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What is Augmented Analytics?

Discuss about the Tendencies of Future of the Business Intelligence.

Business Intelligence has become an inevitable aspect in every organization as it helps in enhancing the productivity of the company, estimate the sales demands, keeping accurate track of information regarding established goals, attaining better strategic awareness and transforming data into actionable information (Chen, Chiang & Storey, 2012). This is the reason that business people have opted for business intelligence in order to attain greater competitive advantage and keep track for the future trends. This business report highlights two of the major tendencies for the future of Business Intelligence- “Augmented Analytics” and “Redefined Business Reporting”. Duan and Da Xu (2012) stated that businessperson emphasized changes in business analytics to gain more benefits compared to other competitor organizations.  

As per as the definition goes, augmented analytics is an approach that automates all the data insight by means of machine language and natural language (Loshin, 2012). Chang (2014) also stated that the insight of the language also automates the data preparation and allow the system to share data. This process will help the marketers to represent effective and clear result which will furthermore allow the users to manipulate the data accurately and quickly. Sauter (2014) furthermore depicted that augmented analytics allow the people of IT department of an organization to focus on strategic issues and helps them to make better decisions for earning greater financial profitability. On the other hand, people in the managerial position spend a significant proportion of their office time for controlling task and coordinating administrative works (Zheng, Fader and Padmanabhan, 2012). These people are also involved with judgmental works and suggest important business decisions for enhancing the process. Thus, incorporating of the business intelligence does not only help management people to work faster but also accurately.

In this section, the two major tendencies for the future of Business Intelligence is discussed are “Augmented Analytics” and “Redefined Business Reporting”.

Zheng, Fader and Padmanabhan (2012) stated that in recent times when all the business needs data and store them in the database. Zhong et al. (2017) furthermore stated that company nowadays focuses more on work automation according to their business objectives and goals and thus utilizes the concept of business analytics for building models and integrating data. In the year 2017, the concept of ‘smart data discovery’ has been introduced, which is used for analyzing the ongoing challenges in the organization. The management people from the IT department of a company can analyze the data and identify issues and patterns which will help them to identify any discrepancy. It is highlighted that smart data discovery can also be handled by a person, who does not have any computer programming knowledge (Linkedin, 2018). This technology can be handled in a drag and drop interface and does not need knowledge of any algorithm and statistical analysis (Linkedin, 2018). Utilizing this technology, Smart Data Discovery tools allow users to share findings which will furthermore help in forecasting and predicting results for organizational resource planning (Linkedin, 2018). Chang (2014) presented a statement that in traditional days, in order to perform these tasks, data scientist were hired; however, in recent times, augmented analytics works as an alternative to these skilled data scientists. Trained employees can easily perform the data monitoring and then develop building models for planning actions (Duan & Da Xu, 2012). Thus, it can be said that data-based decision can be easily taken by the management people and it is also easier for them to release whether or not their company is progressing.

Focusing on Strategic Issues

Focusing on strategic issues 

Turban et al. (2013) highlighted that with the help of augmented analytics clear data can be evaluated with greater efficiency which will help the data scientists for drawing the data outcome. These outcomes are sufficient to track the progress of the company.

Augmented analytics enhances the empowerment as all the progress can be tracked with accuracy. This will help the organization to identify the drawbacks and the downturns. Chang (2014) thus stated that a company is liable for taking effective steps for overcoming the adversity.

Smart Data Discovery unlocks deeper business data and can identify the underperforming products segments and more profitable customer group segments (Zhong et al., 2017). This happens as Smart Data Discovery tools automatically identify the relationship among the data like clusters and co-relations and then helps the marketers to present a better preview of the business progress. 

Lim, Chen and Chen (2013) depicted that this tools also allows making insights and make them accessible via visualizations like exactly what percentage of people like a particular product and how many users belong from a similar group. Chang (2014) also portrays that this data analysis will help the marketers to forecast business predictions that can be used in developing new business plans and product development. Laursen and Thorlund (2016) furthermore stated that not only the augmented analytics is used in identifying the business progress but it also helps in determining the market trends. Taken for instance, if the revenue shows a declination of 15%, the data obtained can be evaluated to assess whether the data reveals loss in business or decrease in the market demand for that products or service.

Agile centralized business intelligence

Collier (2012) stated that this tool is used especially for the companies that use cloud technology. These companies are dealing with greater data and it is important for them to know whether or not their business is in a profitable position. Augmented analytics helps the new generation businesspersons to enable the data analytics delivery at a faster rate while utilizing minimal resources.

Training through the digital technology resulted in a greater understanding. Taken for instance, medical training can be given by a surgeon, who is performing a severe operation and can share the experience with other doctors outside the operation theatre through AR display unit. Google Glass, is one such attempt (Dataversity, 2018).  

AR technologies are now performing well also in agriculture. The farmers can now access the water management systems and sensor drive that alerts them concerning the excessive utilization of water resources (Dataversity, 2018). Some of the other application of the augmented analytics in agriculture is to monitor the water usage, moisture content and temperature of the soil and crop status.

