Collaborative Business Intelligence (CBI) Definition
The term “collaborative” refers to an organized group of people who work towards achieving a common goal. Business intelligence, on the other hand, is defined as a process driven by technology that is used for data analysis and presentation of actionable information that assists executives, managers, and other corporate end-users to make informed business decisions. Hence, Collaborative Business Intelligence (CBI) is a concept that involves the integration of Business Intelligence (BI) and collaborative technological tools in order to support an organization in making new and improved business decisions. An example of CBI is the use of collaborative software like Microsoft Meetings, to distribute and retain quantitative and qualitative information, which might have been lost when employees exit a firm.
A Little More on What is Collaborative Business Intelligence
Collaborative intelligence comprises of multi-agent distributed systems. Individual agent, human, or machine is especially positioned with the freedom to chip into a problem-solving network. There is a need for improved communication between managers and other workers in firms. BI helps to improve communication, as it comprises of various tools, applications and procedures which assists firms in the collection of data from both internal and external systems, make arrangement for the data analysis, develop and run queries against the collected data and making a report out of it. Workers will be able to share information on why and how events are happening through CBI tools. Published BI results can be improved by getting feedbacks from its users.
Benefits of Collaborative Business Intelligence
In general, the main purpose of CBI is to enhance every part of a firm by increasing access to its data and generating more profit with such data. The insights gotten from such data can then be used to make strategic business decisions that will accelerate productivity, revenue, and growth. The greatest aim of companies that employ CBI practices is to make timely and better business decisions. Other unrealized advantages of CBI include, but is not limited to:
- Gaining a competitive advantage over business rivals.
- Assisting firms in identifying market trends.
- Locating areas of a firm’s problems that need to be addressed.
Risks associated with using Collaborative Business Interaction
Although BI is a wonderful tool that can help firms overcome many challenges, it comes with a number of risks. BI fails due to one major reason – the transparency offered by true, real time data reporting. The following are likely risks associated with BI:
- The majority of data collected from internal and external sources isn’t usable, as 85% of collected data is estimated to be inaccurate.
- Data can be easily altered, thereby, misinterpreting the truth if a firm is not using an automated BI solution.
- It is difficult to implement a BI system that uses a dashboard to distribute important and relevant data.
- If a firm’s employees don’t accept that BI makes work easier, they will likely not use it.
Collaborative Business Interaction tools
Examples of modern collaborative business interaction tools include:
- SAP BusinessObjects, a BI tool that works on its own or as a part of a larger SAP technology stack.
- Geckoboard, a dashboard software that displays key metrics and connects companies to existing software.
- Dundas BI suggests the right data visualizations and gives deep insights to non-analysts.
- Oracle BI, a middleware running on the Oracle business stack that provides businesses with wide-reaching analytics options.
- Sisense combines data directly from SaaS products and databases for analytics for every user.
- Domo unites native connections with data processing software apps.
- Tableau is a leading business intelligence software for data analysts and businesses.
Implementation of Collaborative Business Interaction tools
Embedded BI and Collaborative BI are implemented to enable “Self Service BI” for the end-users so that data analysis and interpretation becomes easy for end-users without being dependent on IT teams. In this case, an intelligently implemented BI tool is either embedded into another application, or multiple BI applications are made to work collectively to help end-users set up “Intelligent Alerts” or share dashboards across the team.
In the next few years, there won’t just be the accessibility to flexible and user-friendly predictive analytics tools, but they will also context-sensitive. There will be a concise integration between predictive analytics tools and the Internet of Things (IoT). Data will also be more visual, flexible and customizable.
