Question

Part 1

Drawing on your experience in viewing time series visualizations, which of graph types do you find most useful or easiest to understand? (Provide a web link to a static time series visualization that you think is well constructed.)

How can the addition of an interactive component (such as a linked time interval, time-interval slider, or time-point slider) add to a time series visualization? (Provide a web link to an interactive time series visualization that you think is well constructed.)

Part 2

Describe special issues that may arise in working with data from the Internet of Things (IoT) or machine-to-machine (M2M) technologies.

How do you think data visualization might serve the needs of real-time analytics?

Answer 1

Solution 1: With the growth in the structured and the unstructured data, the organizations ate trying to leverage the presence of the data in this much amount. This data can be used for drawing meaningful insights to make better business decisions. The data analysts are responsible for mining the important information from the data using the appropriate algorithms and the statistical methods. But these techniques cannot be understood by the executives and hence the analysts are supposed to prepare the visualizations of the data so that the people at the executive level can understand the trends in the data and can make the important business decisions. The visulaization techniques that are most popularly used are Bar Graph, Line Graph and the Pie Charts. These are the most common type of graphs and now these days some of their modifications are being used for the data visualization. Yes, to make the graph highly interactive, the features such as slider, drill down, etc can be employed within the same graph to provide even more information regarding the data such as the information regarding a specific time-interval related with a particular attribute.

Solution 2: The main issue that is faced while working with data from the IoT and the M2M technologies is the data that is collected from the IoT. The data is collected through the IoT sensors and is highly unstructured which means that different kinds of data such as the temperature, rotation per minute, current, voltage, etc. is calculated by these sensors and obviously all of these data have different units and scales and hence while the analysts perform the analysis on this data, they have to do a lot of data cleaning and also have to predict the missing data correctly. This is a crucial task which needs to be done very carefully. Yes, the data visualization can play a vital role in changing the course of real time data analytics because it helps the analysts to understand the real-time patterns and the trends in the data. This will help them in building better analytics solutions for their organization which in turn would help the organization to taake better business decisions.

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