Business Intelligence and Analytics in Higher Education Institutions
Business Intelligence and Analytics
Business Intelligence and Analytics refers to the process of collecting and analyzing data from internal and external systems to produce actionable information to help in making decisions. The process of business intelligence and analytics is driven by information technology and in modern business; there are countless software for this purpose. The users of the simplified actionable information could be management or the people in operations.
The current economy is information-driven; producing what has come to be known as the knowledge economy. Modern business is generally driven by having the right information at the right time and in a usable format to inform decision-making to enable businesses to survive in a competitive market. Higher education is an important area where business intelligence finds application. However, there are many challenges that come with dealing with business information.
First, the knowledge economy generates humongous amounts of information available. The fall in internet costs, improved connectivity, the emergence of mobile telephony and social media produce tons of information at any particular moment. The data come in the form of structured and unstructured data, machine data and online data. Unlike in the past when personal communication and official business communication were distinct and apart, the modern information era brings all these sectors into interaction through social media in business and other novel business processes. E-commerce and e-business generate many data. According to Ernst and Young (2014) the data available is vast, comes in a large variety and high speed. Higher education in particular generates so much data through research. Sieving useful data in such an environment becomes a big challenge. The speed with which data is available requires that higher education institutions recognize new knowledge quickly and put into use before it becomes obsolete or before it loses its academic value. Domo (2015) estimates that the amount of new data generated will grow to 44 trillion gigabytes in 2020 from 4.4 trillion in 2013. Such data becomes useless unless there is a way of quickly identifying relevant data and analyzing it. Chen, Chiang and Storey (2012) opine that big data do not necessarily translate to big impact. In research, there is so much information available on the Internet that identifying the most recent, relevant and academic research reports becomes a big challenge for many students. This realization calls for efficient tools to organize the available information to useful and actionable format. In an earlier research, Zeng et al. (2010) recognized that social media was generating data in large proportions that systems that could skim through it to identify useful information needed to be developed.
The vastness of data (also known as big data) introduces the problem of time in making use of it. The huge volumes of data prevent quick discovery of useful information that can benefit academic research. Institutions also need to develop profiles of potential students before admission, to identify the financial capability of students to determine award of financial aid, to track students’ academic performance, to analyze availability and demand of course offerings, to review the cost-benefit analysis of educational programs, to identify the donation potential of alumni and to analyze the job market among others. Despite the development of technology tools to deal with the data, the amount is just too great for them to identify accurately the nature of information relevant to the particular institutions. After discovery, the information needs to be organized and analyzed before being presented to the users for it to be of any benefit. Organizations spend a lot of time training their data specialists to manipulate the data to make it usable. Whenever there is a change in analytics tools, further training becomes necessary. Businesses can use the man hours spent in training for its core activities of instruction and research.
In addition, business intelligence and analytics is an expensive undertaking. To begin with, organizations need to invest in the infrastructure to handle the information. According to Forbes (2015), American businesses spent over US$27 billion in 2014 and the figure was projected to reach US$82 billion in 2018. Forecasts for Calendar Year 2025 stand at US$122 billion; indicating that there will be a steady growth in the sector. The development of new tools for data management requires that organizations replace their software frequently, increasing costs. Training of the human resource to handle the business intelligence and analytics is another cost center that organizations cannot avoid. After every replacement of software, retraining of human resource becomes necessary. Higher education institutions suffer more because of the limited allocations from the federal government. Private institutions suffer equally because they cannot increase fees at will and the trust allocations are limited.
Higher education institutions collect data in different systems at any time. For instance, the student affairs department collects data from its social media account, caller details may be recorded in Excel spreadsheets and yet research reports and other departments may have their data in different formats. Analyzing and reporting this information in different formats and systems poses a major challenge to these institutions. Mobile data compounds this problem, as senior managers require data on their smartphones. Delivering the data onto their smartphones is problematic because of limited storage space and incompatibility of some data formats with the devices. Furthermore, capturing data from mobile devices (a major source of data) is still a major challenge. A cross-organizational view is necessary to make sound decisions that are congruent with the organization’s mission and vision (Sherman 2014).
Typically, an organization needs accurate data, as much of it as possible, delivered as quickly as it is generated and in the most usable format possible (Colier 2012). This problem is even more serious for educational institutions due to the scientific nature of research and fixed academic schedules. Business intelligence and analytic tools fall short of meeting the ideal desire of most organizations (Krishnan and Rogers 2014). The deficiency requires further human input to make effective use of the data and reports. No tool is available that meets the needs of organizations perfectly.
