Big Data Concepts Case Study of AUDI Assignment Sample

Pages: 9 Words: 2145

Introduction

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Big data is sign of the next big thing for companies to gain a competitive edge. It becomes important for the companies to understand the process and mechanism of how big data add value to the business. The companies gain big data from various sources and use these data in their long term operation in future. To understand the need of customers and target audience the big data plays a crucial role in every business whether it is small or big. It is a combination of all the tools and process related to managing the huge data for the company. The research is done on the basis of case law providing the answers to the questions

Evaluation on Technological characteristic use to describe the big data with existing types of analytics and Analytical services used by AUDI

Big Data includes a huge amount of information that is not being processed by traditional data storage. It is a collection of data in large volumes and large which is used in companies' tools. It is used by many international companies to process the data and company of many organizations (Qi, 2020). The data flow can exceed 150 Exabytes per day before replication. There are 5 V’s of big data that are value, volume, variety, and velocity. Big data has vast volumes of sources like machines, media platforms, human interactions, networks, and many others. The variety of big data can be unstructured, structured, or semi-structured that are collected from many different source in which veracity describes the reliability of the data. Value is an important characteristic of big data which is stored, processed, and analyzed.

Data analytics is the examination of data to identify trends, extract insights, and answer questions. This is also known as business analytics to form strategies and in the decision-making process in business. There are 4 types of data analytical services which are descriptive analytics, diagnostic analytics, prescriptive analytics, and perspective. To understand the current situation in the market, Audi can use descriptive analytics (Dremel, et. al. 2018). To figure out the leverage the company can use diagnostic analytics. To determine the trajectory of the situation the company can use predictive analytics. Prescriptive analytics can help the company to consider current and future plan strategies.

Recommendation to Audi upon following the strategic path to apply big data analytics or use a step-by-step approach championed by one business department (e.g., sales and marketing) to prove and implement big data analytics applying Davenport and Harris (2007, p. 118), Porter and Heppelmann (2015), and Ross et al. (2013)

The advancement of technology in the automobile industry includes automatic indicators, self-driving cars, and maintenance. Many companies are offering these technologies like Tesla and Faraday in a competitive market. Data analytics was introduced by the company Audi which is powered by the marketing and sales team. These analytics are divided into three parts i.e. advancing, leveraging, and enabling. Advancing is a business requirement that includes relevant business and descriptive analytics presented to the Audi technology team.

A digital innovation hub is a set-up to replace the outsourcing delays and lack of support. The big data analytics are the basic infrastructure required for data analytics which adds more responsibility and roles for the Audi team. The sales and marketing team of the company has to add the requirement with a digital innovation hub (Whitler, 2021). The second stage of analytics services is enabling decisions of the makers to provide facility of the data insight with the experience and provide guidance to explore the benefit of data. The main aim of enabling includes efforts of the team in sales, IT, and digital innovation hub in Audi. The last stage is leveraging Audi's data and providing analytics services over the company. The department of sales and marketing prepares the budget and supports internal customers with managing the project and profit-oriented design of analytics services.

The best solution for AUDI upon the provided Exhibits (1, 2, 3, and 4)

The provided exhibits include Audi's latest electric car models. These exhibits are the most interactive and performance-focused which include efficient design, digital features, and sustainability. Audi is the first automotive company to exhibit in Miami. Companies that gather exclusively as a financial partner at events, the brand Audi with the four rings is providing creative input. The exhibition period of IAA mobility in Munich depends on the availability of test models to drive on the spot with electric models. In 2015 Audi handled the issues of sustainability, environment, features, and designs in their exhibition (Bondarenko, et. al. 2020). Automotive innovation became the essential tool for the company to survive in the competitive market with their customers.

The exhibit 1 provides the model of components used in software for better solutions. Exhibit 2 gives the solution on the development phase for the concepts of building and buying. Exhibits 3 maintain the company’s benefits by providing reasoning through the solutions by going against the limits of the organization. The Exhibits 4 work upon the coordination for the project development giving solution to the analytics of the company.

Identification upon the most valuable data sources which is used by AUDI with the help of an example relating to the uses of each data source

The important thing that the company can do is to ensure the consistency in automobile products should be maintained properly. Audi needs to maintain their product stuff by changing oil, good quality breaks and tiers, and filters. Data is a lubricant for the company which is the very necessary raw material for the future production of the products. The data should be efficient and systematic which provides value to the information. Audi can simplify its team works, predict mistakes, and process more efficiently with the use of big data. It is a strategic appreciation of the data as a source with separate means of production. Big data is a dynamic and reliable utilization of process and product data. It changes the organizational culture and process of the department by relating and linking the data sources.

