Data visualization

Data visualization or data visualisation is the creation and study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information".

Overview
According to Friedman (2008) the "main goal of data visualization is to communicate information clearly and effectively through graphical means. It doesn’t mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key-aspects in a more intuitive way. Yet designers often fail to achieve a balance between form and function, creating gorgeous data visualizations which fail to serve their main purpose — to communicate information".

Indeed, Fernanda Viegas and Martin M. Wattenberg have suggested that an ideal visualization should not only communicate clearly, but stimulate viewer engagement and attention.

Data visualization is closely related to information graphics, information visualization, scientific visualization, and statistical graphics. In the new millennium, data visualization has become an active area of research, teaching and development. According to Post et al. (2002), it has united scientific and information visualization. Brian Willison has demonstrated that data visualization has also been linked to enhancing agile software development and customer engagement.

KPI Library has developed the “Periodic Table of Visualization Methods,” an interactive chart displaying various data visualization methods. It includes six types of data visualization methods: data, information, concept, strategy, metaphor and compound.

Data visualization scope
There are different approaches on the scope of data visualization. One common focus is on information presentation, such as Friedman (2008) presented it. In this way Friendly (2008) presumes two main parts of data visualization: statistical graphics, and thematic cartography. In this line the "Data Visualization: Modern Approaches" (2007) article gives an overview of seven subjects of data visualization: All these subjects are closely related to graphic design and information representation.
 * Mindmaps
 * Displaying news
 * Displaying data
 * Displaying connections
 * Displaying websites
 * Articles & resources
 * Tools and services

On the other hand, from a computer science perspective, Frits H. Post (2002) categorized the field into a number of sub-fields:
 * Visualization algorithms and techniques
 * Volume visualization
 * Information visualization
 * Multiresolution methods
 * Modelling techniques and
 * Interaction techniques and architectures

For different types of visualizations and their connection to infographics, see infographics.

Data acquisition
Data acquisition is the sampling of the real world to generate data that can be manipulated by a computer. Sometimes abbreviated DAQ or DAS, data acquisition typically involves acquisition of signals and waveforms and processing the signals to obtain desired information. The components of data acquisition systems include appropriate sensors that convert any measurement parameter to an electrical signal, which is acquired by data acquisition hardware.

Data analysis
Data analysis is the process of studying and summarizing data with the intent to extract useful information and develop conclusions. Data analysis is closely related to data mining, but data mining tends to focus on larger data sets with less emphasis on making inference, and often uses data that was originally collected for a different purpose. In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis, and inferential statistics (or confirmatory data analysis), where the EDA focuses on discovering new features in the data, and CDA on confirming or falsifying existing hypotheses.

Types of data analysis are:
 * Exploratory data analysis (EDA): an approach to analyzing data for the purpose of formulating hypotheses worth testing, complementing the tools of conventional statistics for testing hypotheses. It was so named by John Tukey.
 * Qualitative data analysis (QDA) or qualitative research is the analysis of non-numerical data, for example words, photographs, observations, etc.

Data governance
Data governance encompasses the people, processes and technology required to create a consistent, enterprise view of an organisation's data in order to:
 * Increase consistency & confidence in decision making
 * Decrease the risk of regulatory fines
 * Improve data security
 * Maximize the income generation potential of data
 * Designate accountability for information quality

Data management
Data management comprises all the academic disciplines related to managing data as a valuable resource. The official definition provided by DAMA is that "Data Resource Management is the development and execution of architectures, policies, practices, and procedures that properly manage the full data lifecycle needs of an enterprise." This definition is fairly broad and encompasses a number of professions that may not have direct technical contact with lower-level aspects of data management, such as relational database management.

Data mining
Data mining is the process of sorting through large amounts of data and picking out relevant information. It is usually used by business intelligence organizations, and financial analysts, but is increasingly being used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods.

It has been described as "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data" and "the science of extracting useful information from large data sets or databases." In relation to enterprise resource planning, according to Monk (2006), data mining is "the statistical and logical analysis of large sets of transaction data, looking for patterns that can aid decision making".

Data transforms
Data transforms is the process of Automation and Transformation, of both real-time and offline data from one format to another. There are standards and protocols that provide the specifications and rules, and it usually occurs in the process pipeline of aggregation or consolidation or interoperability. The primary use cases are in integration systems organizations, and compliance personnels.

Data presentation architecture
Data presentation architecture (DPA) is a skill-set that seeks to identify, locate, manipulate, format and present data in such a way as to optimally communicate meaning and proffer knowledge.

Historically, the term data presentation architecture is attributed to Kelly Lautt: "Data Presentation Architecture (DPA) is a rarely applied skill set critical for the success and value of Business Intelligence. Data presentation architecture weds the science of numbers, data and statistics in discovering valuable information from data and making it usable, relevant and actionable with the arts of data visualization, communications, organizational psychology and change management in order to provide business intelligence solutions with the data scope, delivery timing, format and visualizations that will most effectively support and drive operational, tactical and strategic behaviour toward understood business (or organizational) goals. DPA is neither an IT nor a business skill set but exists as a separate field of expertise. Often confused with data visualization, data presentation architecture is a much broader skill set that includes determining what data on what schedule and in what exact format is to be presented, not just the best way to present data that has already been chosen (which is data visualization). Data visualization skills are one element of DPA."

Objectives
DPA has two main objectives:
 * To use data to provide knowledge in the most effective manner possible (provide relevant, timely and complete data to each audience member in a clear and understandable manner that conveys important meaning, is actionable and can affect understanding, behavior and decisions)
 * To use data to provide knowledge in the most efficient manner possible (minimize noise, complexity, and unnecessary data or detail given each audience's needs and roles)

Scope
With the above objectives in mind, the actual work of data presentation architecture consists of:
 * Defining important meaning (relevant knowledge) that is needed by each audience member in each context
 * Finding the right data (subject area, historical reach, breadth, level of detail, etc.)
 * Determining the required periodicity of data updates (the currency of the data)
 * Determining the right timing for data presentation (when and how often the user needs to see the data)
 * Utilizing appropriate analysis, grouping, visualization, and other presentation formats
 * Creating effective delivery mechanisms for each audience member depending on their role, tasks, locations and access to technology

Related fields
DPA work has some commonalities with several other fields, including:
 * Business analysis in determining business goals, collecting requirements, mapping processes.
 * Solution architecture in determining the optimal detailed solution, including the scope of data to include, given the business goals
 * Business process improvement in that its goal is to improve and streamline actions and decisions in furtherance of business goals
 * Statistical analysis or data analysis in that it creates information and knowledge out of data
 * Data visualization in that it uses well-established theories of visualization to add or highlight meaning or importance in data presentation.
 * Information architecture, but information architecture's focus is on unstructured data and therefore excludes both analysis (in the statistical/data sense) and direct transformation of the actual content (data, for DPA) into new entities and combinations.
 * Graphic or user design: As the term DPA is used, it falls just short of design in that it does not consider such detail as colour palates, styling, branding and other aesthetic concerns, unless these design elements are specifically required or beneficial for communication of meaning, impact, severity or other information of business value. For example:
 * choosing to provide a specific colour in graphical elements that represent data of specific meaning or concern is part of the DPA skill-set
 * choosing locations for various data presentation elements on a presentation page (such as in a company portal, in a report or on a web page) in order to convey hierarchy, priority, importance or a rational progression for the user is part of the DPA skill-set.