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Data Collection and Analysis for Organizational Development


Data-driven decision-making refers to how organizational decisions are made according to the actual data instead of observation or intuition alone. In the recent past, most organizations and institutions have shifted to it. This is because, with the use of actual data to make decisions, a company can make solid decisions, unlike the companies that use intuition. According to Ian Matt, there are false and biased assumptions without data that would eventually lead to poor decision-making (Ma, & Turk, et al. 2020). According to a survey conducted by Business Times, 59% of the businesses that had failed in Michigan more than half of their companies’ decisions were based on intuition or gut feel rather than data (Wu, & Wang et al. 2020). The essay below seeks to scrutinize data collection and analysis for organizational development.

This decision-making method involves the use of data, facts, and metrics, which inform the strategy of decision-making for the business that aligns with the initiatives, goals, and objectives of the business. The moment an organization understands the importance of its data, the sales manager, business analyst, and human resource specialist can make informed decisions. Nevertheless, decision-making is not achieved simply by selecting the suitable method of analytics technology to obtain the following strategic opportunity (Ma, & Turk, et al. 2020). The organization or institution will be required to propagate a culture that embraces data-driven decision making which will help to enhance curiosity and creativity

Importance of Data-Driven Decision Making

It facilitates accountability and transparency. This is the fundamental importance of this decision-making strategy since it will help improve the accountability and transparency of a given institution or organization. This strategy helps to encourage staff engagement and teamwork in the organization. The policies and strategies of its implementation will assist the organization in tackling any possible risks or threats to ensure an improved overall performance (Wu & Wang et al. 2020). Besides, it improves the morale of the workers in the organization since this strategy of decision-making enables them to understand the objective data backups. This helps the workers to make the most appropriate decisions according to their daily activities. The objective data help in data collection of the organization used in keeping and compliance or the records. This ensures that the organization is accountable for the management of the data. In addition, data-driven decision-making leads to the prioritization of Information. The objectives are concrete, and the total results are evaluated accordingly, which helps promote transparency in the organization (Brynjolfsson & McElheran, 2016).

Besides, this strategy of decision-making is fundamental in the provision of clear feedback from the research in the market. It is vital in the identification of what is supposed to be researched and is not supposed to be researched in a given time. Therefore, the company will identify reliable services and new products and create initiatives for new workplaces. This approach will also help to project the trends that are likely to occur in the future. Besides, data-driven decision-making involves the investigation of the investigation of historical data. This helps to project the future expectations of the business and what must be changed to promote the betterment of the organization’s performance (Wu & Wang et al., 2020). In addition, a data-driven decision-making strategy helps the institution or organization research for Information that will help to improve the relationship between the organization and its clients. This will help the organization to encourage their clients when there are new changes in the business. For instance, introducing a new product or service in the market or business expansion will help strengthen the brand (Brynjolfsson & McElheran, 2016).

Data and Information as used in Data-Driven Decision Making

Data can be defined as raw, unorganized facts that require to be processed. As defined by Wikipedia, data refers to a set of values of quantitative variables or qualitative variables concerning one or many objects or persons (Heeringa, & Berglund, 2017). A single set of variables is known as a datum. More often, data is purported to be an abstract followed by Information, while knowledge is viewed as the most abstract.

With this regard, by interpretation, data becomes Information. For instance, the number of workers in a given organization could be considered as data. In contrast, a book containing the number of workers of an organization could be considered as Information. However, a guidebook containing Information on managing the number of workers in a given company could be considered knowledge. Therefore, Information has a more diverse meaning that ranges from its daily usage to its use in a technical context. Generally, the concept of Information is related to the ideas of communication, constraints, data, control, instruction, form, meaning, instruction, pattern, mental stimulus, representation, perception, and knowledge (Chambers, & Tukey, et al. 2018). Data is helpful in Scientific research, business management, for example; data, revenue, sales, stock price, and profits, governance, for example; unemployment, literacy rates, and crime rates, and in human resource management, for example, the population of homeless people for a non-profit organization.

The collection of data is achieved through a secondary source or a primary source. In a primary source, the person conducting the research is the first person to acquire the data. On the other hand, is a secondary source. The person conducting the research acquires the data collected by the other sources earlier, for example, data disseminated in a scientific journal. After the data has been collected, it is analyzed using various strategies. These data analysis strategies vary according to various factors. They include data percolation and data triangulation. Data percolation enables a more articulate method of classifying, analyzing, and classifying data using five possible metrics (Heeringa, & Berglund, 2017). This helps to maximize the objectivity of the research, thus permitting the comprehension of the phenomena being investigated. It involves quantitative methods, qualitative methods, interviews with experts, computer simulation, and literature reviews.

After data is collected, measured, analyzed, and reported, it is used to formulate data visualizations such as tables, images and graphs. Generally, data refers to some existing knowledge or Information which is coded or represented in forms that are appropriate for processing or better usage. Raw data refers to a collection of characters or numbers before it has been corrected and analyzed by researchers. Raw data requires to be corrected to filter the outliers or data entry errors, such as a thermometer recording from an outdoor arctic location reading a tropical temperature. Besides, field data refers to unprocessed data which is collected in an environment that is uncontrolled. On the other hand, experimental data refers to data which is collected within a scientific investigation context either by recording or observation (Chambers, & Tukey, et al. 2018).

On the other hand, Information refers to data that has been processed, organized, structured, and presented in a given context to make it useful. Generally, Information offers the data a context which is used for decision making. For instance, the sale of one customer in a given restaurant is data, while when the business is able to identify the most popular dish, it is considered as Information (Wickens, & Carswell, 2021). Technically, Information is viewed to be as a result of uncertainties. This is because it helps to answer the question, “what?” hence delineate both its nature and essence of its features. Therefore, the idea of Information, has different meanings under different contexts. This makes the idea of Information synonymous to communication, entropy, representation, proposition, perception, pattern, mental stimuli, understanding, meaning, knowledge, education, form, data, control, and constraint. Therefore, in this regard, Information is highly related to data. The difference between data and Information is that data involves redundant symbols or characters while Information clears uncertainty. However, through data compression, data approaches Information.

Analysis and Interpretation of a Set of Data

The following chart shows the interpretation of analysis of data of a given company after a research was conducted to investigate various aspects in the company. That data used in the preparation of this chart has been extracted from the attachments on the assignment.

Analysis and Interpretation of a Set of Data


In a given organization, the employees hold talks that begin with the data, and they improve their data skills via the application and practice. Fundamentally, it is necessary to have a self-service model whereby individuals will be able to access the data which they require. However, this is balanced by offering governance and security to the data to ensure the system revokes the persons who are not supposed to access specific data. Besides, proficiency and creating development and training opportunities for the workers to understand data skills are required. The essay above seeks to scrutinize data collection and analysis for organizational development.


Brynjolfsson, E., & McElheran, K. (2016). The rapid adoption of data-driven decision-making. American Economic Review106(5), 133-39.

Chambers, J. & Tukey, P. (2018). Graphical methods for data analysis. Chapman and Hall/CRC.

Heeringa, S. & Berglund, A. (2017). Applied survey data analysis. Chapman and hall/CRC.

Ma, Z., & Turk, Z. (2020). Data-driven decision-making for equipment maintenance. Automation in Construction112, 103103.

Wickens, D., & Carswell, M. (2021). Information processing. Handbook of human factors and ergonomics, 114-158.

Wu, C., & Wang, X. (2020). Critical review of data-driven decision-making in bridge operation and maintenance. Structure and Infrastructure Engineering, 1-24.


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