The Effect of Data Security Procedures and Big Data Analytics on Engineering Performance: A Case Study of Lagos (Iganmu) Industrial Layout

The purpose of this research is to comprehend how big data analytics affect engineering performance. The industrial part especially the engineering practice is among the most significant and delicate in the world. Gathering and manufacturing have a huge social impact on the economies of the nations and, consequently, on the lives of individuals all over the world. The potential for big data to completely alter engineering practice and enhance ongoing engineering projects. Many organizations appear to be aware of the advantages big data can bring to their performance in engineering practice, particularly its significant possible worth, but they encounter a number of challenges when implementing it, primarily because they are having trouble figuring out how to use the derived insights for their development. 

The development of new strategies and services is a crucial engineering activity, and it has been demonstrated to significantly affect an organization’s viability. If these insights are monetized, Organizations aiming for an improved engineering practice can build brand-new, customer-centered, and data-driven projects or both goods and services, providing a long-lasting competitive advantage and new revenue streams. According to empirical research, companies that have engineering practice incorporated with a data-driven approach that can show how big data contributes to improved performance, while those that have not yet instilled the entire organization struggle with an absence of comprehension on how to use big data technology to create potential value and accomplish their organizational goals.

Due to the enormous strategic potential of big data, this article tries to conceptualize and investigate its effects on corporate performance. It also explores the impacts of big data on engineering performance because of its high strategic potential. Finally, it explores whether and how the creation of new engineering services and projects makes use of big data and related technologies. An in-depth SWOT, binary Logistic Regression analysis, and the use of grounded theory combine previous big data studies with several enterprises in Lagos, Nigeria’s Iganmu industrial layout area. The caliber of data gathered, data availability, legal considerations of data confidentiality and safekeeping, and highly qualified individuals working with big data are additional critical factors that influence the use of a data-driven approach. Therefore, in order for companies to achieve effectiveness and efficiency, they need to reflect on and make strategic decisions utilizing a comprehensive perspective on big data.

Introduction

Big data has emerged as the most important source of revenue for engineering firms in the future due to engineering technology’s continued progress. In Lagos, Nigeria, one of the areas where firms engaged in engineering practice are crowded is the Iganmu Industrial Layout. This era of industrialization illustrates a variety of useful strategies for the operational operations of the producers. The concept’s exploration was encouraged by the requirements of numerous enterprises regarding the improvement of each industrial setting in the design [1]. There may be an explosion of chances for businesses in relation to the advantages brought about by the use of big data. Given the knowledge gap and the high expectations, the coupling of the companies in the Iganmu industrial layout with big data analytics offers a key topic of study.

This Lagos neighborhood’s Iganmu industrial layout is one that is crowded with companies that use the best engineering practices. The Muritala Muhammed Airport, the Mechanical Engineering Club, Leadway Assurance, Nigeria Breweries, the Nigeria Internet Registration Association (NiRA), the NIIT Fortesoft, the SFS Engineering Service/construction company, and Tetramanor Limited are just a few of the businesses in the area.

The manufacturing industry has recently started using big data technology in an effort to increase sustainability and enhance product quality [2]. It is significant to highlight that manufacturing organizations are getting increasingly as a result of the complexity of the external environment, they must be familiar with digital solutions to improve their operations. Similar to this, innovation is essential for businesses to enhance their ability to make decisions and get a competitive advantage. Since the third period of industrialization, companies have become highly automated, which has actually made it easier to generate data and employ analytics in the upcoming fourth age. Big data is being gathered and analyzed with the goal of finding insightful information that could inspire the creation of new goods and concepts and ultimately boost (Fig. 1) a company’s worth [3].

Fig. 1. Map of Lagos showing Iganmu industrial layout.

