Task 1
The contemporary world of business has become increasingly data-driven, and entrepreneurs are now trying to work with data on a daily basis. That is why organizations use data visualization techniques to tell a good story that defines both past and present features that are significant to their performances and to also predict future aspects. An example can be drawn from the graph below, which depicts changes in Arctic ice since 1978.
Source: Murray (2019).
Visual patterns of line charts have been used to justify the data being presented by incorporating a number of stories along the way. With the use of annotations in a line chart, audiences can now be able to understand the conveyed information. However, there are two distinct types of data visualization techniques, particularly exploration and explanation.
Exploratory data visualization techniques
Exploratory data visualization (EDV) techniques utilize qualitative approaches to tell the story ofunknown data. Appropriate exploratory data analysis tools are often used to extract suggestive ideological insights from given data to bring out clear information. Exploratory techniques for data visualization also enhances the complexion of storytelling by defining and redefining significant variables and features that are relevant to an organization. That is why businesses tend to apply EDV techniques to identify anomalous outliers, trends, and other data set features that definecrucial values of their operations. Besides, since data visualization is one of the elements of exploratory data analysis, its research goals rely on graphing techniques to bring out clear understandings of what the data entails. The only thing that businesses are expected to do is to focus on using the right graph/chart for the right data in order to gain insights into the data more quickly. For example, in checking distributional and other assumptions, the researcher can utilize a simple histogram to examine the distribution processes. Here, the exploratory technique will allow the researcher to examine the data with a normality probability plot. A diagonal straight line would suggest that the data is perfectly normal, while any form of deviation from the straight line would suggest non-normality. Such a phenomenon is depicted in the graph.
Source: Yu and Ds 2015
Explanatory data visualization techniques
Explanatory data visualization techniques are used to tell a story of known sets of data. The techniques are often used during data analysis processes as they are part of the presentation phase. They are mostly used to simplify visual data with the use of infographicselements to enhance one’s abilities to see trends and patterns. The infographic elements tend to improve cognition with the use of graphics, and readers can easily follow the interactive story being conveyed by the elements.
A good example of an explanatory data visualization technique can be drawn from the above chart depicting changes in Arctic ice since 1978. Through the chart, it is crucial that the visual patterns of line charts have been used to justify the data being presented by incorporating a number of stories along the way.
Task 2
The expressiveness of a data visualization technique
In visualization, a technique is termed expressive it all relevant data of a dataset can be expressed by the visualization technique. Relevant data implies the visualization expressiveness could only be gauged vis-à-vis a specific user making use of the visual representation to achieve a specific goal. An expressive technique in data storytelling visualization should be not only accurate but also an interactive interface with affordance should be afforded. The criteria of expressiveness identify a graphical language that can illustrate the information that is desired. A language can express a set of facts if the language’s sentence can encode all the set’s facts and also encode only the facts of the set. When an individual desires to know the rightness of a tool being utilized, expressiveness gets the job done. Expressiveness is all about finding relevant data relationships. Principles of expressiveness are utilized in the process of designing, redesigning, and analysis of a display, and when effectively used, the principles should exclusively bring out data links that exist among data elements in the dataset (Siddiquet al. 2016). The principles of expressiveness are abode by in almost all scenarios but not always. In cases where on a line chart, if the horizontal axis’ characteristics are viewed as categorical as opposed to its normal characteristics of being quantitative in nature, the principles could be overlooked. The change in attribute possesses challenges in data interpretation; more often than not, the visuals will be misconstrued. The unordered data will avail misleading information making the audience think the information in the line graph is moving, taking a shift, and the information being observed will be nonexistent in the original datasets (Kimet al. 2016). Useful creations are guaranteed by the use of hefty spaces in the parameter in story illustrations.
Effectiveness of data visualization techniques
The effectiveness of a visualization technique relies on the readily available perception of the visual information. A technique that is capable of conveying visual perception more readily is considered more effective than a lesser one. In storytelling, a more effective technique is the one that captures the most data and presents it in a manner that is easily perceived by the audience. That being said, it goes without saying that the quality and quantity of the dataset determines the effectiveness of a visualization technique. In data visualization story sessions, effectiveness could be achieved in a number of ways. One notable means of augmenting effectiveness, for example, is through appropriate choice of visual representation methodology. Some data tell a more comprehensive story in a pie chart, while others are more effective in line and bar graphs (Evergreen, 2019).