Agile Centralized Business Intelligence

Head-up displays (HUD) is an application of augmented analytics that helps military pilots to view vital information by not compromising their sight (Telegraph, 2018).  HUD is a transparent screen showing the details of location and state of the aircraft and thus allows the pilot to not look down at their controls. Haeuslschmid et al. (2016) stated that the first application of this concept was used during the Second World War but the application at that time was difficult because it is difficult to locate targets depending on the verbal instructions of the crew members.

According to Laursen and Thorlund (2016), business reporting is classified into two different ways that are present the report through summarization of the organizational output and the second one is to use an illustration like visualization features, cross-tab reports and data tables. In the first case, organization segregate working personnel, who is recruited for preparing the report, enhancement of the report in collaboration with people like developers and then preparing the reports manually. However, in the latter case, tools such as business intelligence are used. Laursen and Thorlund (2016) highlighted that managers of different companies are associated with work that is developing engagement with different stakeholders, implementing strategy and innovation, coordinating and controlling administrative works and helping in solving problems.

In a multinational organization, writing reports based on the findings of all the employees and stakeholders are cumbersome if done manually. In such companies, nowadays AI-powered software robots have been used that help the company to write around 300 stories to 4,400 (Harvard Business Review, 2018). This technology is adopted by a data analytics company named Tableau that has developed a partnership with Narrative Science, natural language generation tools provider based in Chicago (Harvard Business Review, 2018).

According to Chang (2014), business reporting is also known as enterprise reporting and it is a process to make the public aware of the business data and financial data published by a company. Thus, it is important for the managers to present accurate data in the report that results in transparency among the organization and other stakeholders. The future of business intelligence will also be applicable for making judgmental works that require more competency than artificial intelligence. Laursen and Thorlund (2016) on the other hand presented a view that many managers could feel unhappy as the artificial intelligence (AI) can replace their jobs. On the other hand, the report of the Harvard Business Review it is stated that managers think that if AI can be used in making effective decisions, they should support them in suggesting new plans for the betterment of the company rather than replacing them. The report on the other hand also stated that AI also allows the managers to enable the interaction with intelligent machines.   

Applications of Augmented Analytics

Ramakrishnan, Jones and Sidorova (2012) moreover stated that one of the most important work roles of the managers is to harness others’ creativity. The importance of the managers can be well witnessed in bringing together the diverse ideas and integrate them for developing workable and appealing solutions (Harvard Business Review, 2018). Thus, with the AI, these managers will embed the design thinking for reporting the business reports. It can be also said that the findings of the augmented data highlight all the necessary results and all the negative and positive aspects of the business. This will help the managers to present the final outcome in tabular or pictorial form, which is easily understandable by the other users.

Maintaining the data quality of the business report

It is highlighted that since 2002 one of the major problem if the Business Intelligence (BI) software highlighted by the managers is the quality of the data (Lim, Chen & Chen, 2013). Thus, the outcome of the findings is that if organization’s core data cannot be trustable and erroneous, any implications derived from the data will also be incorrect. Thus, data scientists are aiming to bring data together from data silos. These data from different sources is then compiled by the AI in a usable format by maintaining the validity of the original source of the data (Clicdata, 2018).

Turban et al. (2013) stated that with the help of the Business Intelligence, managers can collect data not only within the organization but also from outside of the company. Zheng, Fader and Padmanabhan (2012) furthermore stated that the data found from the analysis of the inner and outer data results are better findings of the loopholes. Thus, the managers will also able to develop effective strategies for the betterment of the company. 

Representing ready to use data

 The use of business intelligence is used to find the relevant data and also track the past data for a better finding of the progress made by the company. Wixom, Yen and Relich (2013) stated that in present times also, BI is used to collect and present data that can be analyzed easily including historical data. It is easier for a manager to track the similar data based on a time span and then assess the progress.

The advanced business reporting presents all the data and underlying figures. The report analysis made by the management people along with the developed backup actions, allow the users to identify the link between the data and action. Thus, it empowers the users to understand the different areas of business and proper justification of the explanation of the taken business decisions.

What is Redefined Business Reporting?

Business Intelligence can discover data from different sources and shows the relationship among them in an effective manner. Companies use this technology in order to demonstrate their data which furthermore allow the management people to explore new business possibilities (Sisense, 2018). Taken for instance, a supply chain service provider or an organization that has a supply chain management unit can implement the BI for evaluating their shipping performance. The report shows the Overall Equipment Effectiveness (OEE) to measure productivity rate and can report the illustration as below:

The people in management are also able to measure the quality index dashboard that shows the shipping progress based on the customer complaints, first yield pass and timely delivery. Thus, in this way, the management, as well as the users, can clearly show how effective their services are performing depending on the rate of the customer complaints. Zhong et al. (2017) also stated that these report also allow the customer to identify the strength and weakness of the companies and they can also judge whether or not to take services from the company.

