Critically framing data problems in a variety of business contexts

Question

Task: Write a REPORT that demonstrates how data analysis has been framed to address business problems. The report will be focusing on the rationale for data-driven decision-making in a variety of business contexts of your choice for at least three types of contemporary business problems. You will need to critically justify the use of data for solving these business problems.

Answer

Analytics for Managers

Introduction

In today's ever-evolving business environment, it is crucial to provide decision-makers with the information they need to make educated, deliberate decisions through data analysis. To maintain relevance, businesses must harness the power of data and base choices on empirical evidence. When it comes to analysing data and making strategic business decisions, data analytics is quickly becoming a must. This report will explore why businesses need to make decisions based on data. It'll also talk about data analytics and how it may help solve today's business problems.

In this study, we'll analyse four sorts of business problems and how data analytics may help fix them. Financial risk management is the primary concern for any company. The connection between systemic risk and accounting issues will be highlighted. The second problem we'll investigate is the going concern risks faced by the real estate sector, and specifically how companies might use numbers to gauge the success of their plans. Thirdly, construction project management is an important area of concern for businesses. Project delays are something that Chiou and Su (2007) want to look at, as well as possible solutions.

Data analytics are discussed, along with the most important variables in each of the problems. It will then explain the specific data difficulties associated with each business issue and how those difficulties affect the functioning of a business. The study also demonstrates how some businesses have successfully used data analytics to address these challenges and what goals they've set to direct their data analysis.

Data Analytics: Variables

Data analytics is the practice of using statistical and computational methods to extract useful information from data. Data must be found, gathered, evaluated, and its importance must be determined. Analytics may be categorised as either an attempt to explain the data or a prediction of what will happen based on the data. Using descriptive analytics, one investigates prior events. The goal of predictive analytics is to foretell the future. Data analytics uses observable constituents in any instance.

Payment Problems in the Construction Industry: One of the most important problems in the construction industry is a disagreement over payment between builders, subcontractors, and owners. It may take a long time to finish a project if workers had to stop working until they were paid. Disputes over payment may arise for a number of reasons, such as disagreements over the quality of work completed or differences in the terms of the original payment agreement. It is possible to utilise data analysis to examine payment patterns, spot payment discrepancies, and predict when payments may be late. Construction firms may avoid late payments and payment disputes by evaluating payment information.

Construction and Property Development Sector:Changing material pricing, worker shortages, and alterations to construction regulations are just a few of the challenges faced by the construction and property development industry. By revealing market trends, highlighting cost-cutting options, and predicting shifts in building standards and legislation, data analysis may help find solutions to these problems. By comparing current and historic costs for materials and labour, businesses, for instance, might use data analysis to find areas for cost savings. Data analysis may also be used to foretell shifts in construction regulations by keeping tabs on political activity and reviewing historical information on the development of legislation.

Real Estate Location Decision-Making:Location selection is a major factor in real estate development. The success of a real estate development venture may hinge in large part on the site selection process. The examination of demographic data, economic information, and other substantial data sets can help people choose a new place to call home. Data analysis may be used by companies, for instance, to locate regions with favourable demographics (such as rapid population expansion, high employment rates, and low crime rates). Data analysis may also be used by businesses to locate regions with favourable land costs and potential growth.

Construction Project Delays:Delays in building projects can occur for many different reasons, such as improper planning, inadequate resources, or even legal complications. Project delays may be investigated and potential remedies developed with the use of data analysis. Trends in project delays, such as when certain subcontractors or commodities cause delays, can be identified through data analysis. Data analysis may also be used to spot gaps in coverage, revealing the need for new tools or personnel. By analysing information on building delays, businesses in the industry may devise methods to quicken and streamline projects.

In each of these business problems, it is crucial to identify and analyse the appropriate variables to gain insights into the problem and develop strategies to address them. However, the quality of the data being analysed is also critical to the success of the data analytics process. It is essential to invest in reliable data collection and management tools to ensure the accuracy and completeness of the data.