The real value of data visualization lies in representing datasets in an easily understandable manner. Effectiveness entails removing data that are not crucial to the story being told by minimizing consequential elements in the available data reduction. Comparison effectiveness is realized by wisely including, if possible, a zero baseline, using the best visualizer, watching data items placements, and not telling the whole story. When data being visualized are numerous, using different colors to represent different colors could enhance the effectiveness of the visualization (Evergreen, 2019). The audience of the visual display relies on the data labels to gain better insight into the data. Double-checking the labels to ensure all components are labeled and labeled appropriately in a clear and visible font could augment the overall visualization effectiveness
Task 3
Cloud computing refers to an online storage model that provides a variety of integrated computing services such as data storage, data backup, and so forth, which can only be accessed with the use of web servers. Big data, on the other hand, is an information asset characterized by high volume, high velocity, and high-variety of data acquisition, storage, and analysis that require sophisticated forms of information processors. However, even the two mainstream technologies differ greatly; their integration has proven to be of great concern to modern businesses.
The role that cloud computing is playing in big datamanagement
Big data management involves data acquisition, data storage, and data analysis. While cloud computing offers on-demand services on sets of data that have been encrypted in virtual servers, organizations tend to store both structured and unstructured sets of data in the cloud to facilitate data analysis processes. Chan (2018) contended that the cloud storage model could be used by organizations to process and analyze big data more quickly, leading to insightful aspects that can enable businesses to improve their performances. The fact that big data and its exponential growth tends to be of great concern to the modern businesses, the cloud, on the other hand, provides an environment where the large sets of data can be stored on multiple virtual servers that can easily be accessed by the companies (Zanoonet al. 2017). Furthermore, since big data projects must be structured with immense resources that require capital expenditure investments, the cloud storage model has enabled companies to shift from capital expenditure investment expenses to operational expenditure since datasets are stored in virtual servers and not on specified servers. With the use of cloud computing services, employees can also be in a position to streamline data sharing since it allows self-synchronization processes.
Impacts of Cloud Computing on Data Visualization
Cloud computing and its distributed platforms tend to have an impact on the open (big) data visualization. Under service-oriented architecture, the cloud serves as a data storage model that delivers solutions to large-scale and complex computing problems by tackling a variety of issues that are related to large amounts of data. In this sense, companies can utilize the on-demand services supported by the cloud to analyze data with the use of the extract, transform, and load (ETL) tools (Lnenickaet al. 2015).
Task 4
The impact that the Internet of Things is having on big data management in term of the opportunities and challenges
In the modern world of technological advancement, IoT and Big Data are the two mainstream technologies that are of great value to modern businesses. While IoT supports data transfer processes over a network with the exception of human-to-human or human-to-computer interconnectedness, big data, on the other hand, contains large sets of data that can be analyzed to get insights into the current trends. IoT serves as a major source for the large sets of data that are yet to be analyzed. Big data analytic trends to examine, analyze, and correlates IoTgenerated data drawn from the connected devices in order to generate proper initiatives that can improve decision-making. However, since the unstructured data in IoT are obtained through the internet, big data for IoT requires lightning-fast analysis in order to make immediate decisions, which is, however, a challenge to organizations.
The impact that the Internet of Things is having on data visualization in term of the opportunities and challenges
The internet of things interconnects a variety of devices, technologies, networks, including human resources, with IoT-based applications to achieve a common goal of automation. IoT devices tend to collect data across the web servers and store them in the cloud. The stored data, when subjected to data analytic through data visualization processes, can give out crucial information that can be used to improve the algorithms of artificial intelligence. In this sense, businesses can be able to make proper decisions on every aspect of the data collected by developing a holistic view of the analyzed data (Towers-Clark, 2019). However, even though IoT tends to improve AI interactions, studies have indicated that security and privacy issues have arisen in the wake of IoT through digital interconnections. Some organizations do not have strong security protocols in place, and pieces of data from the interconnected devices can easily be analyzed with the use of data visualization techniques that can result in a data breach (Al‐Turjmanet al. 2019).
