Data Visualization Essay Example

Published: 2022-07-04
Data Visualization Essay Example
Type of paper:  Research paper
Categories:  Data analysis
Pages: 6
Wordcount: 1551 words
13 min read

Data visualization refers to data representation in graphical or pictorial formats to enable decision-makers to view the visually represented analytics that would then inform their decisions henceforth. The visual representation allows the decision makers to grasp complex theories or identify new configurations. Further, shared visualization can take the idea a step beyond, using machinery to dig deeper into graphs and charts for additional details and altering how available data is processed (Thorvaldsdottir, 2013).

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For centuries, the use of data visualization has been in play, from the use of graphs and maps from the 17th century to the use of pie charts in the 19th century. Technology, however, lit up the concept of data visualization, with computers making it likely to manage a large amount of data at very quick speed. Currently, data visualization has become a fast evolving mix of art and science, specific to modify the organizational landscape. With big data, there are opportunities for investment, for example, improving customer relations, identifying improvement areas in the firm, monitoring customer behavior, identifying which products to trade, and predicting sales volumes among other uses (Thorvaldsdottir, 2013).

Nokia Corporation


Nokia Corporation is one of the top manufacturers of mobile phones, supplying close to 130 nations worldwide. The Company is segmented into four business categories: Multimedia, Mobile phones, Networks and Enterprise solutions. The multimedia group sells cable television set-top boxes, mobile gaming devices, and home satellite systems. The mobile phones group promotes wireless data and voice merchandise in corporate and consumer markets (People &Planet Report 2014, 2014). The enterprise group creates the wireless systems used in the business sector while the networks unit sells transmission gear and wireless regulators.

Use of Data Visualization

Through analysis of big data, Nokia's responsibility plan concentrates on sections in which they can create the largest transformation to the environment and the society through innovation, partnerships and operations. The Corporation's main areas involve advancing lives using innovation, respect for consumers, pushing for change and protecting the environment (People &Planet Report 2014, 2014).

In terms of improving lives through technology, the Corporation is committed to innovation as a way to use technology for a more suitable future. The responsibility strategies focus on using technology to help improve consumer lives by helping to reduce global carbon emissions among other ventures (People &Planet Report 2014, 2014).

Nokia Corporations is also dedicated to operating their transactions in line with globally recognized responsible and ethical corporate customs by promoting a custom in which workers feel certain to raise interests. In the quest to achieve the highest degree of ethical conduct, the corporation's accountability strategy aims at tackling matters related to corporate ethics, potential misuse of technology and privacy. Additionally, the corporation works with other partners to improve labor conditions for their employees.

In terms of protecting the environment, the corporation aims at minimizing environmental impacts from manufacturing, distributing and operating Nokia products. Additionally, the company prioritizes the efficient use of energy. It helps the communication operators to direct energy use in their systems.

Through the analysis of large data by the Corporation, Nokia is able then to identify their key responsibility topics (Basole, 2015). In 2014 the Nokia increased their focus on materiality by revising their materiality analysis that reforms the basis of the company's target setting and responsibility strategy. Revision of the materiality assessment tools included a look at factors such as the technological capacity of humans in a programmable environment, increase of significance of discretion, requirement for morals and transparency, the significance of inviting and keeping talents, climate variation and the need for improved efficiency of resources (Basole, 2015).

Through the analysis of its data, on the human capability of innovation in a programmable world, Nokia manages to realize how the rapid change in technology becomes inevitable in changing how people live. The data informs the Corporation about the Billions of consumers already connected through its networks. In the new phase of connection, therefore, Nokia looks at the connection of things as well as people. Their data suggest that the advantages that will come with the programmable world include proper use of limited reserves, reduced environmental damage, upgraded human welfare and bigger proficiency (People &Planet Report 2014, 2014).

Figure 1: Key topics for Nokia Corporation

The above figure shows the key topics for Nokia Corporation and their impact on suitable development, the interest of stakeholders and the business of the Company.

Figure 2: Nokia Corporation's sales across the globe

Good Practice of Using Data Visualization by Nokia

Using consumer data by the company, Nokia is able, therefore, to track the sales of their products all over the world. The above map is a visualization of the distribution of Nokia products all over the world.