In spite of this, it’s critical to make sure that for an engineering firm to progress, one must be inventive services that set itself apart from its rivals [4]. A company can successfully implement an engineering practice by knowing the engineering processes, plans, and procedures, as well as end-user needs and wants, by scanning the business environment. Because both internal and exterior elements affect a customer’s spending behavior [5]. The procedure of gathering and utilizing data about occasions, designs, and networks in the interior and exterior situations of an organization is known as scanning the environment. They can increase their adaptability, reactivity, reliability, and client service. Service providers can use big data analytics to enhance the performance of the components of their services in engineering practices [6]. The adoption of big data analytics is essential for logistics and engineering operations since improved performance depends on making quick, accurate engineering practice decisions. The consequent effect, which results in inefficiencies among the distribution channels, can be dealt with using big data analytics. Reference [7] claim that the lack of a clear definition of competence in Big Data predictive analytics may restrict the impact of this technology on engineering performance. Statistical studies of Big Data analytics that decrease standard deviations (demand variance) and provide forecasting a signaling component are required [8]. However, as worries about data security and privacy have surfaced, both academia and business have begun to pay attention to how organizations are employing big data analytics. From publicly accessible social media streams, service providers have gathered data in the form of audiovisual files, status updates, likes, shares, followers, retweets, and comments.

Likewise, big data analytics can also utilize information from systems for customer relationship management and enterprise resource planning. There are now hazards to one’s privacy and security due to both structured and unstructured data being readily available. A large amount of information was acquired from various sources. The magnitude, speed, diversity, and complexity of Big Data are frequently too much for traditional non-Big Data security solutions to handle, and most businesses lack systematic methods for guaranteeing proper data access mechanisms [9]. Because of this, service providers are lacking in the systematic tools and methods needed to draw actionable insights from data and enhance operations [10], [11]. Despite the fact that big data analytics can help with engineering practice management and decision-making, many companies have had difficulty deploying the technology.

The last few years have seen a surge in interest in Big Data. But when did interest begin to grow? Google Trends (2020) reports that during the previous 16 years, the following terms have been searched for most frequently:

The present data explosion, which has caused the word “Big Data” to be frequently used nowadays, is caused by three key factors:

• Numerous applications, including those from social media platforms and sensors, are constantly gathering data.
• Data collection is now more economical than ever (Fig. 2) thanks to technologies like cloud computing, making its utilization feasible for any size or financial level of business.
• Tools like machine learning have significantly advanced and gained relevance. This enables enterprises to gain a deeper understanding of the data gathered.

Fig. 2. Interest for the term ‘Big data’ (Google trends 2020).

Big Data Analytics

Information exchanges inside and between enterprises in the engineering practice are now essential for corporate survival and continuity in the contemporary knowledge-based economy. It is essential for companies to implement technology tools for analyzing existing data and predicting customer demand. Although service outcomes are ephemeral, accurate information on clients’ purchasing trends is vital to enhancing the performance of the service in terms of customer service, dependability, responsiveness, and flexibility. Companies should collaborate with other business partners to track a customer’s lifestyle and activities due to the complexity of engineering operation integration. Social media generates 10 to 18 terabytes of data every day on a global scale [12]. Exabytes, or 1 million terabytes, and beyond, and the amount of technology capabilities to store, manage, analyze, interpret, and visualize it are both referred to as “Big Data” [13].

Due to a new processing mode, [14] described Big Data as an information technology for huge data processing. Big Data also provides a robust capability for decision making, detection, and clarifying optimization based on [15]. Decisions about engineering operation network design, product development, and strategic sourcing have all benefited from the use of big data analytics. New challenges in large dataset capture, gathering, analysis, collating, distribution, transmission, and processing in organizations led to the development of the Big Data idea. Making digital, the motivation for this concept is the growing popularity of social media among electronic device users. Huge data has been used to handle datasets that are too big and difficult for conventional database management systems to handle. They cannot, therefore, be effectively recorded, stored, managed, and processed by current software tools and storage systems in a timely manner because they are too enormous [5]. The “3Vs,” or several approaches to the big data notion, comprise volume, variety, and velocity (the rate at which data is collected). Unstructured data are produced by sources including social media, emails, and communication (the volume of current datasets in big data is a significant attribute since such data is considered to be excepted from the existing management techniques of databases). Other professionals characterize big data as using the 3V concept: [1] “Large-scale, high-volume computer equipment that demands effective, cutting-edge information processing to facilitate better recognizing automated decision-making processes” [16], [2] Big data, according to its definition, is “volume, high-speed, and very diversified data used in decision-making and requires imaginative management solutions.” and [4] “Various scopes and their relationships cannot be simply evaluated due to the complication and dynamic nature of this “unique sort of Data that is too massive to be stored, managed, and evaluated by means of a standard system coupled with an unspecified source” [9].

