Information technology has created their in every sector of business processes. Therefore, analysis of these data sets and information about different aspects are crucial for analyzing any impactful situations. These data are often helpful in decision making perspective for any organizations. The organizations and project uses statistical data for understanding impact and applications of different technologies within this contemporary era. It is opened by Shmueli, Patel and Bruce (2016) that data has no use until it is being analyzed for identifying new opportunities for business and research process.
This literature review is aiming at recognizing the impact and benefits of two significant business analytics tools (Penthao and IBM Watson) within the contemporary era of development. In contrast with this fact this literature review is first of all justifying the selection of these two tools. Secondly, literature review is combining applicable methodologies, data analysis and business process re-engineering. Additionally, recommendations and designs are also provided within this literature for these two business analytics tools.
According to Sharma, Mithas and Kankanhalli (2014), innovations of new technologies in different industrial sectors has made huge amount of data accessible with the help of information technology. Watson Analytics is one of the most consistent Business Analytics tools that allow enhancement, narration, evaluation of different management procedures. Maté et al., 2016) cited that Watson Analytics is capable in functions associated with collaboration, efficient management and integration of high quantity of data. Additionally, this analytics tool is capable of revealing hidden information patterns for data collection and providences of enhanced results. However, Marín-Ortega et al., (2014) opined about the hidden relation between various data sets used within the concerned evaluation of information.
IBM Watson Analytics tool is developed based on technological concepts of cloud computing that allows the users in utilizing the system and analytics tools. The customers allowed to the easy analysis of data with help of user friendly interfaces. This helps the business in exploring opportunities from different segments of operations. In addition to this, the applications of Watson Analytics provide guidance to data prediction and data discovery in reference to visualized data interrelations within the dashboard (IBM Watson Analytics, 2017). This data analytics tool also provides cognitive applications for providing direct human interface with an analytic system. However, the advanced analytics tool provides complex cloud based services along with the automatic prediction of data explorations and creation of information and graphic models.
Penthao, a Hitachi Group Company which is considered as leading data integration and business Analytics Company along with an enterprise class, open source based platform for diverse big data deployments. Penthao Analytics tool helps the organizations in harnessing values from big data and Internet of Things (IoT) for gaining revenues from operational excellence from these fields of operation. Penthao’s modern, easy and interactive approaches empower business users in accessing the blend of all types of data (Chung & Chung, 2013). With the help of spectrum of increasingly advanced data analytics from basic reports to adaptive modeling, the users can easily analyze and visualize their data in multiple dimensions with the help of Panthao dashboard.
The interactive visual analysis allows the business users in accessing highlights and zoomed view of information analysis patterns and functionalities. Additionally, the graphical and responsive dashboards provide efficient opportunities to the businesses in achieving significant improvement of performance (Shmueli, Patel & Bruce, 2016). Comprehensive solutions for reporting purposes provide self-service interactive reporting for high volume of data that help the organizations in preparing enterprise reporting. Streamline management and administration is another advantage provided by Penthao Analytics tool in deploying and developing security and reliability of users within different organizations.
Data are being generated at a huge rate in every seconds. The business authorities needs to evaluate and analyses the data for gaining insight of the business processes and help them in their decision making processes (Sharma, Mithas, & Kankanhalli 2014). The rapid development and the advancement of technology in analytical sector has allowed in business authorities to leverage the information technology and real time business analytics tools for gaining access to the underlying relation between the data for developing business strategies (Sharma, Mithas, & Kankanhalli 2014). Pentaho and the IBM Watson Analytics tool are one of the foremost, light weight, real time and user friendly business analytical tools that are used by most of the business organization. Chung, and Chung, (2013) showed that both IMB Watson and Pentaho uses similar methodologies for the analysis of the huge volume of data. For the analysis of the data, the data set needs to be obtained from the data base. According to Chung, and Chung, (2013), the business organization needs to ensure the reliability and validity of the data sources for eliminating any possibilities of data modification at source. Chang, (2014) illustrated that the application of the poor quality of data can results in the incorrect results after the analysis.
Sharma, Mithas, and Kankanhalli, (2014) cited that, both the Pentaho and IBM Watson support various type of data source including. Isik, Jones, and Sidorova, (2013) showed that Pentaho allowed the integration of SQL query, Database tables and CSV files for inputting the raw data (Sharma, Mithas, & Kankanhalli 2014). On the other hand, IBM Watson Analytics allowed the input of data directly from the One drive, HubSpot, SurveyMonkey, Twitter, paypal, SugarCRM, SendGrid and local files including CSV, SQL connection and database table (Isik, Jones, & Sidorova 2013). The successful uploading of the data in the application helped in gaining discovery and insight of the data analysis.
According to Laursen, and Thorlund, (2016), the application of the Business Intelligence tools including the IBM Watson Analytics and the Pentaho have various and significant importance in the architectural and the construction industries (Isik, Jones, & Sidorova 2013). The Watson Analytics provide a robust and flexible tool for creating a predictive model that allowed the site manager and safety manager to identify the significant factors impacting the safety and quality of the construction process. In addition to that, Wu, Chen, and Olson, (2014) showed that the application of Pentaho and Watson Analytics can both be significantly applied in the architectural and the construction process. Through the application of the business intelligence tools, the project manager or the construction manager can obtain visualization of the accidents in the construction sites that are most likely to occur. Predictive Model obtained from both the business analytical tools provides the relative prediction of the places and the reason the accidents are most likely to occur in construction project. Petermann et al., (2014) illustrated that this allowed in increasing the efficiency and quality of the construction and the architectural design.