With data visualization, Nokia is, therefore, able to prioritize its operations across the globe in relation to the opportunities and risks of the company. For instance, the company looks to improve lives with innovation and efficiency of network, where there are brand and revenue opportunities in inventing technologies that upgrade lives and growing networks that are energy efficient (People &Planet Report 2014, 2014). The company also wants to attract and retain talent, with talent attraction ability having an influence on how fit they are capable of managing the company's revenues and cost-related opportunities and risks. Further, the company focuses on discretion and business morals, which are status and control-driven risk areas with opportunities as well. The figure below, therefore, aims at informing the corporation about the relationship between its opportunity ventures against the risk it poses to the Company.

Figure 3: Opportunity vs. risk chart

Such an analysis from large company data can, therefore, lead the company to pursue the best opportunities with the least possible risk, thus growing the Company further.

Motorola Corporation


Motorola Inc., an electronic telecommunication innovator is one of the leading manufacturers and designers of cellular and cordless phones, cable modems, two-way radios, pagers, and broadband set-top boxes, among other communication commodities and schemes. Motorola is also a leading production company of embedded processors through its Semiconductor Products sector, with prominence on high-growth areas like transportation, wireless communication, and internet networking (Woodie, 2014). Additionally, the Corporation's Integrated Electronics System manufactures and designs various electrical products and schemes for computer, automotive, navigation, transportation, industrial, and lighting markets. Motorola's emphasis on quality has gained recognition for the Corporation. Nearly two thirds of Motorola's revenue is produced outside the United States.

Use of Data Visualization

Motorola has been using big data analysis to better their services over the years. For instance, after the launch of the RAZR M smartphone, rumors spread about the flaws of the product. Some customers claimed that users were limited from using Wi-Fi and 4G cellular networks (Woodie, 2014). Thanks to the customers' sentiments, the Corporation managed to correct the misinformation before it could damage the brand even as the corporation was still prototyping.

Figure 4: Motorola's top products 5: Motorola's Sales across the globe

Initially, manufacturers conducted surveys to know their customers' thought about the products. However, conducting surveys can always miss out on what the customers say in the wild. Besides, the surveys can be expensive to conduct (Basole, 2015). Motorola teamed up with Datascope Analytics to help build Motorola's sentiment analysis program on Mobility Service and Repairs. Datascope Analytics sought to extract and distill general thoughts that consumers expressed towards Motorola's wireless devices based on what they said in chat rooms forums social media, news sites and comment section on the web.

Best Practice of Using Data Visualization by Motorola

According to the Business Operations and Strategy Manager at Motorola, teaming up with Datascope Analytics aimed at repairing failures minimizing services and proactively addressing consumer needs following the launch of products (Woodie, 2014).

Datascope Analytics' task, therefore, involved sieving out information that seemed irrelevant to Motorola using a text parsing engine. The software system then would classify the texts according to the feature of Motorola device that the owner of the text was talking about; it could be the phone's camera, network connectivity, PenTile display, or any other feature that seemed to spark the consumer's fancy or wrath (Woodie, 2014).

Using the word cloud, Datascope Analytics could summarize the words or phrases that popped up most often from the data. Additionally, Datascope made further tweaks to ensure that the main thread of people's sentiments could be brought out to their attention (Woodie, 2014).

Figure 6: Motorola's Profit matrix


In conclusion, data visualization techniques have become inevitably important for Major Corporations. These Corporations have switched from using surveys to simply analyze big data from their consumers to predict events that could affect their profitability. With big data analysis, Corporations can save reduce expenditure that could have been channeled to conducting surveys. Instead, they could channel part of the money to big data analysis as the results would still be the same, if not better, at a lower cost. The results of the analysis, therefore, has proved more than profitable, with companies increasing their revenues hugely.


Basole, R. C. (2015). Understanding business ecosystem dynamics: A data-driven approach. ACM Transactions on Management Information Systems (TMIS), 6(2),, 6.

(2014). People &Planet Report 2014. Nokia. Retrieved from

Thorvaldsdottir, H. R. (2013). Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Briefings in bioinformatics, 14(2),, 178-192.

Woodie, A. (2014). How Motorola Uses Big Data Analytics to Improve Its Smartphones. Datanami. Retrieved from

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