Research Proposal
Impact of Business Intelligence and Data Analytics on Business performance & Improvement
Section 1: Research Topic, the Problem, and its Setting
1. Statement of the Problem and Sub-Problems
Business intelligence and data analytics are two modern tools for business management. Problem: Every business has to make different decisions to take different business benefits. However, business management needs the right information at the right time along with strong managerial cognition to use this information and make effective decisions. In the contemporary business era, business management usually contains lots of data but no real information. Without real information, it seems tough for business management to make effective and lucrative decisions in the best interest of the organization. Normally, firms use limited information from spreadsheets or other small sources. They do not realize that data is too big to make decisions. Thus, this problem is associated with the data realization that can help companies gain long-term success.
- Understanding a large amount of data is tough for the business. It seems tough for the company to see the big picture regarding the business information. Breaking information or data is a key challenge for companies to derive some useful insights and accelerate the decision-making process.
- It seems difficult to make strategic and tactical decisions at the same time. The firm needs a database to make these strategic and tactical decisions.
2. Hypotheses
Business intelligence and data analytics help companies improve the business process and promote innovative culture, which provides a lead to overall business improvement
3. The Delimitations
There are several research delimitations. For Instance, this research study is not industry specific. The research does not investigate any company top examine the role of data analytics and business intelligence.
4. Definitions of Terms
Business intelligence:
Business intelligence is a process to examine or evaluate the data and derive actionable information. The actionable information is to be used by business professionals, managers or executives to make effective decisions.
Data Analytics:
Data analytics is a process of examining and transforming data to discover useful information and support the decision-making process in the company.
5. Importance of study (Background and Motivation)
In the contemporary business era, the effective and rational decision-making process has become a vital tool for management. Organizations, through the right information and execution of the data, can make the right decision at the right time. For both internal and external business processes, business intelligence and data analytics are two major tools to improve overall business performance. Many small companies use business intelligence and data analytics to make effective decisions and sustain the competitive advantage. The motivation comes from the assertiveness of both small and large organizations when using business intelligence and data analytics. The advantages or benefits are more than disadvantages or negatives, and it motivates us to explore related ideas in this particular research process.
Section 2: The Literature Review
According to Tarek & Adel, business intelligence helps managers, executives, and many other business leaders derive timely information to improve business performance. Accurate and well-timed business decisions are one of the prominent business advantages. Business intelligence is a kind of data investigation to obtain suitable information to make decisions. Interestingly, the decision is less risky as compared to the traditional decision-making method. The business, from retail to manufacturing, cannot afford any guess in the decision-making process (Tarek & Adel, 2016). Business intelligence has emerged as a powerful tool. Business intelligence helps business experts or professionals to anticipate business changes and make timely decisions. It is an initial phase of improving business performance. The firm has to replace technologies and processes with new ones through intelligence. The manager can improve the cognition and contribute in the business performance and improvements.
By analyzing the data through business intelligence, the management can navigate potential opportunities that were missed in the past. Many organizations use business intelligence tools to find opportunities for the future and make effective comparisons. It can be said that the company can use these tools to obtain some predictable outputs, which are favorable for business performance. However, Tarek & Adel examines business intelligence only from the perspective of entrepreneurs. The difference between entrepreneur competitive intelligence and business intelligence is big. It keeps the concept of business intelligence limited, and actually, it is quite wide
According to Amiri, Shirkavand, Chalak, & Rezai, 2017, many companies are gaining and sustaining the competitive advantage through data-driven business intelligence. Part of the decision-making process, the company can improve financial performance. Business intelligence is in the limelight due to its impact on business performance. For Instance, at the production level, data-driven intelligence can help to improve productivity, increase efficiency, and identify the strengths and weakness of employees. Now, business intelligence is to be combined with data analytics. Personalization is a need of the business to deal with customers at the individual level. Big data, a tool of data analytics, is helping firms to contain the personalization. According to Amiri, Shirkavand, Chalak, & Rezaeei, dealing with the customers according to personalities is a modern approach to make customers loyal and grab new range. It is also a competitive factor that opens ways for a competitive position and effective business performance. Management is applying analytics in designing the workplace or work culture to improve effectiveness and efficiency (Amiri, Shirkavand, Chalak, & Rezaeei, 2017). On the other hand, the firm can integrate with new market trends to mold the production process, assess customer behavior, and streamline product preferences. For Instance, starting from the sales department, the firm can provide sales staff these BI tools to derive customer behavior, trends, and intentions in the market. On the other hand, internally, salespeople can also identify or navigate different performance measures to improve performance. Thus, relying on the data is relying on accuracy or authenticity, and it is a source of improvement. (Amiri, Shirkavand, Chalak, & Rezaeei, 2017). However, authors have tried to elaborate the business intelligence regarding the sustainable competitive advantage. The research could emphasize the process of business intelligence, phases, and evolvement to demonstrate the impact on the competitive advantage. Just elaborating and inter-relating the sustainable competitive advantage with the business intelligence can only indicate the existence, not impact.