References for Collaborative Business Intelligence (CBI)
Academic Research on Collaborative Business Intelligence (CBI)
Structuring collaborative business intelligence: A literature review, Kaufmann, J., & Chamoni, P. (2014, January). Structuring collaborative business intelligence: A literature review. In System Sciences (HICSS), 2014 47th Hawaii International Conference on (pp. 3738-3747). IEEE. Cooperative work in the processes of analysis and decision support currently gains strong attention in the business world. This is motivated by spreading corporate structures and technical developments like social media and network-oriented data storage that encourage the users’ comprehension and demand for easy communication about data. This article reflects the state of research in the domain of business intelligence regarding the opening of processes for new data sources and analysts. Existing approaches are often labeled collaborative business intelligence (CBI), but differ heavily in definitions and focus. Therefore, a framework is presented and three main fields for the research of CBI are identified, which encompass internal communication, data storage with (external) partners and data analysis with partners. This article compares the findings with developments on the software market and describes open topics in the research domain.
Business intelligence solutions for gaining competitive advantage, Muntean, M., & Mircea, G. (2007). Business intelligence solutions for gaining competitive advantage. Informatica Economica Journal, XI, 3, 22-25.
Advanced collaborative business ICT infrastructures, Rabelo, R. J. (2008). Advanced collaborative business ICT infrastructures. In Methods and Tools for collaborative networked organizations (pp. 337-370). Springer, Boston, MA.
A survey on recent research in business intelligence, Aruldoss, M., Lakshmi Travis, M., & Prasanna Venkatesan, V. (2014). A survey on recent research in business intelligence. Journal of Enterprise Information Management, 27(6), 831-866.
Business Intelligence Support Systems and Infrastructures, Muntean, M., & Brandas, C. (2007). Business Intelligence Support Systems and Infrastructures.
Design and governance of collaborative business processes in industry 4.0, Schoenthaler, F., Augenstein, D., & Karle, T. (2015, July). Design and governance of collaborative business processes in industry 4.0. In Proceedings of the Workshop on Cross-organizational and Cross-company BPM (XOC-BPM) co-located with the 17th IEEE Conference on Business Informatics (CBI 2015) (pp. 1-8).
Proposing a capability perspective on digital business models, Bärenfänger, R., & Otto, B. (2015, July). Proposing a capability perspective on digital business models. In Business Informatics (CBI), 2015 IEEE 17th Conference on (Vol. 1, pp. 17-25). IEEE. Business models comprehensively describe the functioning of businesses in contemporary economic, technological, and societal environments. This paper focuses on the characteristics of digital business models from the perspective of capability research and develops a capability model for digital businesses. Following the design science research (DSR) methodology, multiple evaluation and design iterations were performed. Contributions to the design process came from IS/IT practice and the research base on business models and capabilities.
Successful Customer Relationship Management: Why, ERP, Data Warehousing, Decision Support, and Metadata Matter, Davis, J., & Joyner, E. (2001). Successful Customer Relationship Management: Why, ERP, Data Warehousing, Decision Support, and Metadata Matter. In Customer Relationship Management (pp. 301-309). Vieweg+ Teubner Verlag, Wiesbaden. A CRM system is only as successful as the quality of data and data-management processes supporting it. Organizations planning wisely beyond the next millennium are thinking beyond process automation and are focused on getting better acquainted with customers to increase revenues and profits. Maintaining a sound metadata strategy — as well as understanding the roles of ERP, decision support, and data warehousing systems — is crucial for attaining this higher level of understanding.
Introduction to Business Analytics, Business Intelligence, and Big Data Minitrack, Marjanovic, O., Ariyachandra, T., & Dinter, B. (2014, January). Introduction to Business Analytics, Business Intelligence, and Big Data Minitrack. In 2014 47th Hawaii International Conference on System Sciences (HICSS) (pp. 3727-3727). IEEE.
Exploring the Underlying Relations between the Business Intelligence and Knowledge Management, Zarghamifard, M., & Behboudi, M. R. (2012). Exploring the Underlying Relations between the Business Intelligence and Knowledge Management. International Journal of Science and Engineering Investigations, 1(2), 31-35.
A platform for market intelligence, De Man, D. (2012, October). A platform for market intelligence. In International Trade Forum (No. 4, p. 21). International Trade Centre