Decisions made by management have been aided by big data analytics on engineering activities throughout the operational planning phase, which usually involves request preparation, obtaining, manufacturing, recording, and logistics. Future research on the properties of big data analytics on all company performance outcomes is crucial because it is recognized as a viable essential in an engineering practice [17].

Data Security

Due to the enormous volume of information contained in Big Data, the data safekeeping issue is crucial for massive data analysis [18]. Unfortunately, inadequate security and privacy safeguards are frequently absent from the technologies and procedures used to acquire large amounts of data [19]. The primary problem for big data is creating standards and processes that enable enormous amounts of data to be managed and processed in a secure and reliable manner. This will guarantee the integrity of the outcomes. Clearly defined standards and processes are used to preserve data security and privacy [20]. The hazards of exchanging information in the engineering practice are connected to the data (Fig. 3) safekeeping issue in engineering practice management, which is also connected to data ownership, data storage/availability, and data privileges [18]. Big Data analytics management difficulties are noted as being particularly prone to assault [21]. Data security issues are made worse by a lack of sophisticated infrastructure that provides data security services including integrity, confidentiality, availability, and accountability [22]. When data safekeeping is linked to engineering innovation skills, particularly in data gathering, integration, and transformation, these elements continue to provide issues. Big Data technology must be used to analyze the data collected, and the outcomes will be distributed throughout engineering operation network participants for efficiency and effectiveness. Typically, engineering practice innovation takes place after the engineering practices network agrees on how to interpret the data. However, as the number of engineering performances for each engineering project increases, so does the potential for information exchange with unapproved networks. Plans might then be leaked to a careless individual, making it simple for rivals to copy an idea. In order to handle Big Data technologies and ensure that data are secure, and privacy is preserved, a company must be able to manage a strong security architecture. A business must have policies in place for how its data warehouse is used that control how employees and staff of each division can access information that is pertinent to their department.

Fig. 3. Research Model.

Related Works

There are several scholarly viewpoints in the literature that offer analysis for the hopes, the obstacles, the implementation, but also the blending of other ideas within the industry, analytics, and others. In addition, research on big data and analytics is popular since it examines prospects for organizations and deals with potential problems. The variety of studies is constantly expanding as more research is needed to clarify, improve, and validate the notions already established. Organizations are now more motivated to implement these solutions as they recognize the opportunities. To be more specific, current research indicates that the integration of industry and big data is currently an unexplored topic and that additional studies from a variety of perspectives are needed [19], [23]. The associated work for big data in the industry is mostly focused on the opportunities and difficulties portrayed. Numerous studies have cited the benefits of big data projects, including innovation, productivity, customer satisfaction, and future prediction [23]–[25]. Likewise big data V characteristics, structure and competencies, security of information and human resources concerns; are shown to be likely origins of the problems that require consideration and discussion [24], [26]–[31]. The information gathered by industry is referred to as "industrial data" in literature [25], [27]. Manufacturing data, big data for operations, or big data of Industrial IoT, and their source are characterized as sensors, devices, vehicles and systems like ERP and CRM [26], [30], [32], [33]. In several papers, the use of big data analysis approaches is also explored in relation to the project and the intended managerial insights. There are three basic categories of analytics that are used: descriptive, predictive, and prescriptive [25], [27], [30]. Several authors have also mentioned real-time and inquisitive analytics [26], [30].

According to [25], it may be possible to extract data from a variety of sources and integrate it with other data to produce insightful information that is useful in manufacturing. In order to offer information, these data must then undergo special processing. The goal is to enable proactive decision-making and solution execution by giving systems and machines self-awareness. Reference [32] supported the idea that prognostic manufacturing is necessary in order to accomplish the desired results. In addition, [30] made it clear that the development of Big Data Analytics is due to concentric computer modeling, which facilitates data manufacturing, research, and planning for the generation of perceptions.