According to Eidizadeh, Salehzadeh, & Chitsaz Esfahani, It has been observed that small and large organizations are looking to improve the knowledge sharing process to make intelligent. In key business activities, the management usually promotes knowledge and changes the mode of the decision maker. Interestingly, business intelligence tools are effective knowledge sharing enablers for employees in different departments, and it is an ultimate strategy to improve business performance. The most important thing for the company is to build business intelligence systems to store a large amount of data or information. Based on the data, the firms usually anticipate some needs. It depends on the level or quality of data that streamlines major areas to improve in the business. Obviously, business improvements in the innovative culture can produce predictable results.
Remarkably, the business management can retrieve information regarding the competitive activities in the past, modern technology, and the internal capabilities of the company. Thus, based on this information, the performance can be improved regarding new product development, business transformation, and many other key business tactics. With the passage of time, business intelligence has become a major competitive tool for businesses. For Instance, the firm can make different competitive strategies to get an edge over other competitors. However, modern organizations are using business intelligence tools to understand the competition in the competitive markets (Eidizadeh, Salehzadeh, & Chitsaz Esfahani, 2017). But, beyond several factors indicated in this source, the knowledge sharing could have been explained along with some measures. For Instance, the levels of business intelligence have not been shown. The organization needs certain levels of EI to enhance knowledge sharing and the impact on the competitive advantage and business performance.
According to Amiri, Shirkavand, Chalak, & Rezai, business intelligence can also be used as a competitive intelligence in the universal market. Competitive intelligence is the other dimension of business intelligence to increase the innovation capability and get an edge over rivals in the competitive landscape. The management of the company usually gives importance to the information which is obtained from competitive intelligence. In the research and development process, using informal and formal methods to retrieve information and utilizing it in the business process is a good approach. Through the data-driven intelligence (Business Intelligence) the firm must predict what outcomes it is possible to obtain to get a competitive advantage. If the work is delegated by the management to another key stakeholder, more information can be obtained (Amiri, Shirkavand, Chalak, & Rezaeei, 2017). Well, beyond the competitive advantage or sustainable competitive advantage, the author missed illustrating some examples of competitive intelligence technique. In modern business, companies usually take advantage through implementing these competitive techniques. This research source only examines the concept of competitive intelligence with one or two strategies. Some advanced techniques such as industry cost curve or ADL matrix could make the concept of competitive intelligence clear along with the impact on business performance.
Amiri, Shirkavand, Chalak, & Rezaeei, managers in an organization usually identify the needs of intelligence to shape the work and accordingly, lead and control the process. Business intelligence tools are purposeful in organizations, and it can be understood by examining the conceptual model. In this modern business era, firms are using business intelligence tools to identify the changing needs and process awareness. Business managers want to obtain information that can help to improve business performance. The BI tools help to finalize the information that is enough to create urgency in the company. Based on the BI information, planning and focusing on different tasks become easy. The focus of the firm is on the pertinent information gathering and analyzing the possible impact on business performance. (Amiri, Shirkavand, Chalak, & Rezaeei, 2017). Besides, it has been observed that competitive intelligence in a diverse culture. For Instance, the research only contains competitive intelligence with the perspective of customers. When conducting business in different countries, it is important for the company to contain emotional and sentimental attachments with people. Of course, the cross-culture strategy needs data. Adopting the cross-cultural strategy is actually a road towards business improvement and growth. However, the concept of cross-cultural competitive intelligence is more viable. Thus, the cross-cultural competitive intelligence is missing that makes the business intelligence isolated in the business environment.
