-known options are Tableau, Qlik and TIBCO Spotfire. Use the following list to compare and evaluate different tools to make the right decision for your project:
•  Ease of use and an intuitive user interface for business users create interactive views
• Various display components, such as bar charts, pizza graphics, histogram, scatter charts, treemaps, grid charts and much more
• Connectivity with various data sources (for example, Oracle, NoSQL, Hadoop, SAP Hana and Cloud Services)
•  Real ad-hoc data detection: real interactive analysis through drag-and-drop interactions (for example, restructuring tables or linking different datasets) instead of "just" seeing detailed datasets/roll-up in tables.
• Support for loading and analyzing data with alternative approaches: in memory (for example, RDBMS, spreadsheets), in the database (for example, Hadoop) or on demand (for example, event data flows)
• The functionality of grouping of data in line and ad-hoc to place data in the form and quality necessary for a deeper analysis
• Analytical geo using geolocation resources to allow location-based analysis in addition to simple views of layer maps (eg, space research, location-based grouping, distance, and route calculation)
•  Functionality ready for use for "simple" analysis without coding (for example, forecast, grouping, classification)
• Resources ready to make use cases of advanced analysis without additional tools (for example, a built-in R mechanism and corresponding tools)
• Support for the integration of additional advanced analysis and machine learning structures (such as R, Python, Apache Spark, H20.ai, KNIME, SAS or MATLAB)
• Extensibility and improvement with custom components and features.
• Collaboration between corporate users, analysts and data scientists in the same tool, without additional tools from third parties (for example, ability to work in a team, share analysis with other people, add comments and discussions)

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