Abstract
This paper examines the strategic influence of on the business strategic decision-making process. The proposed research will study a multinational company in the manufacturing industry for a period of 24 months. Action research (AR) will be used to make inquiries into the selected company and make conclusions based on the comparison previous results and recorded performance after the implementation of an integrated bottom to top analytic-strategic decision making model.
Introduction
Knowledge is power and the digital age has created an unprecedented quantity of data for conversion to knowledge. Digital data grows tenfold every five years yet a majority of business executives is still unable process accurate information that they can utilize in making strategic decisions. Business decision makers are basing their decisions on faulty, incomplete, or inconsistent information. This is not to say that the development of the digital age has not transformed business processes.
In the last 25 years, businesses have been moving from efficiency to effectiveness and recently, to business transformation. Effective business processes guarantee results as opposed to efficient systems, which may not produce the desired results. All levels of business from large corporations to small-scale start-ups are implementing customer relationship management (CRM) and enterprise resource planning (ERP) systems. These systems have allowed businesses to automate processes, reduce costs, increase speed of service, and improve the overall quality of business customer relationships.
Strategic management as a management role is central to the business planning process. Business analytic systems may not be as old as strategic management but they are viewed as critical to the accuracy and operational effectiveness of strategic business decisions. Business intelligence (BI) describes a wide range of applications, processes, and technologies that are used to gather, store, access and analyze data that is used to support business decision-making (Wixom & Watson 2010). Business analytics are widely used today in most middle and large-scale enterprises. These firms employ some form of business intelligence (BI) in their strategic decisions.
In theory, analytics afford managers and other front line decision maker’s pointers to opportunities, threats, and possible tactical and strategic moves in the future. In practice, few organizations reap the full benefits of analytics because of poor up take, excessive or conflicting raw data, and incomplete information. Additionally, some managers have developed apathy towards technology that seeks to replace their traditional role as the final decision makers.
The existing BI solutions are typically detached business planning systems. The metrics and charts from BI reports often have little correlation to the targets established in financial plans. The dynamic nature of the business environment is largely to blame for these disparities. Another key determinant is the human factor in decision-making. The implementation of strategic business decisions is left to middle level management for supervision and low-level staff for implementation. In the organizational hierarchy, middle level managers and low-level employees are seldom involved in the strategic decision making process. Hallikainen and Marjanovic (2012) found that m idle level managers could contribute creative and practical ideas to the strategy process. This paper explores the hierarchy further by examining the value that low level employees can contribute to the strategic decision making process. Low-level employees have experience that can be used to leverage BI for better analytics and business strategies.
Related Literature
This section will examine literature on business intelligence as it applies to the traditional top- down approach to strategic decision making. This information will then be used to make an argument for an integrated approach to decision making that involves all employees within an analytics based organization (Watson 2009b; Wixom & Watson 2010)
As technology spreads to all departments in organizations, BI is becoming attuned to business specific business related issues. An analytics based organization requires accurate and up to date information on the full spectrum of the business from inputs to outputs and the eventual feedback from these processes. Data on customers, suppliers, competitors, and employees are required to prepare effective BI tools. Management cannot claim to have access to accurate information from all levels of the organization. Even where an efficient feedback system exists, there could be issues of inaccurate interpretation, interference, and delays in reception (Bertram 2010). If this information is fed into BI technology, erroneous reports are produced which are in turn used in strategic decision making leading to wide spread organization underperformance or failure (Gartner 2009).
According to LaValle (2011 p23) senior executives utilize evidence based data driven decision models to run business. The error in this model is that it inelastic to small but significant changes in the microenvironment (Mc Nurlin & Sprague 2009). Firms should strive for democracy of information at all levels of the organization in order to fully utilize the benefits of business intelligence applications. Low-level employees can become powerful decision makers by contributing their first hand insight to the strategic decision making process. The vertical free flow of information and decision making ability converts BI analytical ability from aspirational to transformational (Gartner 2009). The three stages of analytical adoption are aspirational, experienced, and transformed. In a study conducted on over 3000 participants in 30 industries worldwide, La Valle et al. (2011) found that companies in each of the stages had different uses of strategies in their day-to-day operations. Aspirational organizations had little or no use of insights in their future strategies or daily operations. Experienced companies demonstrated a marked increase in the use of insights in strategic decision making but had limited use for insights in daily operations. Lastly, transformed companies employed insights for both future strategies and daily operations.
