Introduction

Most organizations rely on the data that they obtain from services and clients to improve service delivery and become competitive. As a result, many companies opt to track essential processes that affect them such as sales advancements, customer preferences and complain, and production levels. However, the firms have to utilize efficient software such as data visualization tools to track daily operations successfully. Likewise, the tools enable them to work with bright illustrations and diagrams to come up with comprehensive analyses. Indeed, the software allows companies to identify weak points and improve on them.

Data visualization is a technique that helps organizations to analyze and interpret data efficiently. It determines the influence of data on companies’ plan because it helps them to develop viable strategies, extract useful insights, and improve on statistics assets. In the last decade, companies have used several data visualization tools, namely facts presentation, exhaustive cataloging of visualization, data profiling, and consistent methodology depending on the audience. However, users willing to acquire the software should ensure that it has primary features such as streamlined software interfacing, capacity to track developments, the ability to select dynamic graphs and images, and maintaining high-security levels. Undoubtedly, companies have a range of data visualization solutions that they could use to enhance performance.

Sisense

Sisense is a sophisticated program that supports firms to envisage essential information about performance to make improved and more intelligent decisions. It incorporates rigorous analysis software with exhaustive data visualization to obtain possible results. Similarly, the program allows companies to use a drag and drop user interface to generate graphical illustrations and dashboards. Likewise, it will enable them to use the interactive panels to share the diagrams with consumers, coworkers, and commercial partners (Helfman & Goldberg, 2016). Sisense has gained popularity because it is user-friendly, fast, and comprehensive. Likewise, the software gives value for money because it has some advantages, such as a far-reaching functionality without any hidden charges, integrates well with other data software, fair pricing policy, real-time data tracking, and efficient technical support. Consequently, the software assists organizations to make informed decisions as they save money.

Zoho Analytics

The data interpretation platform enables users to analyze data effectively and make appropriate choices at work. It uses refined applications such as tabular view mechanisms, Key performance indicators (KPI) widgets, and pivot tables to make informed decisions (Runlker, 2016). The tool is collaborative because it enables firms to work together with business partners to generate reports and solve pertinent issues. Likewise, it allows users to attach the statements on any blogs, websites, and applications. Zoho Analytics is one of the most secure tools because its developers use sophisticated system securities such as encrypted connections to protect its privacy. Nevertheless, the software is helpful because it has a highly protected online interface, easy to upload data, accessible dashboard, and fair price packages.

Tableau

It is an advanced business scheme that uses new interpretation tactics to interpret a wide range of data successfully. It also allows data visualization to work fast because it uses dashboards and worksheets to convert raw data into an understandable form (Schulz et al., 2016). Likewise, Tableau incorporates data collaboration, information blending, and real-time analysis to allow its users to make dashboards easily. The tool has a simple interface that enables even non-technical professionals to use it to interpret data and make viable business choices. Tableau consists of five key sections namely, desktop, online, public, server, and reader. Therefore, the software is helpful because it can extract data from any platform and express it the simplest way possible.

Domo

Domo is a business intelligence tool that incorporates many statistical sources including social media, databases, spreadsheets, and cloud-based software results. It is suitable for all corporations that use mobile devices, tablets, Mac, and Windows platforms to operate. Likewise, the tool supports sparklines, trend meters, and widgets that help users to make simple but comprehensive dashboards. The tool is proficient because it has software assimilation programs like Google Analytics that allow it to collect data successfully (Schulz et al., 2016). Many companies use the software because it uses exhaustive dashboards to display data from a wide range of departments and progress metrics. Vendors use a reasonable annual pricing system that depends on the number of users accessing the tool. Consequently, Domo is helpful because it has social sharing features such as data sharing that are not common in other software.

AT&T is one of the companies that utilize data visualization techniques in daily operations. The media firm uses entertainment and communication to motivate human development. Likewise, it has unique services that help to improve client experiences such as fast networks, premium content, effective customer relationships, and advanced advertising technology (Rivera, Roman, & Schaefer, 2018). The company has four major units namely Xandr, Warner Media, AT&T Communications, and AT&T Latin America. Additionally, AT&T Aspire represents the social responsibility unit because it oversees more than $350 million to help needy people to access quality education and training to get jobs. Indeed, the company’s success allows it to rank among the top entertainment and communication companies in the world.

Furthermore, the company has state of the art science labs and artificial intelligence research stations that enable it to use several visualization tools such as Tableau and Sisense to solve challenges. The research helps the firm to turn complex situations into practical and simple ones. Likewise, the data techniques allow AT&T to analyze available data to support it to optimize its network coverage (Rivera, Roman, & Schaefer, 2018). The data analysis tools provide information that is useful to the customer care section of the firm. The department handles complex products that could complicate the whole service delivery process; therefore, big data technologies help to streamline the procedure for both the client and customer care agent. As a result, the company makes a significant number of sales in every fiscal year because of its excellent service delivery techniques.

However, AT&T should employ more technicians that incorporate new technologies to analyze complex data. Talented staff would help the firm to identify the underlying issues and solve them proficiently. Similarly, qualified personnel allows the company to run smoothly and offer the best customer experience (Rivera, Roman, & Schaefer, 2018). Consequently, the executive managers should create annual courses that train current and potential employees to learn more about handling big data. Accordingly, a skilled workforce would increase the productivity and profitability of the business.

Conclusion

Organizations receive a significant amount of data they have to analyze and interpret competently every day. As a result, they use big data analysis techniques to understand the information and make meaningful decisions. Companies use data visualization techniques such as Domo, Tableau, Zoho Analytics, and Sisense to interpret data efficiently. Likewise, AT&T uses such approaches to improve the level of customer experience through efficient communication and entertainment. However, the company should train its employees often to ensure they analyze data successfully. Indeed, organizations should incorporate more data analysis tools to enable them to interpret data correctly and make viable business decisions.

References

Helfman, J., & Goldberg, J. (2016). U.S. Patent No. 9,477,732. Washington, DC: U.S. Patent and Trademark Office.

Rivera, S. I., Roman, J., & Schaefer, T. (2018). An application of the Ohlson model to explore the value of big data for AT&T. Academy of Accounting and Financial Studies Journal.

Runkler, T. A. (2016). Data visualization. Data Analytics (pp. 37-58). Springer Vieweg, Wiesbaden.

Schulz, C., Nocaj, A., El-Assady, M., Frey, S., Hlawatsch, M., Hund, M., … & Keim, D. A. (2016). Generative data models for validation and evaluation of visualization techniques.  Proceedings of the Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization (pp. 112-124). ACM.