Big data analytics is the application of modern analytic techniques to very large, heterogeneous data sets that include prepared, semi-structured, and unstructured data from a variety of sources and range in size from gigabytes to zettabytes. Big data is a term for datasets that are too massive or complicated for traditional relational databases to collect, maintain, and analyse in a timely manner. Four questions about Volkswagen’s automobile industry are addressed in the context below.
Task 1: Four(4) applications of using big data analysis in Volkswagen’s decision-making.
Volkswagen Group may utilize Big Data to accomplish its objectives. Big Data was a technology that gave more storage space than standard software. Likewise, big data analytics enables automakers to create future automobiles in response to changing consumer demands and requests. Since more companies shift to data-driven decision-making, businesses must support learning and engage their people to get value-added credentials in this sector. Companies should fund staff for appropriate training programs on analytical processes and tools, which will provide their team with the skills and knowledge needed to exploit data for intelligent decisions (Robinson, 2020). Here are four examples of how extensive data analysis may be used to make business decisions:
- Identifying sales trends in the car industry:
A basic line graphs depicting sales volume because its introduction summarizes market information and demonstrates sales patterns. With the help of a data analyst, an executive may immediately understand the highs and lows of sales revenue by just inspecting graphs. It provides a chance to determine the variables influencing those changes (Tiwari & Daryanto, 2020). As a result, activities become up-to-date and relevant. Furthermore, if three components are exhibited on a graph simultaneously, it is easy to determine which one works better. If the sales trend has plummeted someplace, the executive might inquire what caused the drop in sales. Prescriptive analysis, like predictive analysis, evaluates developing sales patterns, develops ideas, and resolves diverse scenarios depending on existing data.
- Improve Automobile Industry Operating Efficiency:
Businesses are now using data to optimize operations, sales techniques, and overall corporate efficiency. Volkswagen cars, for instance, are outfitted with monitors that gather data and transfer it to central computers for analysis. This assists the corporation in improving the performance of its vehicles. Individual car owners are also notified about priority repairs or services by the firm. Tesla’s autopilot technology is yet another exciting application of Big Data. Currently, Tesla records more kilometers each day than Google’s autonomous car program (Bag & Kayikci, 2020). This has created a roadmap for self-driving cars by aggregating all of this information in the cloud. Such roadmaps are ten times more precise than traditional navigation devices. The improved autonomous software assists in matching a vehicle’s speed to congestion, guiding change lanes, and self-parking even without the driver’s interaction. Diagnostic Analytics helps organizations transform complicated down into manageable and comprehensible information, which is then provided in visuals and recommendations that anybody can utilize, improving operational efficiency. Managers can identify impediments and design actions that want to expand team productivity and remove them. Organizations that do diagnostic analyses on a regular basis can establish new obstacles and change their sales technique in order to stay competitive.
- In the automotive sector, real-time data may be used to improve customer engagement and retention:
Customer support is among the most critical areas where companies must provide metrics nowadays. Companies have harnessed real-time data to provide individualized solutions and services to their clients. Volkswagen used Big Data to serve its consumers with personalized loyalty programs. The firm uses data from approximately 770 million people to provide valuable intelligence that aids the brand in increasing customer revenue and profitability. Volkswagen says that rewards programs account for 95 percent of its purchases, and it has recorded 60 percent renewal rates for more than $12 billion in incremental income. This has allowed the firm to remain profitable even through the global economic downturn (Tripathi & Vishnoi, 2020). To do this, cognitive analysis has pulled together a variety of clever technologies, including semantics, artificially intelligent algorithms, and various learning approaches such as machine learning and artificial intelligence.
- Enhanced production in the car industry:
The deployment of Big Data and Insights, including its connection with Crm and Erp platforms, allows managers to monitor the most diversified sectors and required consistency of their firm in real-time, easing judgment. The data analysis created in manufacturing, preservation, and service operations may be expanded by technology, allowing previously undetected bottlenecks to become more visible. The business’s revenue improves by finding and eliminating them. It is also crucial to note that technology enables a firm’s distinctive traits and areas where it achieves the best outcomes to be displayed more effectively. Descriptive analytics uses raw data to generate meaningful insights into the past via data gathering or data mining (Syafrudin & Rhee, 2020).
