A. Gartner Magic Quadrant analysis approach offers a graphically dynamic classification of four styles of product suppliers in fast-growing markets: Leaders, Visionaries, Niche Players and Challengers.
a. Leaders are expected to perform well towards the current vision and are well positioned for next growth.
b. Visionaries grasp the direction where the business is headed or have a vision for reforming the laws, but do not yet work well.
c. Niche Players effectively work on a small segment or become distracted and do not innovate or out-perform the other players.
d. Challengers are doing well today, or may control a broad portion, but may not show an appreciation of the course of the industry.
It is observed that Gartner Magic Quadrant for Analytics and Business Intelligence Platforms from 2018 to 2019 had minor changes:
1. Challengers: Micro strategy was the constant challenger in 2018 and 2019.
2. Leaders: In year 2019 saw a shift of Thoughtspot from the Visionaries to the Leaders, apart from Tableau, Qlik, and Microsoft who were the constant leaders since 2018. It can be perceived that Thoughtspot must have obtained the ability to capture the market share.
3. Visionaries: Thoughtsoft due to its ability to gain mew market, shifted to leaders in the 2019. While IBM, probably due to its inefficiency to develop and execute their vision, shifted to niche segment. While the others; Sisense, Salesforce, SAS, SAP, TIBCO Software, remained constantly in the visionary segment in the two years.
4. Niche: IBM shifted from visionary segment, the niche segment saw a new entrant, Gooddata. It was because it was trying to enter the market. While others such as: Looker, Domo, BOARD International, Yellowfin, Logi Analytics, Oracle, Information Builders, and Pyramid Analytics, remained constant.
B. It can be analysed that 2020 report, witness minor changes:
1. Challengers: Two new brands Looker, from Niche segment, and TIBCO Software, from the vision segment, shifted to the challenger segment because they could gain the market position and had developed resources for sustainable growth and challenge the leaders.
2. Niche: There were two new entrants Dundas and Alibaba cloud that have sort to enter the market segment this year. While Looker shifted to the challenger segment. Oracle and Yellowfin shifted to the Visionary segment. And the others such as: Information Builders, Pyramid Analytics, Domo, BOARD International, Logi Analysis, Birst, and IBM remained constant.
3. Leaders: There was no change in this segment since 2019, the world leaders in the Business Intelligence and Analytics platform were Microsoft, Tableau, Qlik, and Thoughtspot.
4. Visionary: This segment saw movement of three brands, one, TIBCO Software, shifted from the segment to Challenger segment and two, Oracle and Yellowfin, shifted to the vision segment from Niche. Oracle and Yellowfin were able to develop their products, capture market and increase profits.
C. Technology has been a central determinant of the sustainability of organisations across the globe. However, the effect of Information Technology can only be understood by human action. Organisations continue to track developments in technology. In addition, companies can educate and inspire their workers to be creative, because creativity influences all operational facets (Deng, & Chi, 2012). When emerging technologies are introduced in organisations, the facets of the process are subject to significant adjustments. One of the key problems confronting organisations is the handling of rapid technical transition. The degree of innovation is typically dictated by the productivity of organizations. Organizational technology comprises of information, abilities, techniques and practical representations, such as devices and machines. The effect of technology that is currently experiencing radical transition has culminated in the need for the jobs of professional employees. Entering the organisation, such employees would be encouraged to be creative in order to enable the company to cope with technical changes. The capacity of the company to build prosperity and enhance the well-being of employees is defined by the degree of technical growth (Deng, & Chi, 2012). Furthermore, the technological progress has a major effect on organisations across the globe. Online media has transformed the globe, allowing companies to control their contact networks.
Business Intelligence utilizes tools, procedures and technology that enable companies to capture, store, track and interpret market data for the purpose of taking decision-making simpler. Higher learning organizations are going through a time of transition as they are digitized to render them successful like most organisations. Technology needs to be implemented to increase the consistency and reliability of the implementation of research processes and enhance the experience of students. In fact; learning institutions must recruit and retain students in order to achieve higher graduation levels (Deng, & Chi, 2012). Business intelligence will also allow the management of higher education organizations to obtain and use appropriate and timely knowledge and make choices that will ultimately enhance main success metrics.