Analytics using big data in industries have tremendous potential for enhancing innovation and managerial decision-making, according to [25]. In a similar vein, [32] noted that the influence of process openness in production has an effect on failure discovery, record planning, and product design. Big data analytics often benefit a wide range of business sectors. For Big Data Analytic capabilities, [27] developed a 3-layer concept: [1] problems including quality, efficiency, maintenance, breakdowns, and environmental impact in the manufacturing process [2] Data transformation; and [3] The potential benefits of Big Data analytics for the entire enterprise. Additionally, [27] concentrated on elucidating the role of big data in engineering practice management and showed how big data is currently used to influence business in terms of operational effectiveness, customer experiences, and innovation in goods or business models in industrial settings.

In order to inform the body of the organizations about the anticipated advantages, [30] illustrated the five aspects of the value created through the application of big data analytics. The article’s five dimensions are as follows: [1] Informational, which refers to the significance of information in the strategy adopted; [2] Transactional, which refers to financial gains and cost reductions; [3] Transformational, which refers to organizational processes becoming innovative and highly efficient processes; [4] Strategic, which refers to the analytics’ ultimate goal of creating a competitive advantage; and [5] Infrastructure, which refers to the technology that would be developed. Reference [31] set out to conduct a study of the literature on the machinery behind industrial Internet of Things data analytics and to outline any relevant issues. According to their findings, understanding the characteristics of the data obtained is the first step in the collection, storage, and processing of heterogeneous data. Second, a data warehouse, cloud, or network solution is needed to store the data, and third, advanced technological tools are essential for conducting the analysis. According to [31], efficient data processing is necessary to produce useful insights. The requirements can be divided into three groups: the data typical, which requires combined semantics and interfaces; the data content, which comprises information from products, processes, businesses, and sensors; and, ultimately, the vertical and horizontal data integration of the CPS lifecycle. Decision processing, knowledge processing, and real-time processing are the three categories of processing requirements [33]. To provide the processes enough chances to become self-aware and maintain themselves, [31] mentions the analytics approaches used, such as descriptive, predictive, and prescriptive.

The use of cyber-physical models, predictive manufacturing, and the transformation of production are the three views [32] used to categorize the current trends in manufacturing big data. For a better understanding of the effect of big data at the industry level, [30] emphasized the need for additional research. Additionally, [31] supported the claim that there aren’t many studies for big data analytics in Industrial IoT while [32] argued that research into the data produced by manufacturing processes is necessary.

Methods

Through the researcher’s analytical interpretation of the respondents’ responses, qualitative research seeks to comprehend the viewpoints of people or groups regarding a topic [34]. The fact that data is gathered through observations, interviews, and documents is one of the primary characteristics of qualitative research. As a result, there are a variety of resources that can be examined by either an inductive or a deductive method [34]. In the research, the systematic literature review establishes the notion, and actual data gathering through interviewing provides additional support for it.

Questions using the SWOT Analysis Technique and Review

The SWOT analysis is chosen as a way to categorize the systematic literature findings in terms of strengths, weaknesses, opportunities, and threats with the aim of examining various elements of the findings. Organizations utilize the SWOT analysis to draw conclusions about their strategic preparation and running [34]. SWOT analysis is the process of analyzing an organization’s internal and external environments. The organization determines its strengths and weaknesses with the help of the internal analysis, which complements the external analysis in identifying risks and opportunities in the competitive environment. Uncertainty surrounds the origin of SWOT due to the concept’s concurrent development with the idea of strategy and the numerous academic and professional endeavors it has been linked to. Although it has the advantage of being well-known in both the academic and business communities, making it simple to understand and able to offer broad solutions by contrasting the good and bad aspects of the decisional context, its applicability is disputed due to the dynamic nature of the business environment [34]. In order to improve decision-making for the implementation of various projects, this research seeks to present an overview of the impact of big data on industry. SWOT analysis is a useful tool to support the research because it is a strategic management strategy.