A lot remains to be done in the study of BI research especially focused studies on specific industries. There is widespread consensus among scholars that high level BI management requires five key components in order to fully function. These are performance, people, platform, process, and strategy (La Valle et al 2011). For BI to be fully effective, the individuals who create BI systems must align the daily operations of businesses with the strategic business component. Low-level employees carry out the daily operation of the business while top-level managers traditionally perform the strategic component. This paper will make a case for the active inclusion of low-level employees to create transformed companies.
Research Design
This study will use action research (AR) as the main research method. AR is appropriate for this study because it is effective in finding solutions for current problems while building on existing scientific knowledge. AR is often applied using action learning cycles (Bradbury & Reason 2003).
The AR learning cycles involves five stages: diagnosing, planning, acting, evaluating and reflecting on the action taken. AR is highly intertwined with practice because researchers often double up as industry practitioners or they work closely with practitioners during the research. AR is primarily conducted through active participation as opposed to observation. AR will produce valuable insights into the evolution of strategic influences and the role of low-level employees in the BI unit in relation to the overall organization.
The study will be conducted in a multinational company preferably one dealing in manufacturing for two financial years (24 months). The choice of a multinational company will bequeath an international spectrum to the study. Furthermore, multinational have rigid departments and roles multiplied across its branches, which use top-down decision-making models. They also have centralized reporting systems and efficient reporting systems.
The first half of the study will be conducted in the first four quarters of the fiscal year. At this stage, the researcher will collect sales and marketing data from primary sources (low-level staff in global branches) and secondary sources (Industry competitors). The data will then be converted to information using BI technologies. The information will be used in the second half of the study where action will be taken based on the information received from low-level employees. Evaluations and reflections will be made at the end of the study to establish the impact of utilizing strategic solutions made from low-level employee contribution to analytic strategic decision-making compared to the previous year(s).
Conclusion
A well-rounded analytics- based organization promotes the integration between business intelligence systems and overall organizational strategy. To survive in an increasingly competitive business environment, businesses come up with innovative products and services to create a competitive edge over their competitors. Watson (2008) acknowledged the importance of the alignment of business goals with BI outcomes especially during the strategic decision making process. It is for this reason that all tiers in the organizational hierarchy should be involved in the strategic decision making process. Low-level employees are particularly sensitive to daily changes that can affect the overall strategic direction of the business. If low-level employees are actively involved in analytics based management, they can contribute appropriate and updated information leading to transformed companies.
References
Bertam, I. (2010) “The next stage” Managing Information Strategies, MIS Australia: 05, 2010
Bradbury, H., and Reason, P. (2003) “Action research and opportunity for revitalizing research purpose and practices, “Qualitative Social Work (2:2), pp 155-175.
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Hawking, P., and Sellitto, C. (2010). “Business Intelligence (BI) Success Factors,” ACIS 21, Brisbane: AIS.
LaValle, S., Lesser, E., Shockley, R., Hopkins, M., S., and Kruschwitz, N. (2011). “ Special Report: analytics and new path to value, “ MIT Sloan Management Review : Winter 2011, pp 22-32.
McNurlin, B.C., and Sprague, R.H.J (2009). Information Systems Management in Practise, (8 ed). Upper Saddle River, New Jersey Pearson Education, Inc.
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Watson, H.J (2009). “Tutorial: Business Intelligence- Past, present and future,” Communications of the Association for Information Systems (25:39).
Wixom, B., and Watson, H. (2010). “ The Bi-Based Organisation, “International Journal of Business Intelligence Research (1:1), pp 13-28.