References
Al‐Turjman, F., Zahmatkesh, H., and Shahroze, R., 2019. An overview of security and privacy in
smart cities’ IoT communications. Transactions on Emerging Telecommunications
Technologies, p.e3677.
Chan, M., 2018.Big Data in the Cloud: Why Cloud Computing is the Answer to Your Big Data
Initiatives.Thorn Technologies.https://www.thorntech.com/2018/09/big-data-in-the-
cloud/
Evergreen, S.D., 2019.Effective data visualization: The right chart for the right data. Sage
Publications.
Kim, N.W., Schweickart, E., Liu, Z., Dontcheva, M., Li, W., Popovic, J. and Pfister, H., 2016.
Data-driven guides: Supporting expressive design for information graphics. IEEE
transactions on visualization and computer graphics, 23(1), pp.491-500.
Lnenicka, M., and Komarkova, J., 2015, November. The Impact of Cloud Computing and Open
(Big) Data on the Enterprise Architecture Framework. In Proceedings of the 26th
International Business Information Management Association Conference. Norristown:
International Business Information Management Association-IBIMA (pp. 1679-1683).
Murray, E., 2019. How Do You Tell A Story With Data Visualization? Forbes.
Siddiqui, T., Kim, A., Lee, J., Karahalios, K., and Parameswaran, A., 2016. Effortless data
exploration with zenvisage: an expressive and interactive visual analytics system. arXiv
preprint arXiv:1604.03583.
Towers-Clark, C., 2019.Big Data, IoTAnd AI, Part One: Three Sides Of The Same Coin.
Forbes. https://www.forbes.com/sites/charlestowersclark/2019/02/15/big-data-iot-and-ai-part-one-three-sides-of-the-same-coin/#77d3592769da
Yu, C.H., and Ds, P., 2015. Exploratory data analysis and data visualization.
Zanoon, N., Al-Haj, A., and Khwaldeh, S.M., 2017. Cloud computing and big data is there a
relation between the two: a study. International Journal of Applied Engineering
Research, 12(17), pp.6970-6982
BIG DATA ANALYTICS
By (Student’s Name)
Class (Course)
Professor
Name of the School (University)
City and State
Date
Task 1
The contemporary world of business has become increasingly data-driven, and entrepreneurs are now trying to work with data on a daily basis. That is why organizations use data visualization techniques to tell a good story that defines both past and present features that are significant to their performances and to also predict future aspects. An example can be drawn from the graph below, which depicts changes in Arctic ice since 1978.
Source: Murray (2019).
Visual patterns of line charts have been used to justify the data being presented by incorporating a number of stories along the way. With the use of annotations in a line chart, audiences can now be able to understand the conveyed information. However, there are two distinct types of data visualization techniques, particularly exploration and explanation.
Exploratory data visualization techniques
Exploratory data visualization (EDV) techniques utilize qualitative approaches to tell the story ofunknown data. Appropriate exploratory data analysis tools are often used to extract suggestive ideological insights from given data to bring out clear information. Exploratory techniques for data visualization also enhances the complexion of storytelling by defining and redefining significant variables and features that are relevant to an organization. That is why businesses tend to apply EDV techniques to identify anomalous outliers, trends, and other data set features that definecrucial values of their operations. Besides, since data visualization is one of the elements of exploratory data analysis, its research goals rely on graphing techniques to bring out clear understandings of what the data entails. The only thing that businesses are expected to do is to focus on using the right graph/chart for the right data in order to gain insights into the data more quickly. For example, in checking distributional and other assumptions, the researcher can utilize a simple histogram to examine the distribution processes. Here, the exploratory technique will allow the researcher to examine the data with a normality probability plot. A diagonal straight line would suggest that the data is perfectly normal, while any form of deviation from the straight line would suggest non-normality. Such a phenomenon is depicted in the graph.
Source: Yu and Ds 2015
Explanatory data visualization techniques
Explanatory data visualization techniques are used to tell a story of known sets of data. The techniques are often used during data analysis processes as they are part of the presentation phase. They are mostly used to simplify visual data with the use of infographicselements to enhance one’s abilities to see trends and patterns. The infographic elements tend to improve cognition with the use of graphics, and readers can easily follow the interactive story being conveyed by the elements.