To suitable into the SWOT analysis magnitudes, the research questions were divided into four groups. The goal was to identify the impact of big data analytics on manufacturing industry environments in terms of their strengths, limitations, possibilities, and dangers. The background study and related research served as the foundation for defining the questions. It is important to keep in mind that an organization develops its strengths and weaknesses internally, as opposed to threats and opportunities, which are identified in the exterior environment. In relation to this research, it is presumable that the benefits of big data analytics’ potential are the strengths. Additionally, the dangers and prospective problems that are highlighted in the literature and should be taken into account are the weaknesses, which are the existing big data analytics flaws found in organizations. The logic behind how the research questions were developed is first explained in this section. First off, the fact that the primary objective of companies is profit leads to the question of strengths. Companies strive to optimize their decision-making and performance in order to attain high levels of profitability while also gaining a competitive edge in the marketplace. However, it is important to exhibit ecologically sustainable conduct (Table I), particularly in the manufacturing sector. Big data has a variety of implications for these objectives and gives companies the tools they need to accomplish them successfully. On the other hand, considerations regarding the costs and infrastructure needed for the application of analytics creativities, as well as organizational adjustments in plan and human resources that should be made in order to succeed, were the disadvantages. However, given the current system and the stakeholders’ aversion to change, it is challenging to persuade them to embrace the change. The effects of the strengths are directly related to the categories. Two questions are used to symbolize the threats. The first is the impact on society, particularly in terms of human resources, and the second is related to privacy and security issues. The external factors that businesses should evaluate and address because they may have a substantial negative influence on business success and continuity are what lead to the risks. Based on these presumptions and the scientific literature, the research questions were developed with the goal of covering the entire topic. The project’s goal is to present a SWOT analysis of how big data will be used in companies in Iganmu Industrial Layout.

Table I. Questions that are Addressed in this Project

Q	What effect does big data have on industry?
Q1	Strength
Q1.1	What effect does this have on client relationships?
Q1.2	What effect does this have on decision-making?
Q1.3	What is the effect of Engineering performance?
Q1.4	What effect does this have on sustainability?
Q2	Weakness
Q2.1	What effect does it have on spending?
Q2.2	How are human resources affected?
Q2.3	How is the impact on human resources?
Q2.4	What effect does this have on organizational culture and strategy?
Q3	Opportunities
Q3.1	What effect does this have on competition?
Q3.2	How does this affect the effectiveness of the process?
Q3.3	What effect does this have on sustainability?
Q4	Threats
Q4.1	How are human resources affected?
Q4.2	What effect does this have on privacy and security?

Empirical/Statistical Data Gathering

Interviews are a recommended method because they provide a useful way to determine how people comprehend the phenomenon. Most interpretative studies use interviews because they are a crucial method for gathering information from experts in each discipline. Three data analyst professionals were questioned in order to gather opinions and viewpoints on big data projects. The participants and the author have worked together on a number of information system projects. The SWOT analysis framework developed as a result of the performed systematic literature review served as the foundation for the questions. To make sure that the subject was at ease and comfortable, each interview was conducted individually in the participant’s environment. The researcher performed the other two interviews by taking notes because they took place in noisy locations, but one session was recorded because the interviewee was willing to offer a lot of information. The time slot was set aside for an hour to allow participants enough time to reflect and explain their responses. Before the interview, the participants received the interview questionnaire so they could become acquainted with the concepts and conclusions of the research. To reassure the participants about their privacy and rights, it was also made clear how personal information will be protected and kept confidential. The participants were aware of the procedure and eager to contribute their opinions to this study.

Data Analysis

Following the SWOT analysis, the results were grouped according to strengths, weaknesses, opportunities, and threats. The intention of this was to assess the advantages of big data analytics in business and the opportunities for execution that these initiatives offer. However, it was difficult to distinguish between an opportunity and a strength because each opportunity may be thought of as a potential strength. To guide the research and reflect proper responses to the research objectives, specific categories were thus defined in each SWOT analysis. Since there were fewer findings and they were sufficiently clear about their nature and source, it was simpler to identify the weaknesses and risks. The identification and categorization of findings were done twice to prevent errors or omissions. In this approach, it was made sure that the SWOT accurately reflected the findings of each study.