Task 2: Evaluate three(3) benefits of using big data analysis for Volkswagen
Data Analytics is a very significant aspect of a company’s growth, development, and promotion. In today’s progressive world, customers all around the world are very much engulfed each and every day with numerous advertisements for products and services (Zwitter, 2014). For drawing the consumers’ attention, the brands are becoming much more creative with new and innovative ideas. Significant data terms concerning large-scale industries, for example, the automotive sector like Volkswagen, is a no new term. But in the earlier times, analysis of such data was carried out by typing by hand and then manually analyzing, which turned out to be a pretty challenging and tiresome job. The inclusion of innovative ideas and revolution in technologies has paved the way for changing the rules concerning extensive data analysis. Advancement in software systems dramatically reduces the analytics time, providing the companies with the ability to make quick decisions which in turn helps in increase of the company’s revenue, reduces the cost, and also enhancing growth. All these together offer the brands that are capable of working faster with a number of competitive advantages and even help in targeting their customers more efficiently.
Let’s take into account the process of big data analytics used in the large-scale automobile industries like Volkswagen. It is helping the company to advance in a multitude of ways, which in turn provides several benefits, which include:
- Supply Chain Management
- Automobile Financing
- Connected Cars
- Predictive Analysis
- Design and Production
Supply Chain Management:
Generally, everyday automobile companies are required to handle massive amounts of data and components. Thus, a tremendous amount of revenue is needed to be sanctioned for these departments (Bag & Kayikci, 2020). The signs of a powerful company depend on its ability of supply chain management. The factors determining the stability of any supply chain management are:
For making innovative strategies, operations are needed to be taken care of Optimized manufacturing processes are generated Market competition and healthy conversations are to be efficiently death with (Hammerström, Giebe & Zwerenz, 2019). By incorporating the strategy of Big data Analytics into the supply chain of the company, Volkswagen is able to compare its products with the other market available products. Comparison of products depends upon the factors like reliability, cost, and quality of the constituents. Thus, the company can make a choice between the best components in the market and the ones which would help in increasing the profit of the organization.
Nowadays, big data analytics is an essential aspect of the automobile industry. And apart from mobile phones, cars are the second most accepted technological device. Today’s world is fast progressing into the era of intelligent vehicles, which provide the user with the experience of more supportive or independent driving, fuel efficiency, safety alerts, and at the same time, real-time vehicle condition recording. Connected cars can communicate interactively as well as bi-directionally with the other systems that are outside the car LAN (local area network) (Stieglitz et al., 2018). This feature is very much helpful in providing the allowance to the car to share internet access with devices both inside and outside the car.
One of the most vital features for the success of a company like Volkswagen is the prediction of customers’ problems in advance. Predictive analysis helps the company to take the necessary actions and also to take care of the better health of the vehicles mining (Syafrudin & Rhee, 2020). At the same time, it also enhances customer satisfaction and cost control measures. It helps in providing the company with insight into predicting the defects that might occur in the vehicles still under the warranty coverage period and replacing them. The efficiency of the auto parts, which are vital for a particular car, is also ensured through this process. This, in turn, helps in maintaining the reputation of the company in the market.
Data related to the driving experience in the real world helps in the improvement of some basic parameters like safety, the efficiency of an engine, fuel economy, and battery power in automobiles (Stieglitz et al., 2019). The assimilation of Predictive Analysis, Big Data Analytics, and Manufacturing Stimulations altogether can commence improvements sequences to improve the general efficiency of their workings.
Thus, in this manner, Big Data converts the designing and manufacturing processes into more efficient and well-informed aspects and even supports the delivery of a better transportation system.
Task 3: Analyse two (2) challenges of using big data analysis for Volkswagen
In today’s world, data is a very valuable asset. Although big data analytics is still in its initial stages of growth, but their importance in improving the future of the automobile industry cannot be thoroughly ignored. Today companies are advancing at a rapid speed, and so are furtherance in big technologies (Rabah, 2018). This indicates that the brands must be ready to test and adopt large amounts of data in a manner by which they become an integral feature of the information handling and analytics infrastructure. With prominent potential and opportunities, however, comes substantial challenges and hurdles, which in the standard term refers to the company’s ability to solve all the concerned hurdles for unlocking the real potential of big data analytics and its associated fields.
