Think about the data mining process using the CRISP-DM model. c. Why do the earl
ID: 3906496 • Letter: T
Question
Think about the data mining process using the CRISP-DM model.
c. Why do the earliest phases take so long to accomplish?
d. If an organization uses SEMMA instead of CRISP-DM, what do they supposedly already know?
What is the difference between information visualization and visual analytics? Why should storytelling be part of reporting and data visualization?
What are the different architectures that organizations have to choose from when implementing a data warehouse? Give a basic description of each. Which one is considered the best & why?
Explanation / Answer
What is the difference between information visualization and visual analytics? Why should storytelling be part of reporting and data visualization?
Answer:
difference between information visualization and visual analytics:
Information visualization is aimed at answering "what happened" and "what is happening" and closely associated with business intelligence, visual analytics is aimed at answering " why is it happening," "what is more likely to happen," and is usually associated with business analytics
information visualization:
Data visualization, quite simply, is the art of placing data in a visual context. The aim of data visualization is to identify trends, patterns, and contexts that usually go unrecognized in text-based data. Data visualization tools help in representing data beyond the typical spreadsheets, charts, and graphs and display it in more sophisticated formats using infographics, maps, detailed bars, pie and fever charts, sparklines and heat maps and communicate relationships between data values. The images used in data visualizations can also have interactive capabilities which allow the users to manipulate data for query and analysis.
The nature of data visualization makes it both an art and a science and is viewed as a branch of descriptive statistics. It is also viewed as a grounded theory development tools by many. Scientists, Martin M. Wattenberg and Fernanda Viegas, leaders of Google’s ‘Big Picture’ project and pioneers in data visualization, have suggested in their observations in ‘How to Make Data Look Sexy’ that ideal data visualization should “not only communicate clearly but stimulate viewer engagement and attention”.
Subjects that are close to graphic design and information representation such as displaying connections and data, displaying news and websites, mind maps, tools, and services, navigate large information spaces lend themselves well to data visualization using categorical and quantitative data and make it easier to identify new patterns and understand difficult concepts. Data visualization aims to change how analysts work with data and it helps them respond to issues faster
Visual analytics:
Visual analytics, too, works towards representing data in an easily understandable format but combines automated analysis techniques with interactive visualizations. This helps in the easier understanding of complex data and facilitates reasoning and decision-making based on large and complex data sets.
While there is a certain amount of overlapping between data and information visualization and visual analytics as they both represent data in visual interfaces, Visual Analytics couples interactive visual representations and analytical processes such as data mining techniques, statistical evaluation etc. so that complex activities of reasoning, planning, assessment support and decision making can be performed easily using coordinated graph visualizations, NFlowVis, etc. Data mining, data management, spatiotemporal data analysis and human perception and cognition form the basis of visual analytics.
Visual analytics tools help in creating striking interactive reports and dashboards in an easy to share format. Most tools also allow the users to visually explore all relevant data, identify critical business drivers and apply filters and manipulate the data as per their requirements to arrive at important business conclusions. The increasing adoption of new technologies such as mobile business intelligence and location intelligence software also increases the opportunities for applying visual analytics and improve actionability of the insights.
Data visualization and visual analytics definitely are not the same thing. At the same time, they are two parts of the same coin that aim to make data more understandable and more effective and hence more usable and make good use of the sea of data at our disposal
Why should storytelling be part of reporting and data visualization?
Crafting story elements helps define characters, understand the challenge, identify the hurdles, and crystallize the outcome or decision question. By finding the real stories in your data and following the best practices, you can get people to focus on your message - and thus on what's important
Related Questions
drjack9650@gmail.com
Navigate
Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.