In order to collect data for the survey by studying the method used in the quantifiable inquiry, questionnaires were distributed. It included closed-ended questions with nominal and ordinal scoring. Six assessors with backgrounds in the subject of assisted in conducting the study. Managers and Engineers who agreed to participate received an email with a link to the operational study. An email with a link to the online survey was sent to managers and engineers who consented to participate. With the signing of participation agreements with 26 of the companies in Iganmu industrial layout, the procedure came to a close. 22 managers provided extensive and insightful comments for this survey, with a response rate of 90.7%. The SPSS application, which also contained the factorial correspondence analysis, binary logistic regression model, hi-square test, Student’s t-test, frequency, and contingency tables, was used to analyze the collected data.

Also, the binary logistic regression model was employed, which depicts the relationship between k factorial variables and a nominal variable Y (value 1 = success; 0 = failure). Factorial variables are quantitative or categorical in contrast to Y, which is a binary variable with a Bernoulli-type distribution with the parameter p = P (Y = 1). The gathered data were analyzed using the SPSS program, which also featured the factorial correspondence analysis, binary logistic regression model, hi-square test, Student’s t-test, frequency and contingency tables, and the linear logistic regression model’s basic equation.

Given that Y is a nominal variable with a Bernoulli-type distribution and the parameter p = P (Y = 1), the association between k factorial variables and a nominal variable Y (value 1 = success; 0 = failure) was demonstrated using the binary logistic regression model. In contrast, Y is a binary variable with a Bernoulli-type distribution and the parameter p = P (Y = 1). Factorial variables are quantitative or categorical. Eq. (1) is the core equation for the linear logistic regression model.

(1) $$\begin{array}{rl}& \text{P}(\text{Y}=\frac{1}{x1,x2,x3\dots xk})\\ & =\frac{\exp (\beta \mathrm{o}+\beta 1\mathrm{x}1+\beta 2\mathrm{x}2+\beta 3\mathrm{x}3+\dots \dots ..\beta k\mathrm{x}k)}{1+\exp (\beta \mathrm{o}+\beta 1\mathrm{x}1+\beta 2\mathrm{x}2+\beta 3\mathrm{x}3+\dots \dots \dots .\beta k\mathrm{x}k)}\end{array}$$

where P is the likelihood that a given activity or intent will occur (yes answer), x1, x2, x3,.......xk are independent variables included in the model, and β0, β1, β2......, βk are the model coefficients derived in accordance with the interdependence of the variable values.

Conclusion

Big amounts of data from many sources are beneficial for making decisions in real-time. Integration of big data analytics has substantial challenges, including the quantity of data sources, the quality of the integrated data, and its visualization. According to the survey’s findings, 60% of Iganmu Industrial Layout have experience with big data analytics and have integrated various software programs into their engineering performance. Large firms (those with more than 250 employees) allocate significant annual funds for initiatives involving the use of big data analytics to the performance of engineering and even their engineering practice or for the hiring of specialists in the field. Companies with yearly sales revenues of up to #100 million are having trouble using big data analytics in their engineering performance due to high investment costs, security concerns, and a lack of senior backing. Businesses with annual revenues of over #150 million have run into problems with security and privacy. In addition, 90% of respondents said they had implemented an enterprise-wide strategy (which covers engineering practice), which made it easier to use big data to boost company value. 36% of these organizations have specific engineering practice strategies, and 11.2% have used big data strategies for some operations. While consulting firms (4.4%) prioritize enhancing the effectiveness of engineering practice, certain industrial (especially manufacturing) (24.2%) and e-commerce (4.4%) organizations have accepted an approach that draws attention to enlightening consumer need projections. Managers and Engineers at the company acknowledge that recent technological advancements have altered work processes, necessitating investments in staff training and development for big data analytics. Companies having a greater than five-year market history have profited from the knowledge of specialists and have seen improvements in Client Feedback and demand fulfillment, reduced cost of serving, increased efficiency, and increased inventory and asset productivity.

Engineers are aware that part of their responsibilities include thoroughly examining new techniques, instruments, cloud computing, security technologies, and statistical technological methods. Businesses may become more nimbler and resolve issues that were previously considered to be intractable with the use of big data analytics, enhancing their bottom line. A model of information that actually adds value to the business and organization itself will replace the use of a model that is mostly based on the experience of decision-makers.