Benefits
The benefits of business intelligence and analytics are many. On of the benefits is that it improves business processes. When there is accurate data available regarding the way business processes operate, organizations are able to identify unnecessary stages and they can improve the remaining process (Burns 2016). For instance, New York University uses data warehousing to manage student details, grades, financial aid information and budgets of past years (NYU 2017). A data governance policy requires that all information regarding the operations of the university is stored in soft format to enable quick access and analysis. Care should be taken when improving processes not to eliminate actions that will compromise the overall mission and vision of the company in the name of improving efficiency and saving money.
Business intelligence improves the performance and productivity of employees. When the staff is aware of the business environment and what is working, they are able to concentrate their efforts towards actions that are more productive (Davenport and Harris 2007). They will also improve their performance by using processes that are more efficient. This benefit will however accrue if the employees are motivated to develop the organization. This calls on the organization to manage its human resource well and to avoid relying on business intelligence and analytics only to improve overall performance, as it is not a panacea.
The availability of data on student and employer feedback gives a complete picture of what the job market desires. It provides a clear glimpse on student needs and offers the institutions the opportunity to rectify their failures. Intelligence gathered from social media interaction can enable the institutions to serve its students better and to improve employee relations (Williams and Williams 2007). Business intelligence will also enable the instituions to streamline their work relations with other partners and stakeholders since it enables it to recognize the needs and trends of the industry.
Recommendations
The organization can take several actions to counter the challenges of business intelligence and analytics. First, for institutions of higher education, the organization should invest in the good tools that are competent in intelligence and analytics. The initial cost of these tools may be high but the saving in time and the academic environment picture that they provide will be worthwhile. The choice of the correct tools will however depend on the individual needs of the institutions. Large institutions competing globally will have different needs from small community colleges operating locally. The nature of the institution will also determine the type of tools it requires to collect its intelligence. Science and technology institutions will have different needs from those of institutions offering humanities.
Internal communication and interdepartmental liaison provides a good solution to the problem of cross-organizational collaboration. Providing open channels of communication opens up collaboration between departments and encourages upward and lateral communication. The institutions should build a culture of information sharing and teamwork so that all teaching and non-teaching staff and students work towards the achievement of the company’s vision. In this manner, data sharing between the institutional functions or units will make easy the work of data mining and analysis.
Last, the institutions should hire competent staff with the skills and knowledge to conduct business intelligence and analytics. Although they will need to retrain them from time to time, competent employees minimize the need for training and save them costs. A competent staff is also able to learn faster the needs of new analytic tools.
References
Burns, L. (2016). Growing business intelligence: An agile approach to leveraging data and analytics for maximum business value. Basking Ridge, NJ : Technics Publications.
Chen, H., Chiang, R.H.L., & Storey, V.C. (2012, December). Business intelligence analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188. Retrieved from http://s3.amazonaws.com/academia.edu.documents/32970305/FROM_BIG_DATA_TO_BIG_IMPACT.pdf?AWSAccessKeyId=AKIAJ56TQJRTWSMTNPEA&Expires=1484732814&Signature=8P9C87eTx4a2kQhIZQqzxkv0tAw%3D&response-content-disposition=inline%3B%20filename%3DSPECIAL_ISSUE_BUSINESS_INTELLIGENCE_RESE.pdf
Collier, K. (2012). Agile analytics: A value-driven approach to business intelligence and data warehousing. Upper Saddle River, NJ: Addison-Wesley.
Columbus, L. (2015, May 25). Roundup of analytics, big data & business intelligence forecasts and market estimates, 2015. Forbes. Retrieved from http://www.forbes.com/sites/louiscolumbus/2015/05/25/roundup-of-analytics-big-data-business-intelligence-forecasts-and-market-estimates-2015/#4924e7414869
Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Boston, Mass. : Harvard Business School Press
Domo. (2015). From big data to big decisions. Retrieved from https://www.domo.com/assets/downloads/15_bi-guide.pdf
Ernst & Young. (2014). Big data: Changing the way businesses compete and operate. Retrieved from http://www.ey.com/Publication/vwLUAssets/EY_-_Big_data:_changing_the_way_businesses_operate/%24FILE/EY-Insights-on-GRC-Big-data.pdf
Krishnan, K., & Rogers, S. P. (2014). Social data analytics: Collaboration for the enterprise. Amsterdam : Elsevier.
New Yoirk University. (2017). Information and analytics. Retrieved from https://www.nyu.edu/about/leadership-university-administration/office-of-the-president/office-of-the-executivevicepresident/program-services/analytics-and-information.html
Sherman, R. (2014). Business intelligence guidebook: From data integration to analytics. Amsterdam : Elsevier.
Williams, S., & Williams, N. (2007). The profit impact of business intelligence. Amsterdam: Elsevier/Morgan Kaufmann.