A good example of an explanatory data visualization technique can be drawn from the above chart depicting changes in Arctic ice since 1978. Through the chart, it is crucial that the visual patterns of line charts have been used to justify the data being presented by incorporating a number of stories along the way.
Task 2
The expressiveness of a data visualization technique
In visualization, a technique is termed expressive it all relevant data of a dataset can be expressed by the visualization technique. Relevant data implies the visualization expressiveness could only be gauged vis-à-vis a specific user making use of the visual representation to achieve a specific goal. An expressive technique in data storytelling visualization should be not only accurate but also an interactive interface with affordance should be afforded. The criteria of expressiveness identify a graphical language that can illustrate the information that is desired. A language can express a set of facts if the language’s sentence can encode all the set’s facts and also encode only the facts of the set. When an individual desires to know the rightness of a tool being utilized, expressiveness gets the job done. Expressiveness is all about finding relevant data relationships. Principles of expressiveness are utilized in the process of designing, redesigning, and analysis of a display, and when effectively used, the principles should exclusively bring out data links that exist among data elements in the dataset (Siddiquet al. 2016). The principles of expressiveness are abode by in almost all scenarios but not always. In cases where on a line chart, if the horizontal axis’ characteristics are viewed as categorical as opposed to its normal characteristics of being quantitative in nature, the principles could be overlooked. The change in attribute possesses challenges in data interpretation; more often than not, the visuals will be misconstrued. The unordered data will avail misleading information making the audience think the information in the line graph is moving, taking a shift, and the information being observed will be nonexistent in the original datasets (Kimet al. 2016). Useful creations are guaranteed by the use of hefty spaces in the parameter in story illustrations.
Effectiveness of data visualization techniques
The effectiveness of a visualization technique relies on the readily available perception of the visual information. A technique that is capable of conveying visual perception more readily is considered more effective than a lesser one. In storytelling, a more effective technique is the one that captures the most data and presents it in a manner that is easily perceived by the audience. That being said, it goes without saying that the quality and quantity of the dataset determines the effectiveness of a visualization technique. In data visualization story sessions, effectiveness could be achieved in a number of ways. One notable means of augmenting effectiveness, for example, is through appropriate choice of visual representation methodology. Some data tell a more comprehensive story in a pie chart, while others are more effective in line and bar graphs (Evergreen, 2019).
The real value of data visualization lies in representing datasets in an easily understandable manner. Effectiveness entails removing data that are not crucial to the story being told by minimizing consequential elements in the available data reduction. Comparison effectiveness is realized by wisely including, if possible, a zero baseline, using the best visualizer, watching data items placements, and not telling the whole story. When data being visualized are numerous, using different colors to represent different colors could enhance the effectiveness of the visualization (Evergreen, 2019). The audience of the visual display relies on the data labels to gain better insight into the data. Double-checking the labels to ensure all components are labeled and labeled appropriately in a clear and visible font could augment the overall visualization effectiveness
Task 3
Cloud computing refers to an online storage model that provides a variety of integrated computing services such as data storage, data backup, and so forth, which can only be accessed with the use of web servers. Big data, on the other hand, is an information asset characterized by high volume, high velocity, and high-variety of data acquisition, storage, and analysis that require sophisticated forms of information processors. However, even the two mainstream technologies differ greatly; their integration has proven to be of great concern to modern businesses.
The role that cloud computing is playing in big datamanagement
Big data management involves data acquisition, data storage, and data analysis. While cloud computing offers on-demand services on sets of data that have been encrypted in virtual servers, organizations tend to store both structured and unstructured sets of data in the cloud to facilitate data analysis processes. Chan (2018) contended that the cloud storage model could be used by organizations to process and analyze big data more quickly, leading to insightful aspects that can enable businesses to improve their performances. The fact that big data and its exponential growth tends to be of great concern to the modern businesses, the cloud, on the other hand, provides an environment where the large sets of data can be stored on multiple virtual servers that can easily be accessed by the companies (Zanoonet al. 2017). Furthermore, since big data projects must be structured with immense resources that require capital expenditure investments, the cloud storage model has enabled companies to shift from capital expenditure investment expenses to operational expenditure since datasets are stored in virtual servers and not on specified servers. With the use of cloud computing services, employees can also be in a position to streamline data sharing since it allows self-synchronization processes.
