Nowadays, the field of analytics is evolving to change the companies’ work in the future. Notably, more organizations and firms decide on competing using and implementing analytics in various business areas. Davenport and Harris divide the upcoming analytics technologies into three core groups, such as “approaches driven by technology, those involving human capabilities, and those involving changes in business strategy” (545). The business reality is associated with the generation of a massive amount of data. Receiving and processing this data provides a competitive advantage for companies.
Since manual processing and classification of uncovered volumes of information becomes almost impossible and ineffective, companies are trying to automate the data obtained using text mining and visual analytics. Therefore, these two crucial trends and upcoming technologies, among others, will influence the future of analytics. Davenport and Harris identify text mining tools such as spiders and data crawlers, which can calculate the phrases and words on websites, monitor technical-user blogs, and understand references to places, objects, people in order to analyze competitors’ behavioral patterns (527). Thus, text mining provides the ability to intelligently analyze and identify the importance of a specific text for solving a company’s goal.
Data visualization is also considered a new trend for business analytics. In general, data visualization refers to the graphical presentation of any data. Moreover, many companies are turning to visualization for active use in business. Davenport and Harris illustrate that OLAP tools were used for data visualization; however, there is considerable growth due to the introduction of newer generation tools, such as Tableau and QlikView, and the development of MicroStrategy and SAS (523). Consequently, visualization functions help present information better and evaluate its value and relevance. Additionally, tools like QlikView help simplify an enormous amount of data, illustrate it in the most understandable form, and provide the capabilities to companies to interact with the obtained data and find solutions to their problems.
To summarize, companies are exposed to an enormous flow of information from a wide variety of customer devices and applications. This factor affects the fact that soon, analytical tools will allow companies to analyze the data obtained and come to independent conclusions instantly. According to Davenport and Harris, AI, machine learning, and cognitive technologies will empower and increase analytics’ business value through automated decisions, analyses, and actions (547). Moreover, companies will be able to perform more text, speech, and image mining in the future.
Besides, visual analytics and text mining are developing and will evolve and expand significantly in the next few years. Consequently, Apple and Amazon have invented Siri and Echo/Alexa relatively; hence, these consumer applications will develop further and, through deep learning algorithms, learn how to analyze and make decisions concerning unstructured data (Davenport and Harris 556). In addition, various companies, including SAP, IBM, SAS, Microsoft, and Oracle, already offer product suites that involve “data visualization, business intelligence, and reporting solutions” that facilitate business operations (Davenport and Harris 537). Therefore, the upcoming trends and technologies provide insights that enable companies to perform text mining, visualize complex data, and better collaborative data exchange between parties (Davenport and Harris 537). It is crucial to note that the primary task of analytics was to provide reports on the collected statistical data earlier. Nowadays, business analytics and its future trends offer new opportunities and tools to companies for extensive research and forecasting based on more detailed data analysis.
Work Cited
Davenport, Thomas, and Jeanne, Harris. Competing on analytics: Updated, with a new introduction: The new science of winning. Harvard Business Press, 2017.