The significant challenges faced by the company include:
Lack of Proper Understanding of Big Data which in general language refers to the company’s failure of Big Data initiatives due to lack of understanding (Lim, Kim & Maglio, 2018). Lack of employees’ knowledge about what data actually means, its importance, its storage processing, and their sources. Data professionals of industries like Volkswagen possess knowledge about the things happening that others might not possess.
The inefficient capability of handling the ever-increasing large data sets within the databases of the companies is of the vital challenges faced by the company. Most of these data come in the form of unstructured files and also in the format of documents, videos, audio, and even text files.
The company lacks efficiency in the process of tool selection for extensive data analysis and storage (Fraga-Lamas & Fernández-Caramés, 2019). For carrying out all these processes, the company needs the employment of highly knowledgeable and skilled data professionals, a deficiency of which poses a severe threat to the future of the company.
Task 4: Evaluation of social, legal and ethical dilemmas associated with big data analysis for Volkswagen
Big data analytics is the application of algorithms to big and convoluted data sets in order to identify structures, correlations, and other observations (Martin, 2015). Although big data analytics may be used in many ways to increase strategic returns in the automotive sector, it has lately been criticised for having unethical implications for a variety of relevant parties (Zuboff 2015). Some public objections have been expressed about information leakage, important individual segmentation, and prejudice against consumers (Zwitter, 2014). These issues reveal a conflict in financial markets, in which organisations’ goals and motivations deviate from those of individuals and communities (Markus & Topi, 2015). As a result, the current system lacks comprehension of how to equitably transfer the advantages and drawbacks of big data among key stakeholders.
Volkswagen has a multitude of significant problems in maintaining accuracy to its basic ethical principles and gaining people’s confidence. Companies’ commitments to protect private information, be truthful and accountable in business operations, act with credibility in all of their transactions, be dependable, and consider their consumers equitably have all been principles that might find new implications in the Big Data environment (Loebbecke & Picot, 2015).
One of the most significant problems that companies confront is the ethical conflict that exists between their intention of achieving and utilising statistics to boost productivity and business operations and their obligation to protect stakeholders’ confidentiality (Someh et al., 2019). Sensitive data that a company gets access to, such as an authentic database of a person’s travels or interactions, is confidential and might cause harm to certain segments of the population (Landini & Noussia, 2022). People must have direct authority over how a company collects knowledge about them, as well as how that material is used and shared. Companies must begin to consider which types of projections and judgments should be permitted and which might be prohibited. In Volkswagen’s operations, big data should not obstruct human decision-making.
There are also certain social issues associated with the utilisation of Big data. The sources of social prejudice — institutionalised racism, homophobia, and social inequalities – are hidden when people’s experiences are transformed into statistics (Mullins, Holland & Cunneen, 2021). As per Carpenter (2019) Technological and data-driven “alternatives” draw attention away from real-world problems and toward appropriate solutions. These factors must be appropriately controlled by Volkswagen’s management in order to prevent social controversies while utilising big data analytics for corporate decision-making.
As mentioned by Bonatti, & Kirrane (2019) Despite the revised legislative structure, it also creates immense legal issues in terms of data protection (GDPR). Conventional methods and conceptions of information privacy may be unsatisfactory in certain cases (e.g. informed consent techniques) in the Big Data environment, while data is frequently utilised and re-used in manners that have been unthinkable while the information was obtained. Copyright infringement, data ownership, and data licencing are all key issues for Volkswagen’s operations (Da Bormida, 2021). They must also avoid security breaches and secure data security throughout the Big Data analysis process.
Big Data’s accessibility, low-cost commodity technology, and breakthrough data management and analytic tools have created a once-in-a-lifetime opportunity in data analysis history. For the first time in history, we now have the tools needed to analyse enormous data sets quickly and cost-effectively because to the convergence of these trends. These abilities are not theoretical nor minor. They indicate a substantial stride forward or a clear opportunity to achieve massive gains in productivity, revenue, and profits. The Age of Big Data has here, and these are truly revolutionary times if both engineering and business professionals continue to work and deliver on the promise.
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