The most valuable data source used by AUDI for the future benefit is the information technology which helps in evaluating Data science related to sales, information related to production department, finance data and reviewing the customer’s feedback. Audi wanted to advance their IT efficiency, in order to reduce capital, operational cost and to create a greener infrastructure, which are the key projects within the company sustainability approach. The company wants to increase their performance on SAP system with more flexible and scalable. Audi’s IT department has faced some business challenges which include increasing employees demand, customers need, new technologies, and growing competition. The company decided to implement a private cloud which would be the best option for the future term. A company by constructing a complete virtualized infrastructure will able to remove computing capability on demand.

Important facts about big data analytics to be called as buzzword or emerging and sustainable technology trending in daily life of customers, affecting the importance of big data analytics

Big data analytics is an advanced systematic technique used against various big data set that contains semi-structured, structured, and unstructured data from diverse sources, and it is in different sizes. Big data is a set of data that is used by the company in its future performance. It is in the form of high velocity, volume, and variety which are driven by artificial intelligence and social media. Big data analytics can help Audi to increase better and faster decision-making, modeling, and forecasting of future outcomes and improve business intelligence. Big data analyze the data from devices, video, sensor, transactional application, and social media which empower a company to be data-driven (Hariri, et. al. 2019).

The big data analytics also estimates customer needs and possible risk which helps in creating new product and service. The understanding can be made by following the example of Audi by screw and bolt analysis. The company has more than a thousand bolts and screws which make machines smooth and tighter. The case company has electric screwdrivers that permanently measure the working angles being applied. If this constraint exceeds the limit defined for each bolt or screw, the engine automatically switches off which usually happens within 2 seconds of the start of the process (Dremel, et. al. 2018). But team members of the Audi Production Lab have found that so much time is not necessary. The screwdrivers then switch off immediately; the employees save a lot of time and are less rushed for the next bolt.

Solutions to the obstacles encountered by the company for the progress by analytics ladder

Data analytics is important for the company to make strategies and to become successful. Audi should implement the right strategies which will help the company get its finances on track and insufficient data solutions. The company is mapping goals with the help of experts and identifying the real area of company revenue. The future of automobiles has come with new designs and concepts. The case company is providing these special features in a new era of automobiles. Audi must focus on maximizing the value of data to strengthen its business performance.

The company has its products with interactive space, better quality, innovation, and focus on the needs of the customers. The leaders have to examine the toolkits required in the manufacturing process to make strong business analytics. This will improve the position of the company in the market with efficient planning and production process. Data modeling helps the company to measure dimensional hierarchy and calculate fields to work with complex data efficiently (Fletcher, et. al. 2019). The company can display multifaceted analytics with good quality style guides by using charts, colors, and widgets to run the business smoothly.

Conclusion

This report answers the questions relating to the use of big data in business operations. The company uses data mostly for the use of future performance to run its business efficiently. Big data provides useful data to a large company, in a variety of forms with a larger value. The above research is made to highlight AUDI’s marketing and sales strategies with the use of big data. The big data analyses are done with case study applying different strategies the company use to improve its performance in the future (Díaz-de-Arcaya, et. al. 2020). the company has a strong brand image in the automotive market which is the best competitive advantage for the company. The report concludes the business objectives related to the use of big data in large geographical markets with its innovative design, feature, quality model, and effective advertising strategies.

References

Bondarenko, I., Bondarenko, B. and Gonchar, O., 2020. Expositional design of the Audi museum in Ingolstadt: representation of the brand’s characteristics in automobile industry history. Muzeológia a kultúrne dedi?stvo, 8(1), pp.19-30.

Di, J., Fletcher, J.E., Fan, Y., Liu, Y. and Sun, Z., 2019. Design and performance investigation of the double-sided linear induction motor with a ladder-slot secondary. IEEE Transactions on Energy Conversion, 34(3), pp.1603-1612.

Díaz-de-Arcaya, J., Miñón, R., Torre-Bastida, A.I., Del Ser, J. and Almeida, A., 2020. PADL: A modeling and deployment language for advanced analytical services. Sensors, 20(23), p.6712.

Dremel, C., Wulf, J., Maier, A. and Brenner, W., 2018. Understanding the value and organizational implications of big data analytics: the case of AUDI AG. Journal of information technology Teaching Cases, 8(2), pp.126-138.

Hariri, R.H., Fredericks, E.M. and Bowers, K.M., 2019. Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6(1), pp.1-16.

Qi, C.C., 2020. Big data management in the mining industry. International Journal of Minerals, Metallurgy and Materials, 27(2), pp.131-139.

Tabesh, P., Mousavidin, E. and Hasani, S., 2019. Implementing big data strategies: A managerial perspective. Business Horizons, 62(3), pp.347-358.

Whitler, K.A., 2021. Positioning for Advantage: Techniques and Strategies to Grow Brand Value. Columbia University Press.