Data Problems

Data problems can have a significant impact on the reliability and validity of analytical results, particularly when it comes to solving specific business challenges. In order to ensure accurate and relevant insights, it is important to consider the specific data variables that may impact the analysis. Here are some examples of data problems that are relevant to specific business contexts and challenges:

Payment Problems in the Construction Industry:

The construction business is prone to payment troubles, especially with subcontractors and suppliers. Concerns about late payments, payment problems, and missed payments can have a devastating effect on the project's budget. Lack of familiarity with various forms of payment is a major problem. This complicates payment management and makes it harder to spot overdue bills. Incorrect data or paperwork may also be the root cause of payment problems. Therefore, correct information is essential while handling construction industry payment concerns. Payment patterns and risks might potentially be identified through data analysis, enabling for the early discovery and resolution of payment difficulties. Monitoring payment plans, payment history, and payment conflicts helps spot potential problems before they become serious.

The construction sector is difficult, and many businesses have trouble being paid. For efficient payment management, accurate data is essential. The documentation must be correct, full, and well-organized, and the payment information must be current. Payment processes may be sped up and payment information can be better tracked with the aid of trustworthy payment handling technology. In the end, this can help to prevent payment problems, letting construction firms concentrate on completing high-quality projects on time and within budget.

Construction and Property Development Sector:

The construction and real estate sectors might benefit from using data analysis to spot patterns and trends that could lead to more informed decision making. The absence of reliable data on supply and demand is a problem here. Future investment and market trend decisions suffer from a lack of precise information. Because of this, projects may be over- or under-funded due to inaccurate predictions of future demand and supply. Information analysis might aid decision making by revealing supply and demand trends in the market.

Data quality is another challenge in this sector; bad information can have a negative effect on a project's bottom line. Cost overruns and decreased profits are commonplace when not enough specific information is available to accurately estimate how much something will cost. Therefore, it is crucial to have accurate data when controlling project pricing in the construction and real estate development industries. By keeping tabs on project timelines, expenditures, and resource utilisation, for instance, data analysis may help identify and mitigate cost hazards.

Real Estate Location Decision-Making:

Location choices in the real estate industry are among the most important, making data analysis and accuracy paramount. Lack of transparent and reliable property and location data is a problem in this sector. It's possible that faulty predictions of future market behaviour and consumer demand might result. You may be able to uncover key site traits and trends through data study, which might lead to more informed decision-making. To do this, we need to analyse data on population, regional economic development, and consumer demand.

Another major issue is the poor quality of the data used, especially for things like property and location information. Poor investment decisions can result from a lack of knowledge about a site's potential and its value due to a lack of credible information about the site's qualities. Therefore, it is essential to have high-quality data when making location decisions for real estate. Monitoring property prices, corporate trends, and geographical attributes are just a few examples of how data analysis may help identify and mitigate risk. This might help you avoid making expensive mistakes when deciding where to buy property.

Construction Project Delays:

Numerous professionals, including architects, construction workers, and project managers, are often needed to complete a single building project. These folks employ data analysis to monitor the project's development and spot any bottlenecks. The effectiveness of data analysis, however, can be undermined by problems with the data itself, such as missing or duplicate information, which can delay or prevent project completion. Errors in data collecting, missing data points, and mistakes in data input can all contribute to data that is insufficient for its intended purpose. This might make it harder to spot issues in a timely manner and implement solutions.

Duplicated information is a major contributor to construction project delays. When the same information is stored in two different places, this is known as data duplication. This results in an excessively high sample size. Incorrect research may ensue, making it harder for project managers to discover and address delays due to missing important information. Consequently, project managers need to take measures to lessen the likelihood of data duplication, such as implementing compression techniques or combining data where it makes sense to do so. This might improve the quality of the data being analysed and make it more likely that any delays in the project will be uncovered.

In conclusion, data analysis can be a powerful tool for solving modern business challenges. However, to ensure accurate and relevant insights, it is important to consider the specific data problems that may impact the analysis. By carefully cleaning, integrating, and deduplicating data, businesses can make more informed decisions and improve their overall performance.