Impacts of Cloud Computing on Data Visualization
Cloud computing and its distributed platforms tend to have an impact on the open (big) data visualization. Under service-oriented architecture, the cloud serves as a data storage model that delivers solutions to large-scale and complex computing problems by tackling a variety of issues that are related to large amounts of data. In this sense, companies can utilize the on-demand services supported by the cloud to analyze data with the use of the extract, transform, and load (ETL) tools (Lnenickaet al. 2015).
Task 4
The impact that the Internet of Things is having on big data management in term of the opportunities and challenges
In the modern world of technological advancement, IoT and Big Data are the two mainstream technologies that are of great value to modern businesses. While IoT supports data transfer processes over a network with the exception of human-to-human or human-to-computer interconnectedness, big data, on the other hand, contains large sets of data that can be analyzed to get insights into the current trends. IoT serves as a major source for the large sets of data that are yet to be analyzed. Big data analytic trends to examine, analyze, and correlates IoTgenerated data drawn from the connected devices in order to generate proper initiatives that can improve decision-making. However, since the unstructured data in IoT are obtained through the internet, big data for IoT requires lightning-fast analysis in order to make immediate decisions, which is, however, a challenge to organizations.
The impact that the Internet of Things is having on data visualization in term of the opportunities and challenges
The internet of things interconnects a variety of devices, technologies, networks, including human resources, with IoT-based applications to achieve a common goal of automation. IoT devices tend to collect data across the web servers and store them in the cloud. The stored data, when subjected to data analytic through data visualization processes, can give out crucial information that can be used to improve the algorithms of artificial intelligence. In this sense, businesses can be able to make proper decisions on every aspect of the data collected by developing a holistic view of the analyzed data (Towers-Clark, 2019). However, even though IoT tends to improve AI interactions, studies have indicated that security and privacy issues have arisen in the wake of IoT through digital interconnections. Some organizations do not have strong security protocols in place, and pieces of data from the interconnected devices can easily be analyzed with the use of data visualization techniques that can result in a data breach (Al‐Turjmanet al. 2019).
References
Al‐Turjman, F., Zahmatkesh, H., and Shahroze, R., 2019. An overview of security and privacy in
smart cities’ IoT communications. Transactions on Emerging Telecommunications
Technologies, p.e3677.
Chan, M., 2018.Big Data in the Cloud: Why Cloud Computing is the Answer to Your Big Data
Initiatives.Thorn Technologies.https://www.thorntech.com/2018/09/big-data-in-the-
cloud/
Evergreen, S.D., 2019.Effective data visualization: The right chart for the right data. Sage
Publications.
Kim, N.W., Schweickart, E., Liu, Z., Dontcheva, M., Li, W., Popovic, J. and Pfister, H., 2016.
Data-driven guides: Supporting expressive design for information graphics. IEEE
transactions on visualization and computer graphics, 23(1), pp.491-500.
Lnenicka, M., and Komarkova, J., 2015, November. The Impact of Cloud Computing and Open
(Big) Data on the Enterprise Architecture Framework. In Proceedings of the 26th
International Business Information Management Association Conference. Norristown:
International Business Information Management Association-IBIMA (pp. 1679-1683).
Murray, E., 2019. How Do You Tell A Story With Data Visualization? Forbes.
Siddiqui, T., Kim, A., Lee, J., Karahalios, K., and Parameswaran, A., 2016. Effortless data
exploration with zenvisage: an expressive and interactive visual analytics system. arXiv
preprint arXiv:1604.03583.
Towers-Clark, C., 2019.Big Data, IoTAnd AI, Part One: Three Sides Of The Same Coin.
Forbes. https://www.forbes.com/sites/charlestowersclark/2019/02/15/big-data-iot-and-ai-part-one-three-sides-of-the-same-coin/#77d3592769da
Yu, C.H., and Ds, P., 2015. Exploratory data analysis and data visualization.
Zanoon, N., Al-Haj, A., and Khwaldeh, S.M., 2017. Cloud computing and big data is there a
relation between the two: a study. International Journal of Applied Engineering
Research, 12(17), pp.6970-6982
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