According to Hocevar & Jaklic, business intelligence is a comprehensive concept, and it has become the backbone of business. The firm has to develop business intelligence systems to acquire useful information to make decisions. An example of the business intelligence system is online analytical processing and many other data mining tools. BI systems are alternatives to the classical or traditional information system for firms. The business intelligence system is not a single application. Different components are triggered by these systems to choose and examine data and make several aggregations. They can display the results, which can be navigated or understood easily. No doubt, the BI systems are helpful in gaining competitive advantage. However, integration with these systems must be aligned with the strategic directions or goals of the organization (Hocevar & Jaklic, 2010). Well, examining the business intelligence regarding its advantages looks incomplete. The research has shown the use of historical data as a key to business intelligence. It is not only the single factor indeed. Instead of focusing on historical data, business professionals are focused on forecasting to make strategies. Sometimes, business intelligence seems complex due to intricate data. The research says that useful information can be derived. However, it does not explain the possible complexity that can make business techniques rigid.
Ultimately, business expansion is also a competitive edge in the global markets. Also, data quality management is also a key trend to justify the importance and effectiveness of business intelligence in the business world. Data acquisitions, advanced data processes, effective distribution and effective oversighting of the data are the main elements of the data quality management process. When retrieving information from countless data sources, data quality management applies to find fine information, which can be used for competitive strategies. In business improvement, data governance is also a key trend. Firms are moving forward with effective business intelligence, and data accessibility and security is highly recommended. For Instance, the firm must have access to the right information by some authorized people. Concerning some cybersecurity attacks, data can be stolen, and the firm may lose the chance or get a competitive advantage. Business intelligence is a key tool for business to find the right direction in the competitive landscape (Hocevar & Jaklic, 2010). Gaining the sustainable competitive advantage due to business intelligence is viable. But the research does not disclose the fact that business executives or experts usually draw different conclusions from the same data. The research is just focusing on the benefits of business intelligence. The impact of different conclusions may lead towards business disadvantages as well. Instead of emphasizing on the single factor, two-sided arguments could explain the concept effectively.
Data analytics, on the other hand, is a process of examining the information to obtain some meaningful insight. According to Ransbotham, Kiron, & Prentice, Minding the Analytics Gap, examining data and insights help the company improve many things. The use of data analytics is beneficial for the business to improve customer relations. Through technology, companies gather customer data to identify trends, behavior, and needs. Knowing the customer needs and enhancing the seamless experience at different tough points has become the main benefit of data analytics (Prentice, 2015). Prentice, on the other hand, struggles to view the big picture. A business professional always finds a gap between business information and business performance. The trends of customers and the market are changing dramatically. It increases the differences between business information and software applications. The research only explores gaps but does not indicate several business requirements, which are not met by the management. Minding the analytics gap is not just identifying the gap, as it is related to missing business requirements.
However, the company has to implement or execute some critical steps when applying data analytics. The first step in the data analytics process is defining the objective. It is important to investigate what is to be achieved to get the expected outcomes. When navigating the countless data, it seems imperative to know what data can give. Applying data analytics is a key to implementing data analytics successfully. Amazon, a top retail giant in the global retail industry, is using data analytics to integrate with key customer insights and make decisions or strategies. Business performance is possible by improving the supply chain process through data analytics. The supply chain management usually makes effective decisions regarding depending on forecasting and procurement with the help of data (Patil, 2018). Additional perspectives regarding the supply chain executives could present better results in this study. Merging and optimizing the supply chain or delivery networks are two major elements of the modern supply chain. Tools have not been described by the author, which are based on big data, to develop the supply chain. Business evolvement is a need of the business. Evolvement or change with time is another dimension of business growth and improvement.