It is an open source Eclipse project used by the company to create data visualizations by the managers and also help them to report the findings. The major components of the BIRT tool are BIRT runtime and report designer (Eclipse, 2018). There are some other components like chart designer, chart engine and viewer which act as a standalone solution for publishing report (Eclipse, 2018).

Pentaho is a business intelligence suite and uses the data from reporting to data mining (Hitachi Vantara, 2018).  Pentaho BI suite is also a development from the same company that provides an infrastructure not only to run a report but also view the report through a web-based user interface (Hitachi Vantara, 2018). This tool is transformed all the required data to information in a pixel-perfect report and is compatible for all formats of data like PDF, HTML, XML, Rich-Text-File, Excel and CSV (Hitachi Vantara, 2018). Companies can also embed their codes to run reports and access available data and it can also be used through web-application.


 In this business report highlights two of the major tendencies for the future of Business Intelligence is discussed that is "Augmented Analytics" and "Redefined Business Reporting". It has been found that augmented analytics is the concept of ‘smart data discovery' which is used for analyzing the ongoing challenges in the organization. Moreover, Smart Data Discovery tools also allow users to share findings which will furthermore help in forecasting and predicting results for organizational resource planning. The advantage of augmented analytics that is addressed in this report is focusing on strategic issues, improves accountability, positive impact on ROI and TCO and accurate business predictions. The applications of augmented analytics highlighted are agile centralized business intelligence, training purpose, agricultural directions and Head-up displays (HUD). Another future tendency identified is redefined business reporting which also known as enterprise reporting and is a process to make the public aware of the business data and financial data published by a company. The main purpose of such reporting system is to present accurate data in the report for maintaining transparency among the organization and other stakeholders. The advantages of Business intelligence in business reporting found in this report are maintaining the data quality of the business report, ease in collecting the data for the report, representing ready to use data and empowering end-users. The one application of business intelligence in business reports is found to perform as an analytics.

AI-powered Software Robots

Reference List

Chang, V. (2014). The business intelligence as a service in the cloud. Future Generation Computer Systems, 37, 512-534.

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS quarterly, 1165-1188.

Clicdata. (2018). The Future of Business Intelligence – What’s Coming in 2018 and Beyond. [online] Available at: [Accessed 03 Jul 2018].

Collier, K. (2012). Agile analytics: A value-driven approach to business intelligence and data warehousing. Addison-Wesley.

Dataversity. (2018). Augmented Analytics Use Cases. [online] Available at: [Accessed 03 Jul 2018].

Duan, L., & Da Xu, L. (2012). Business intelligence for enterprise systems: a survey. IEEE Transactions on Industrial Informatics, 8(3), 679-687.

Eclipse. (2018). BIRT Buzz. [online] Available at: [Accessed 03 Jul 2018].

Haeuslschmid, R., Shou, Y., O'Donovan, J., Burnett, G., & Butz, A. (2016, October). First steps towards a view management concept for large-sized head-up displays with continuous depth. In Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 1-8). ACM.

Harvard Business Review. (2018). How Artificial Intelligence Will Redefine Management. [online] Available at: [Accessed 03 Jul 2018].

Hitachi Vantara. (2018). Pentaho Reporting. [online] Available at: [Accessed 03 Jul 2018].

Laursen, G. H., & Thorlund, J. (2016). Business analytics for managers: Taking business intelligence beyond reporting. John Wiley & Sons.

Lim, E. P., Chen, H., & Chen, G. (2013). Business intelligence and analytics: Research directions. ACM Transactions on Management Information Systems (TMIS), 3(4), 17.

Linkedin. (2018). What is Augmented Analytics and Why Does it Matter?. [online] Available at: [Accessed 03 Jul 2018].

Loshin, D. (2012). Business intelligence: the savvy manager's guide. Newnes.

Ramakrishnan, T., Jones, M. C., & Sidorova, A. (2012). Factors influencing business intelligence (BI) data collection strategies: An empirical investigation. Decision Support Systems, 52(2), 486-496.

Sauter, V. L. (2014). Decision support systems for business intelligence. John Wiley & Sons.

Sisense. (2018). Manufacturing Dashboard Examples. [online] Available at: [Accessed 03 Jul 2018].

Telegraph. (2018). What are head-up displays and how do pilots use them?. [online] Available at: [Accessed 03 Jul 2018].

Turban, E., King, D., Sharda, R., & Delen, D. (2013). Business intelligence: a managerial perspective on analytics. Prentice Hall, New York.

Wixom, B. H., Yen, B., & Relich, M. (2013). Maximizing Value from Business Analytics. MIS Quarterly Executive, 12(2).

Zheng, Z., Fader, P., & Padmanabhan, B. (2012). From business intelligence to competitive intelligence: Inferring competitive measures using augmented site-centric data. Information Systems Research, 23(3-part-1), 698-720.

Zhong, R. Y., Xu, C., Chen, C., & Huang, G. Q. (2017). Big data analytics for physical internet-based intelligent manufacturing shop floors. International journal of production research, 55(9), 2610-2621.

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