Using Big Data and Other It Resources to Formulate Basic Strategy

Introduction

Big data and other IT resources are increasingly being used by many organizations to enhance business efficiency. Organizations use the two resources for strategic functions. They also use them for competitive reasons. However, there is a minimal focus on how Databases and other IT resources can be utilized to formulate a firm’s basic strategy. It is important to recognize that these resources are vital in the formulation of organizational strategies. Databases and other IT resources can be utilized to provide vital information critical to the formulation of basic strategies. This essay provides a critical argument and rationale why Databases and IT resources are used to formulate basic strategies for businesses.

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Using Big Data and Other IT Resources to Formulate Basic Strategy

The organizational need for Databases and IT resources is dynamic and cannot be predicted easily. Database applications keep on evolving thus changing their roles every single day. Their evolution and other technological resources are used to formulate simple strategies for organizations. There are several reasons behind this argument.

Internet-hosted databases have become phenomenal in organizational decision-making (Mahdi & Alreshaid, 2005). For instance, Decision Support Systems (DSS) software has enabled many companies to utilize database information in the decision-making process. DSS software comes with analytical and report features that enable decision-makers to translate data into useful information. This information is utilized in the decision-making process (Mahdi & Alreshaid, 2005). The information gathered by an organization from such sources is used in the formulation of basic organizational strategies that anchor a corporate-level strategy.

It is important to recognize that effective decision-making requires simple and elaborate information. Complex information is unsuitable in solving complex problems. Therefore, complex problems should be reduced into simpler problems so that reliable solutions are designed for the problem. DSS Analytical Hierarchy Process (AHP) technique enables users to break down complex problems into simple optional tasks (Mahdi & Alreshaid, 2005). After this, a quick decision is made between the options hence facilitating the formulation of simple strategies that provide solutions to complex problems. One example of a business that recorded improved decision-making after using the DSS software is Payless Cashways. DSS enabled this organization to formulate basic warehousing, marketing, and staffing strategies (Kansas City Business Journal, 1999). Payless Cashways used DSS to extract information on sales, stores, and staff productivity in the formulation of the strategies.

Big Data Systems and business analytics are used to push the decision making process, formulate strategies that are productive, and that guarantee returns on investment (LaValle, Lesser, Shockley, Hopkins & Kruschwitz, 2011). Big Data Systems allow businesses to collect data across its core functional units. Moreover, it helps businesses to collect reliable information about its customers and partners in the market. The data collected from these sources is used as a core element of the company strategy.

Businesses are bound to change if they utilize Big Data Systems. They are capable of transforming what is considered as state-of-art databases. They introduce real-time personalization of business operations, allowing customer-oriented companies to track individual customer behaviors and preferences from the internet applications such as web, social media, and blogs. This enables companies formulate basic sales strategies through bundling customer preferred products and designing of promotional and reward programs. In addition, big data alongside the growing applications of data through social network’s conversations, internet purchases, and smartphone applications, are useful in dividing customers into micro-segments (LaValle, Lesser, Shockley, Hopkins & Kruschwitz, 2011). In the end, a company can use the data together with the analytical tools to formulate strategies that target these micro-segments.

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Automated Big Data analysis allows data mining for many companies. For example, retailers use big data “sentimental analysis” techniques to mine consumer data streams from social media platforms. More so, it allows retailers to gauge marketing campaigns in real time. Therefore, retailers can use the data generated through sentimental analysis to adjust its strategies accordingly (LaValle, Lesser, Shockley, Hopkins & Kruschwitz, 2011).

Big Data Systems are also employed to solve technical problems (Russom, 2011). The traditional databases are slow with limited scalability (Webster, 2011). This makes enterprises to fail to afford or manage data collection due to strained budgets. There is an urgent need to develop greater storage capacities, increase speed, and intelligence. However, Big Data Systems present business opportunities for these enterprises. The Big Data Systems analytics have the ability to converge structured and unstructured data from multiple data sources (Webster, 2011). Furthermore, they are capable of handling big data sets than ever before. On top of this, they provide a gigantic statistical sample, which after being analyzed offers reliable results that give intelligence and customer-insight information to enterprises (Russom, 2011). This information can then be utilized in business decision-making processes. For instance, information about enterprise intelligent can be used to formulate customer-base segmentation and business turnaround strategies to cope with dynamic business environments.

Big Data Systems offer a big deal and opportunities to companies. Traditional warehousing is relatively slow to produce information for the users utilizing the information. In addition, traditional warehousing, though large, has limited sources of data, and hence cannot cope up with the current demands of quick and easy access to information. Segments such as MapReduce, Real-time stream processing, scalable database, and big data appliances represent a huge opportunity to companies (Webster, 2011). These analytics tools parse web data and extract it from several sources such as the social networks and the internet to generate transient streams of data. These streams of data in real-time can be used to draw important correlations, allowing decision makers to understand the current position of their businesses. Furthermore, it allows users to know how the business is performing, and put important measures in place to enhance the performance (Webster, 2011). Therefore, it is critical to acknowledge that multi-sources of information present diverse alternatives in decision-making that enable an enterprise formulate the best strategies.

Big Data Systems enable analytics to be used as strategic assets of the company (Kiron & Shockley, 2011). On top of this, it influences company managements to support analytics to ensure information availability in the organization. This creates a data-oriented organizational culture that recognizes the importance of data to everyday performances of an organization. Big Data Systems act as foundations of analytic competence and information management expertise (Kiron & Shockley, 2011). The technology builds analytical expertise. Data management practices and the ability to deliver the right information, to the right individuals, and at the right time also builds information management expertise. A data-oriented organization is able to support analytics and utilize analytic informative insights in formulating a business strategy (Kiron & Shockley, 2011).