According to Nywlt & Grigutsch, in the business world, change is mandatory with the passage of time. However, companies are using data analytics to change the business process. The relevant product delivery is possible now on the market based on the latest trends and needs of customers; the firm can increase the innovative capacity and service or product quality. It is based on the data or information by using different data analytics tools. In the digital world, business intelligence is a prominent factor (Nywlt & Grigutsch, 2015). Nywlt & Grigutsch only explores the supply chain process regarding the efficiency of the delivery system. It is more than the delivery efficiency. The author could tell how the data analytics helped supply chain management create different measures to reduce the freight cost
Advanced analytical techniques or traits have been developed by companies to improve some field operations. Customer service, production, marketing, and many other segments can be improved by using the data in the decision-making process. Some fantastic techniques in data analytics are workable for businesses. Business experts have increased the use of data visualization. They are now able to grasp the difficult concepts or insights. Identification of new patterns of information and finding the new variables are possible due to data analytics. The example of the customer service department is relevant to justify the importance of visualization. In a visual, the customer service manager can find or navigate the impact on the customer buying behavior. The customer buying behavior is variable that can be changed due to spikes in the customer service trends (Nywlt & Grigutsch, 2015).
The data visualization tells managers what traits or strategies are creating a positive or negative impact and based on this information, some lucrative decisions are to be made. Data analytics are related to data diversity as well. Many firms are using data diversity, originating from data analytics to run business operations. Combining data from different sources and relating variables is effective. If a firm is combining data regarding the sales, climate, and society, it can initiate the marketing campaign in a successful manner (Lou, 2017).
Some advanced analytics are quite visible in industries. Firms are relying on data and move towards prescriptive analytics instead of descriptive analytics. It is important to use advanced analytics tools to transform the business and make it relevant for long-term sustainability, and obviously, the sustainable competitive advantage is the main priority. Business forecasting is important to emerge stronger. From the supply chain process to product delivery, the firm can use or adopt modern data analytics tools such as big data to anticipate future business needs and maintain excellent business performance. It depends on the nature of the business and intentions or objectivity of the company to adapt the type of data analytics. According to Norbert, Julia, Stephen, & Miklos, it should be purposeful and relevant to the strategic direction of the company. Evolvement with data analytics is essential. The firm has to estimate or predict the usability or workability of data to make competitive decisions (Norbert, Julia, Stephen, & Miklos, 2016). The next frontier in data analytics has been explained well, but future trends are emerging. The research successfully described different future aspects. However, metadata concept could improve this perspective. Recently, the proliferation of Metadata emerged, pushing old techniques to perceive data. Norbert, Julia, Stephen, & Miklos just normalized existing trends in the study, and it indicates a huge gap.
From 2013 to onward research studies indicate the behavior of companies regarding data analytics adaptation. It has been observed that firms existing in different industries are investing in data analytics to gain a competitive advantage. The data analytics have increased the innovation surge in the industries. Old and new firms are investing in data analytics to increase their analytical capabilities (Amiri, Shirkavand, Chalak, & Rezaeei, 2017).
The purpose is to increase innovation capability. In existing and new procedures of the business, data analytics have increased the ability to innovate. Firms manage or effectively maintain the data. Governing the data has enabled timely decisions and right innovation at the right time, and it is quite beneficial in the competitive market. In the data analytics process, companies are enabling strong data governance and data sharing. It is ensuring the exploitation of new ideas, thoughts, opinions, and observations to drive innovation. Business professionals are using smart machines to discern new information patterns to spend more time making strategic decisions. From 2013 to 2015, many retail and manufacturing firms are reporting competitive advantages through data analytics. Designing attractive marketing and production strategies to get the edge is not enough now. It is difficult to get a competitive position without having command of the data. The early adopters of data analytics can get an early advantage (Prentice, 2015).
Interestingly, initial adopters have used data analytics as a learning engine, and it has also been reflected in their business strategies and returns. The workability of analytics and business intelligence can be observed when concentrating on analytics to deal with different business issues. To tackle several business issues such as product failure, poor communication, and corporate frauds, the data-driven approach is the best solution for companies to find some measurable results. In the internal business environment, some issues are employee behavior, training, and development, supply chain, process design, and compensation. Through data-driven approaches, it seems easy for the company to make pertinent adjustments. Amazon, Google, IBM, General Electric, and Uber have created innovative culture to improve the ability to get the advantage. Professionals in the business industries are admiring the existence of data analytics (Prentice, 2015).