Going Concern Risks

Problem 1: Payment Problems in the Construction Industry

Data variable used -

Payment problems are endemic in the construction sector. Workers and subcontractors in the construction industry often have difficulties being paid on time. Ramachandra and Rotimi (2011) looked at the causes of construction sector payment problems. They found that payment delays were a serious problem in the industry. Payment delays were shown to be associated with disputes about the quality of work, the terms of the contract, and the amount owed.

Payment issues in the construction business may be addressed through data analysis by looking for patterns in payment delays and identifying their root causes. Using data analytics, Mpofu et al. (2017) looked at the reasons why building projects in the United Arab Emirates were taking so much longer than expected. Inadequate planning, lack of stakeholder participation, and poor stakeholder communication all contributed to delays, as stated by KV &Bhat (2019). By studying payment data, businesses may spot patterns and possible problems with payments before they become severe.

Problem 2: Going-Concern Risks in the Construction and Property Development Sector

Especially in the real estate development industries, where economic and market instability can have far-reaching effects on a company's ability to function, going-concern concerns are a key cause for concern. Apprehensions about the companies' viability were examined in the CSR and integrated reports of Polish construction and real estate development firms by Szczepankiewicz (2021). The study found that businesses in this sector regularly dealt with issues such as economic instability, shifting government rules, and volatile markets (Chiou, & Su, 2007).

By considering both financial and non-financial factors, data analytics may be utilised to address going-concern worries in the construction and real estate development industries. Looking at things like income and expenditures might help businesses spot possible financial threats. Business risks may be identified using non-financial indicators such as changes in government legislation and market volatility (Gao, &Topuz, 2020).

Problem 3: Real Estate Location Decision-Making

To optimise profits, real estate investors and owners must make strategic site choices. Market trends, demand, demographics, and zoning regulations are just a few examples of the many variables that might affect where the best site is for a business. The complexity and fluidity of these factors also make making decisions challenging, especially for first-time buyers or those without access to adequate market data. According to research (Mosallaeipour, 2020) several decision support systems have emerged as a result of the need to make informed decisions about where to invest in real estate. These systems combine market data with other elements to provide an in-depth assessment of investment prospects. These systems' precision and usefulness, however, are reliant on the quality and amount of the data used in the analysis.

Decision support systems need to gather and synthesise data from a wide range of sources, such as real estate advertising, past transactions, zoning maps, census data, and more, to address this data difficulty. This information gives us suggestions about recurring tendencies and patterns that may guide our decision of where to settle down. Also, when data comes from a wide variety of sources, the system should be built to account for differences in data quality and dependability. Outlier identification, data smoothing, and data imputation are all ways to check that the study is accurate and consistent, as stated by Syed Abu Bakar and Jaafar (2018).

Using metrics like property values, demand, and demographic trends, data analysis may help investors find promising commercial real estate markets. Zoning regulations, infrastructure, and environmental factors are all important to consider while conducting location research. It is possible to utilise machine learning techniques like clustering and classification to recognise patterns and trends in data and base decisions on them.

There have been several studies looking into the role of decision support systems in guiding homebuyers to the best neighbourhoods. This research has proven that proper and fast analysis requires a large quantity of high-quality data. A strong expert decision support system was built by Mosallaeipour et al. (2020) to aid individuals in determining where to invest in real estate. This system combines market data, GIS, and BIM. The system employs data validation strategies, machine learning algorithms, and optimisation models to advise investors and developers on where to put their money. To aid in real estate investment decisions, Shkundalov and Vilutiene (2022) created a method called "quantitative view assessment" (QUVIAS) that combines geographic information systems (GIS), building information modelling (BIM), and the world wide web. The method utilises Google Maps and other open-source data sets, among others, to suggest potential destinations.

Problem 4: Construction Project Delays

Delays in construction projects are a common source of extra expenses, tension, and sometimes litigation. Understanding the root cause of the problem is essential for resolving it and avoiding further setbacks. Design faults, a lack of resources, labour issues, and unforeseen events like extreme weather are only some of the causes of construction delays. Mpofu et al. (2017) suggest that identifying the root causes of delays and how they affect project plans will help in finding workable solutions.