In the taxi industry, the invention of the company model is a required capacity for management, as it allows companies to respond to emerging market conditions, such as growing competitive stresses, transforming economic influence, and the evolving customer’s needs and wants (Chaudhuri, Dayal, & Narasayya, 2011). Being the deciding authority, executives should be creative in their preference of market strategies, because that is the primary determinant of how an enterprise works and the principles to be obtained. It has been suggested that the effect of technology on the market strategy of organizations is the primary determinant of corporate success.
Business intelligence will allow Uber and related organisations to obtain and use accurate and appropriate knowledge and make choices that can ultimately boost key success metrics. Business intelligence covers a range of similar practices, such as electronic information analysis, data collection, querying and monitoring.
D. Uber Technologies Inc. is a pioneer in the mobility industry, using technology that assists users to take rides from their decided pickup point. Uber uses Google Analytics as the business intelligence application. Its helps in using the data for surge pricing and its rating system.
Daniel Marshall was the Guest Speaker and the topics of discussion were around Business Intelligence and Data Analytics. The speaker also lectures at Kaplan Business School in Business Analytics Program. Currently, he is a manager at Data Science Investment Division, which is a Whiteboard superannuation fund.
He started by explaining the requirements which were beneficial for him to start his career in data science. He has a Ph.D. degree in mathematics which was beneficial in the data science work, along with this he also has a degree in computer science, and background of software programming. He also discussed the investment strategy that has changed drastically with the advent of data science. He also discussed the various uses of the data, in various fields, particularly in his current organization.
He further discussed various BI tools and states that earlier Excel was used primarily to present the findings to the stakeholders. Additionally, most of the stakeholders are non-technical, and hence it is important to use an easy platform that is easier to understand by the stakeholders. Further, it has not been a long time since the business intelligence tools have developed and become more advanced. Thus, senior managers prefer using excel than other business intelligence tools.
He also described that having a background in mathematics is not necessary for being a data scientist, however, it helps in understanding the data and helps in understanding the problem and helps in data analysis. Having a background in mathematics helps an analyst in answering the questions posed by someone, whereas, data scientists help in defining the questions in the simplest way possible.
It was also discussed in the session that, using descriptive statistics was useful while talking to people, however, inferential statistics may be used for investigating the patterns of interest or problems which are similar to data mining or data exploration.
Visualization tools were also discussed in the session, how they have progressed over the years. According to Daniel, it wasn't very long that the visual aids have come up for use. It is with the advancement that the visualization tools have become widespread, popular, and powerful. They help in exploring the random data and also help in making the data understandable for the other non-technical stakeholders. The three tools or languages which are most prevalent are Spot fire, Tableau, and Power BI. On the other hand, the primary language in data is R and Python.
It was also discussed that analytics is a trivial thing for the present as every organization is using data, and need a data analyst. With that comes the problem of ethical considerations taking into account that a lot of data is being collected and circulated. Privacy is being impacted, however, a lot of time people are comfortable giving their information to the company for which they know how their information is going to be used. The companies need to collect the data ethically and legitimately, before releasing it.
Thus overall, the message that the speaker was trying to put across is that background knowledge about the data or the languages would help develop a career as a data analyst or data scientist. It is also important to use a method that makes the data understandable to the general public. Moreover, it is important to understand that data is trivial in today’s world as it is used by almost all the organizations in all the industries.
Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of The ACM, 54(8), 88.
Deng, X., & Chi, L. (2012). Understanding post adoptive behaviours in information systems use: a longitudinal analysis of system use problems in the business intelligence context. Journal of Management Information Systems, 29(3), 291-326.
Microsoft. (2020). Microsoft recognised as a leader in analytics and BI platform for 11 years. Retrieved from https://info.microsoft.com/ww-landing-gartner-bi-analytics-mq-2018-partner-consent-test.html.
Qlik. (2020). 2020 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms. Retrieved from https://www.qlik.com/us/gartner-magic-quadrant-business-intelligence.
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