From early adopter analytical maturity, data analytics has become a part of the favorable business performance. For Instance, analytical innovators are visible in industries, which contain the analytics culture. The management of these companies relies on data analytics to make rational decisions and streamline some key strategic insights and innovative ideas. Analytical practitioners in industries are emerging with some improved capabilities. The professional in this company has always intended to have adequate access to the data. These organizations are developing or transforming the process to become more data-driven and make data-driven decisions. The main aim of these companies is to contain operational improvements by using the data with the passage of time. The third level is the company, which is analytically challenged. As mentioned above, there are many firms, which are still depending on management skills or intuition. Management only makes decisions without any data. Therefore, they lack quality and further operational performance.
According to Ransbotham & Kiron, integrating with data analytics and business intelligence has increased the moderate innovation in different business sections. Different business sections such as customer service, finance, human resource administration, supply chain, marketing, operation, product development, risk management, sales, information technology, and research and development are experiencing the moderate innovation. The modern firm intends to contain the high ability to innovate. However, the high ability to innovate depends on the data sharing in the internal business environment. If a firm is using big data as a key data analytics tool, it has to share the information with all key stakeholders or department to make some changes or adjustment in an innovative culture. It has been revealed that organizations are sharing almost 80% of data with the internal stakeholder. It is a process of making the internal force innovative to get an edge in the intense rivalry (Ransbotham & Kiron, 2017). Companies cannot just initiate innovation by using data. Strategic thinking is a major element in innovation. Some other innovative techniques such as work freedom, allocation of resources, and building an innovative culture are workable for the firm. Many firms, without any data analytics tools, have built an innovative culture. Now, the evolvement of data tools in the business culture to drive innovation is to be explained to understand the concept.
According to Norbert, Julia, Stephen, & Miklos, depending on the business nature, the management can use different types of data analytics. It depends on the type of improvements or improvements in different departments. For instance, if the firm wants to improve the sales process, it can retrieve the sales data. Similarly, relevant data will be used to justify relevant business growth and performance. For Instance, an organization which contains strong business intelligence may use descriptive analytics to navigate the past information and compare it with the current business situation. Professionals in some companies, due to their critical behavior, use diagnostic analytics to navigate the causes of past results. Based on this information, they can make better decisions to derive better outcomes than in the past. In predictive analytics, to be used in the modern organization, the management can identify some key information patterns to navigate the historical data. They want to predict the future based on historical data.
Now, in the current business environment, the firms are increasing the prescriptive analytics to navigate best alternatives to gain desired outcomes or results. The optimization of several techniques and machine learning are major elements in prescriptive analytics. Now, research shows that many organizations are moving towards prescriptive analytics to find the best opportunities to expand the business, enhance innovation, and gain a competitive advantage. However, there is a need to develop the analytical mindset in the internal business environment to use prescriptive data analytics. People have to develop the necessary analytical skills to increase cognition and make competitive decisions to contribute to the competitive advantage (Norbert, Julia, Stephen, & Miklos, 2016).
Section 3: Conclusions/Suggestions
Business intelligence and data analytics have helped companies to create innovative culture and gain a competitive advantage in the competitive industries. Business is becoming data-oriented, and all key business decisions are based on the data or information, derived from different BI and DA tools. The decision-making process needs the accuracy of the data, which helps to make an effective or rational decision. Studying or conducting research regarding data analytics and business intelligence is important due to the adaptation process of many organizations. Modern business approaches such as artificial intelligence, personalization, and machine learning are associated with business intelligence and data analytics. Finding the information and breaking it to obtain meaningful insights is a real problem for the businesses. Now, this research has indicated the help of business intelligence and data analytics to make improvements and justify business performance.
The literature has highlighted the role of business intelligence and data analytics in business operations. The knowledge sharing process has been improved in many companies by using these tools. Business professionals are looking to differentiate them by adopting data-driven approaches. The increase in innovative capability and competitive advantage can be observed in many industries. Data analytics and business intelligence have become the backbone to get a sustainable competitive advantage and overall business sustainability and relevancy.
The major strength of this research study is a comprehensive analysis of these two terms. The research study streamlines the impact on business performance, strategies, process improvements, and the decision-making process. Some examples, types, and the adaptation process have also been illustrated to enhance the understanding of the importance of business intelligence and data analytics. Conversely, the major weakness of the research study is that it is not a company or industry specific. It seems general research, which is to be applied to all companies or industries. The real business case study could improve the whole research study and help to understand the implications. However, it is still an effective analysis, and many insights and new ideas can be retrieved.