Today, IT resources play a critical role in the decision making processes of many organizations. For instance, Enterprises Resource Planning (ERP) software integrates multiple data sources and processes, enabling increased information flow to managers in real time (Nobel, 2010). From a common database and software, managers are able to access and retrieve information on a wide range of business function like production, inventory, and sales. This enables managers at all levels in the organization hierarchy to access the information and formulate business-unit level strategies without necessarily consulting their seniors (Nobel, 2010).

Hayles (2007) is of the opinion that when aligned with business strategies, IT is of significant benefits to firms. The author argues against compartmentalization of business units, and holds that understanding how IT works helps avoid duplication of work. This is achieved through the provision of a shared infrastructure across all business units of enterprise. He acknowledges that proper planning ensures that technologies play a strategic role in understanding the business environment. Business strategies serve as an input on the IT strategy and business system plan. Through IT strategy, approvals and priorities can be generated out of the business strategy. In the end, an enterprise is able to formulate basic strategies and efficiently deliver products and services to its customers (Hayles, 2007).

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The above discussion provides an analysis of the strategic opportunities presented by Big Data Systems to organizations. The use of these systems and analytic techniques in organization strategic management is increasing at a very fast rate. However, they are not free from complications. This clearly points out that the Big Data Systems’ contribution to strategic decision making can be also be negated.

First, there are a number of analytical challenges that are associated with the use of these systems. It is critical to note that strategic decisions need to be evidence-based. Although these analytical challenges cannot be separated, summarizing data, interpretation, and anomaly detection among others are challenges attributed to Big Data Systems (UN Global Pulse, 2012). Summarizing data from people’s opinion on multiple digital sources of data can be biased, unverifiable, hypothetical, and of no actual intent. These illustrate one of the weaknesses of the Bug Data Systems. Interpreting data is another dimension that renders big data challenge in the decision making process. Although the data may be considered accurate, its interpretation may not be straight forward. This is attributed to sampling bias of the digital media used to collect the data. In addition, the data is hard data based on non-representational samples, and there is a risk that the massive data may raise judgments based on the data without understanding its dynamics (UN Global Pulse, 2012). Relationships drawn from the analysis may mislead strategic decision-making process, hence formulate malfunction strategies.

Privacy of data is another important challenge for big data. Linking data from multiple sources provides access to personal information of individuals. Sharing of personal information with minimal disclosure is still a concern in data mining (UN Global Pulse, 2012). There is free access to private information from digital sources, and organizations utilizing this data may infringe on the privacy of private privacy. In the end, private information compromises the quality of strategies formulated.

Big Data Systems analysis produces homogenous and incomplete data that is characterized by anomalies (UN Global Pulse, 2012). There is no measure that identifies these anomalies. It lacks the human sensitivity abilities to detect these anomalies and specify relevant cases that are critical in strategic decision making. Lack of sensitivity creates false positives from the data while the inability to specify relevant cases from the data fails to detect errors. Therefore, a source of biased data arises (UN Global Pulse, 2012). These errors make the detection of data anomalies or malfunction undesirable. They also render the credibility and the relevance of data generated from digital sources. Strategic decisions that are based on this type of data may create strategies that malfunction, and hence compromises achievements of organizational objectives.

Despite the fact that big data has a number of shortcomings, it offers a good innovation through which organization leverage strategic decision making processes. Digital data allow rapid data collection that enables users understand how to formulate strategies in real time. Data is collected and analyzed in real time, creating information that improves decision-making processes. It also helps identify opportunities that address business challenges. In addition, an organization utilizing big data is able to reduce time-lags in the transmission of information across all organizational management levels allowing quick interpretation and analysis of data that is evidence-based. In the end, this information becomes part of the business core strategy.

Conclusion

The use of Big Data is increasingly becoming an important tool in strategic decision-making processes. It allows the decision makers operationalize decision-making process by accessing data from multiple sources and analyze it in real time. Digital data collected from multi-sources such social media network messages, blogs, news and other digital sources can be mined to provide information on different dimensions of the organization. When analyzed, this data produce customer information that can be used to track changes in customer behaviors. An organization utilizing this data creates a data-oriented culture, an information management expertise, and competence analytics. These three organizational dimensions reinforce each other and become core elements in the organization’s core strategies. From the core strategies, basic strategies are formulated.

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References

Hayles, R. A. (2007). Planning and executing IT strategy. IT Professional, 9(5), 12-18.

Kansas City Business Journal. (1999). Microstrategy helps Payless Cashways grow. Web.

Kiron, D., & Shockley, R. (2011). Creating business value with analytics. MIT Sloan Management Review, 53(1), 57-63.

LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21-31.

Mahdi, I. M., & Alreshaid, K. (2005). Decision support system for selecting the proper project delivery method using analytical hierarchy process (AHP). International Journal of Project Management, 23(7), 564-572.

Nobel, C. (2010). How IT Shapes Top-Down and Bottom-Up Decision Making. Working Knowledge: Harvard Business School. November 1. Web.

Russom, P. (2011). Big data analytics. TDWI Best Practices Report, Fourth Quarter.

UN Global Pulse. (2012). Big Data for Development: Challenges & Opportunities. Web.

Webster, J. (2011) Understanding Big Data Analytics. SeaRchStorage.com. Web.

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