What is data visualization
Data visualization is the representation of data in a visual context so that people understand the data better. Some of the patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software. Today’s data visualization tools are more than just charts and graphs used in Microsoft Excel spreadsheets, displaying data in more sophisticated ways like infographics, dials and gauges, geographic maps, spark lines, heat maps, and detailed bar, pie and fever charts. The images can also include interactive capabilities thereby enabling users to manipulate them or dig deeper into the data for analysis. Alerts can be setup to inform the users when
…show more content…
The two leading vendors in the BI space, Tableau and Qlik have emphasized visualization which caused other vendors to move towards visual approach in their software. Virtually all BI softwares has strong data visualization functionalities. Data visualization tools have played an important role in democratizing data and analytics and making data-driven insights available to workers throughout an organization. They are easier to operate than traditional statistical analysis software, leading to a rise in data visualization tools.
Data visualization software also plays an important role in big data and advanced analytics projects. As businesses accumulated massive troves of data during the early years of the big data trend, they needed a way to quickly and easily get an overview of their data. Visualization tools were the solution. When a data scientist is writing advanced predictive analytics or machine learning algorithms, it is necessary to visualize the outputs and monitor results to ensure that models are performing as intended. This is because visualizations of complex algorithms are generally easier to interpret than numerical
van Wijk, J.J., "Views on Visualization," Visualization and Computer Graphics, IEEE Transactions on , vol.12, no.4, pp.421,432, July-Aug. 2006. Retrieved from http://ieeexplore.ieee.org.uproxy.library.dc.uoit.ca/stamp/stamp.jsp?tp=&arnumber=1634309&isnumber=34266
The proliferation of graphic scores emerging in Europe and America from the mid-1950s has had a profound impact on musical thought, broadening links between performers and composers, audiences and art forms. Exploration of notational methods based on graphics flourished rapidly and diversely during the fifties and sixties, primarily as a trend amongst young radicals. So many composers producing scores of this kind used a personal vocabulary of symbols – often creating different notation systems for each work – that the effectiveness of their approaches in realising a sonic concept can be assessed only on a case-by-case basis. But the significance of early graphic scores does not depend entirely on how they sound; rather it lies in their capacity to accommodate or even to generate new forms, techniques and mediums, and to challenge notions of what constitutes a musical composition. In addition, these works demonstrate that notation can extend beyond instructional functionality to allow for prominent interpretive and aleatoric elements, and can harbour an intrinsic aesthetic value of its own, apparent before a single note is sounded.
The first sets of the visual analysis are on drunk driving and drug use. All of these pictures relate to drunk driving and drug use and why it is bad for us to drive under the influence of alcohol or drugs. In the first visual set, there are drunk driving and drug use advertisement pictures that are more effective than the others.
In photography it is common for images to have similar qualities and at the same time, have qualities that oppose each other. In order to show that two photographs can be similar yet very different, the two images I chose were Chariots of Fire by Adam Bartos and The Open Door by William Henry Fox Talbot. These two images are not only similar and different in regards to their formal elements and composition but the artists who created them are focused on the same goals of their photography. These two photographers grew up differently had had two different interests. I felt that these two images were the best images to compare and contrast. Without learning formal elements such as light, color, depth and balance, these two images would look
Thirdly, when using data visualization, unrelated information can be filtered and help people concentrate on the useful information.
One thing to remember when using any decision-making tool is to keep the end in mind. In other words when working through a process, visualizing the end result is Helpful. We must also remember that in every decision-making process there are a multitude of tools to help us along the way. No tool is really better then another, in fact most are made to compliment each other not as a soul solution to the process at hand.
Now days, companies are searching for new ways of gathering data so that they can get useful data in order to make well informed decisions regarding the market they are operating in. Google analytics is considered one of the best tools offers extensive amount of data to business owners for free. However, the success of business is highly depended on how well they can arrange data and customize their collected data corresponded to their business priorities. Google analytics provides beneficial information for companies regardless of their extent of operation.
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
The only problem is, if you look at infographics, they look complicated as if not all people can do it. Of course, if you can't you have to hire someone to do it for you. However, the thing is you can't just give money to someone and expect the results are always what you want it to be. You'll have to spend a lot of money to to get what you want.
This allows statistics to be used to recognize trends and possible causal factors.
First of all, business intelligence analysis requires the capturing of information and storing in a single location for effective data analysis. Currently, data analysis is supported by transactional systems, business specific data marts, and other ad-hoc processes. Information is distributed making it difficult and time-consuming to access. Business teams have adapted to this environment by creating user maintained databases and manual “work-arounds” to support new types of reporting and analysis. This has resulted in inconsistent data, redundant data storage, significant resource use for maintenance, and inefficient response to changing business needs.
Friedman, Uri. “Big Data: A Short History.” foreignpolicy.com. Foreign Policy, 8 Oct. 2012. Web. 16 Mar. 2014.
Big data will then be defined as large collections of complex data which can either be structured or unstructured. Big data is difficult to notate and process due to its size and raw nature. The nature of this data makes it important for analyses of information or business functions and it creates value. According to Manyika, Chui et al. (2011: 1), “Big data is not defined by its capacity in terms of terabytes but it’s assumed that as technology progresses, the size of datasets that are considered as big data will increase”.
Business intelligence, or BI, is an umbrella term that refers to a variety of software applications used to analyze an organization’s raw data. BI as a discipline is made up of several related activities, including data mining, online analytical processing, querying and reporting. 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.
Last year, my all-out efforts to explore ‘data analytics’ were abruptly interrupted as a dream, predominantly, due to a life threatening surgery. On my way to a healthy recovery,