In my opinion, business intelligence and data analytics can help both in large and small companies to create innovative culture and get the sustainable advantage over other firms. Based on knowledge, the firm can be in a good position to think differently and streamline new ideas. Viewing the data or information differently is a new tactic that can assist to enter or exist in the market differently. Instead of focusing on predictions or guesses, relying on the data is the best option for the company to drive both internal and external business success. Being an entrepreneur or a business expert, I would like to adopt a data-driven approach to make the process different from other to predict different outputs. However, evolvements or changes with time must be considered to be functional and relevant in the spirited landscape.
Section 4: References
Amiri, N. S., Shirkavand, S., Chalak, M., & Rezaeei, N. (2017). Competitive intelligence and developing sustainable competitive advantage/la inteligencia competitiva y el desarrollo de una ventaja competitiva sostenible. Ad-minister, 30, 173-194.
Eidizadeh, R., Salehzadeh, R., & Chitsaz Esfahani, A. (2017). Analyzing the role of business intelligence, knowledge sharing and organizational innovation on gaining competitive advantage. Journal of Workplace Learning, 29(4), 250-267.
Hocevar, B., & Jaklic, J. (2010). Assessing Benefits Of Business Intelligence Systems – A Case Study. Management: Journal of Contemporary Management Issues, 15(1), 87-119.
Lou, S. (2017). Applying Data Analytics to Social Media Advertising: A Twitter Advertising Campaign Case Study. Journal of Advertising Education, 21(1), 26-32,4.
Norbert, T., Julia, K., Stephen, K., & Miklos, V. (2016). The next frontier in data analytics. Journal of Accountancy, 222(2), 58-63.
Nywlt, J., & Grigutsch, M. (2015). Big Data Analytics Based on Logistical Models. Journal of Centrum Cathedra, 8(1), 57-62.
Patil, S. (2018). Data Analytics and Supply Chain Decisions. Supply Chain Pulse, 8(1), 29-32.
Prentice, P. K. (2015). Minding the Analytics Gap. MIT Sloan Management Review, 56(3), 63-68.
Ransbotham, S., & Kiron, D. (2017). Analytics as a Source of Business Innovation. MIT Sloan Management Review, 58(3).
Tarek, B. H., & Adel, G. (2016). Business Intelligence versus Entrepreneurial Competitive Intelligence and International Competitiveness of North African SMEs. Journal of International Entrepreneurship, 14(4), 539-561.
Section 5: Appendix
Chosen References to Repeat and Extend
- The next frontier in data analytics
Norbert, T., Julia, K., Stephen, K., & Miklos, V. (2016). The next frontier in data analytics. Journal of Accountancy, 222(2), 58-63.
- Analytics as a Source of Business Innovation
Ransbotham, S., & Kiron, D. (2017). Analytics as a Source of Business Innovation. MIT Sloan Management Review, 58(3).
- Assessing Benefits of Business Intelligence Systems – A Case Study
Hocevar, B., & Jaklic, J. (2010). Assessing Benefits Of Business Intelligence Systems – A Case Study. Management: Journal of Contemporary Management Issues, 15(1), 87-119.
- Competitive intelligence and developing sustainable competitive advantage
Amiri, N. S., Shirkavand, S., Chalak, M., & Rezaeei, N. (2017). Competitive intelligence and developing sustainable competitive advantage/la inteligencia competitiva y el desarrollo de una ventaja competitiva sostenible. Ad-minister, 30, 173-194.
- Business Intelligence versus Entrepreneurial Competitive Intelligence and International Competitiveness of North African SMEs
Tarek, B. H., & Adel, G. (2016). Business Intelligence versus Entrepreneurial Competitive Intelligence and International Competitiveness of North African SMEs. Journal of International Entrepreneurship, 14(4), 539-561.
Research Strategy
The research strategy in this research process was using the qualitative approach. Navigating previous studies on business intelligence and data analytics was a successful approach to obtain an immense range of information and illustrate the argument. Many case studies of organizations are navigated. Viewing scholarly articles was part of the strategy, and these are main sources of information.
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