Shmueli, Patel, and Bruce, (2016) defined business process reengineering as a specific process used for the redesign and analysis of the workflow used within the organization for optimizing the enterprise process and automating various activities followed. The application of the BPR allowed in organizing and identifying the process used for determining the deliverables and activities in the project. Rausch, Sheta, and Ayesh, (2013) showed that the application of the business intelligence provide effective way of data collection, analysis and integrating the raw information gathered during the business processes. According Lessard, and Eric, (2014) the process of BPR is developed by taking advantages of the KPI (“Key Performance Indicators”) that determines the action course that needs to be followed. The Business analytics tools including Watson Analytics and pentaho analytics tools helps in identifying the KPI impacting the performance and quality of the project and activities followed in the project. Therefore, including the business intelligence application in the process of business re-engineering provides a driving force for gaining specific information for increasing the efficiency and quality of work done.
The application of the Business intelligence Application in helped in the developing he data driven decision making procedure for enhancing the business process and the quality of the services. In the current system, the global technology development provides various real time tools that allows analysis, aggregation, generation and visualization of the data for strategizing and improving the business management processes (Sharma, Mithas, & Kankanhalli 2014). Both the Pentaho and Watson Analytics provides insight of the business information for identifying the underlying relation between the different factors impacting the business process. Both the applications have different process and relevant advantages for using them in particular business opportunities. Being real time and based on cloud technology, the application of the BI tools would allow the organization in gaining assistance in decision making process. Sharma, Mithas, and Kankanhalli, (2014) showed that Pentaho helped in eliminating the organizational barriers from valuing the data. Furthermore, extensible and embeddable architecture of Pentaho allowed the business to transform the raw data into valuable information.
Isik, Jones, and Sidorova, (2013) illustrated that Watson Analytics tools allows visualization of the discoveries and the findings in one single location for easy evaluation and visualization.
Similarly, Pentaho provides similar simplified, simple and interactive dashboard for showcasing the analysis and findings of the raw data obtained from the analysis (Isik, Jones, & Sidorova 2013). Furthermore, the interactive visualization allows the user in filtering, highlighting, and zooming any particular area within the analysis for obtaining advanced and customized visualization.
Conclusion
Business analytics is the practice of iterative methodical exploration of an organization’s data along with the emphasis on statistical analysis. This business analytics is used by different companies committed to data driven decision making processes. Business processes deals with huge amount of data involved within different segments of applications related to business analytics. The business decisions are analyzed with the help of different business analytics tools. Among all of these tools, Penthao Analytics and IBM Watson Analytics are used for managing different analysis based on required data sets and huge information. This literature review is presenting about various applications, methodologies and designs that are used within these analytics tool. These business analytics tools incorporate various data analysis patterns and processes for identifying significant changes among business processes or decision making perspectives. This literature review is also elaborating about data analysis patterns, models that are used within this analysis processes and the business objective behind finding these applications of business analysis tools. The designing approaches used for developing the dashboards make the users feel easy in accessing their data analysis results and functional aspects involved within the data analysis perspectives.
References
Chang, V. (2014). The business intelligence as a service in the cloud. Future Generation Computer Systems, 37, 512-534.
Chung, P. T., & Chung, S. H. (2013, May). On data integration and data mining for developing business intelligence. In Systems, Applications and Technology Conference (LISAT), 2013 IEEE Long Island (pp. 1-6). IEEE.
IBM Watson Analytics. (2017). Ibm.com. Retrieved 17 May 2017, from https://www.ibm.com/in-en/marketplace/watson-analytics
Isik, Ö., Jones, M. C., & Sidorova, A. (2013). Business intelligence success: The roles of BI capabilities and decision environments. Information & Management, 50(1), 13-23.
Laursen, G. H., & Thorlund, J. (2016). Business analytics for managers: Taking business intelligence beyond reporting. John Wiley & Sons.
Lessard, L., & Eric, S. K. (2014). Value Cocreation Modeling: Supporting the Analysis and Design of B2B Service Engagements through Agent Orientation and Business Intelligence. In iStar.
Marín-Ortega, P. M., Dmitriyev, V., Abilov, M., & Gómez, J. M. (2014). ELTA: New Approach in Designing Business Intelligence Solutions in Era of Big Data. Procedia Technology, 16, 667-674.
Maté, A., Trujillo, J., García, F., Serrano, M., & Piattini, M. (2016). Empowering global software development with business intelligence. Information and Software Technology, 76, 81-91.
Pentaho.com (2017). Pentaho. Retrieved 17 May 2017, from https://www.pentaho.com/product/product-overview
Petermann, A., Junghanns, M., Müller, R., & Rahm, E. (2014). Graph-based data integration and business intelligence with BIIIG. Proceedings of the VLDB Endowment, 7(13), 1577-1580.
Rausch, P., Sheta, A. F., & Ayesh, A. (Eds.). (2013). Business intelligence and performance management: theory, systems and industrial applications. Springer Science & Business Media.
Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations. European Journal of Information Systems, 23(4), 433-441.
Shmueli, G., Patel, N. R., & Bruce, P. C. (2016). Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner. John Wiley & Sons.
Wu, D. D., Chen, S. H., & Olson, D. L. (2014). Business intelligence in risk management: Some recent progresses. Information Sciences, 256, 1-7.
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