Construction firms need to gather and analyse data on project durations, budgets, and snags to solve this data problem. Goals, resource utilisation, material supply, and even weather conditions may all be useful data points to track. Data analysis might identify trends and patterns about the causes of delays. As a result, companies may zero in on the most pressing concerns and prepare responses accordingly (KV, &Bhat, 2019). The goal of data analysis in the context of a building project is to identify the most consequential delays and assess their impact on the schedule as a whole. It is possible to utilise statistical methods like regression analysis to learn how variables like weather, material supply, and labour availability are related to project delays.

Conclusion

Last but not least, data analytics and data-driven decision-making are crucial tools for overcoming many difficulties in business. In this study, we examined four pressing businessproblems confronted by real estate businesses and how data analysis may be used for each of them.

First, accounting variables and location-based analysis might help reduce systemic risk in real estate and construction companies (Gao, &Topuz, 2020). The second problem was that individuals within the structures couldn't see outside. This issue might be fixed by conducting a quantitative perspective evaluation employing BIM, GIS, and digital platforms (Shkundalov&Vilutien, 2022).

The third problem was building delays, which could be remedied by learning their root causes and acting accordingly. A clear conceptualization of data problems and the necessity of acquiring, analysing, and relating data to specific business scenarios were both made possible by the selection and thorough evaluation of peer-reviewed conference papers or journal articles in each case. To guarantee thatthe information acquired can be utilised to make smart decisions, managers need to be proficient at data analysis and establish clear goals and objectives for data analysis. According to Gao and Topuz (2020), companies may benefit from data analysis in several ways, including improved decision-making, lower risk, and higher performance.

In the modern business world, it is no longer optional to base decisions on available facts. Managers may improve their chances of success and acquire a competitive edge by making decisions based on data. By identifying the principal causes of the data problems plaguing their company and employing the appropriate data analysis tools, managers may get valuable insights.

Companies in areas as diverse as construction, real estate, and project management are shown to have a wide range of data challenges using case studies. These problems manifest in a variety of ways, including but not limited to payment delays, construction delays, risk assessment, location selection, and systemic risk. These businesses overcame obstacles and accomplished their goals thanks to data-driven decision-making procedures.

References

Chiou, C. C., & Su, R. K. (2007).On the relation of systematic risk and accounting variables. Managerial Finance, 33(8), 517-533.

Gao, X., &Topuz, J. (2020). Firm location and systematic risk: the real estate channel. Review of Accounting and Finance, 19(3), 387-409.

KV, P., V, V., &Bhat, N. (2019).Analysis of causes of delay in Indian construction projects and mitigation measures. Journal of Financial Management of Property and Construction, 24(1), 58-78.

Mosallaeipour, S., Shavarani, S. M., Steens, C., & Eros, A. (2020). A robust expert decision support system for making real estate location decisions, a case of investor-developer-user organization in industry 4.0 era. Journal of Corporate Real Estate, 22(1), 21-47.

Mpofu, B., Ochieng, E. G., Moobela, C., & Pretorius, A. (2017).Profiling causative factors leading to construction project delays in the United Arab Emirates. Engineering, Construction and Architectural Management.

Ramachandra, T., &Rotimi, J. O. (2011).The nature of payment problems in the New Zealand construction industry. Australasian Journal of Construction Economics and Building, The, 11(2), 22-33.

Shkundalov, D., &Vilutien?, T. (2022).Quantitative View Assessment (QUVIAS) method for window visibility analysis utilizing BIM, GIS and Web environments. International Journal of Strategic Property Management, 26(4), 287-304.

Syed Abu Bakar, S. P., &Jaafar, M. (2018).Achieving business success through land banking and market analysis: Perspectives of Malaysian private housing developers. Property Management, 36(5), 562-574.

Szczepankiewicz, E. I. (2021). Identification of Going-Concern Risks in CSR and Integrated Reports of Polish Companies from the Construction and Property Development Sector. Risks, 9(5), 85.