The Analytics Problem of the Audi Organization (Thesis Sample)

Data analytics problem at AUDI AG
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Introduction.
For one to understand the organizational implication and value of data analytics, in its presentation, the organization AUDI AG attempt and highlights extensive data analytics implementation on the organization highlights an OME situation in the automotive industry, thereby resulting in problems arising in big data analytics technology which may be entailed in such organizations. Students are helped in grasping technical characteristics, organizational implications, and data analytics values and the services types, which are distinct in analytics (Davenport et al., 2007). In ramping up the big data analytics topic, AUDI engages design and industry experts and external consultancy, the IT Consult. The big data analytics problem is presented to Hortensie, who is a manager aspiring at AUDI. She gained big data analytic interest firmly hence positioning it within AUDI after receiving the task.
First, the potential of big data analytics was heard in the ''Data Scientist'' article, and ever since, Hortensie had been learning of the companies evidently profiting from big data analytics, an example being Netflix. For its quality and precision for cars, as a company, AUDI made itself to be known and building together with their progress in innovating engineering. Still, it was not known for data scientists on-site. So, Hortensie had no thought for possibilities in big data analytics at AUDI company, but her opinion was changed fundamentally by Nicolas’s assignment.
Challenges
Challenges being faced by the AUDI organization include; data analytics unraveling. So, to curb this challenge, Hortensie arranged a meeting with team members and discussed briefly with the other members of the board if they will participate in investing a unified organization of data analytics to gain in return leverage to all the departments of big data analytics (Delen et al., 2013). After all, Hortensia determined that the future organizational unit is to deliver as a service analytic. Hence analytic service insight will be received by standardizing an interface like an analytics platform.
The second challenge the organization faces is how to bring data analytics to the organization. Matthias identified an external service provider, and he was a leading IT consultancy. Hence, after having a successful board meeting, the leading IT consultancy was given the opportunity to identify potential work models for the future and also to help in the identification of potential AUDI use cases as well as implementing the use cases in collaboration with the future task forces.
The third problem of AUDI is the achievement of data analytics. Hence, the parties adopted an attitude that values data concerning data-specific technical infrastructure. To overcome the challenge, AUDI should institutionalize a standardized way of sharing data across the teams, whether manufacturing or sales. Together with organizing data and technical warehousing systems, the standards were to be ensured as soon as possible have been established and facilitated along a change management plan.
Specific data for the new markets were gathered independently to each market, and this was not always available in the right richness and quality. With the analytical model being built, the biggest continuous challenge during delivering and developing of Micro Targeting service is getting acquainted with the market-specific data. Hence, delivering service efforts to newly acquired customers did not depreciate but was maintained on the same levels. Two major challenges were posted out from developing a new model of analytics, which would be seen predicting the future order entry. First, data used from AUDI always were incomplete, and second, entry orders were previously entered and elevated by unknown effects; hence, they were misinterpreted by the analytics model. These challenges were solved by designing Demand Analysis for the analytics service, which would be seen anticipating entry orders three months in advance.
Change management
Advancing: Marketing and sales initiated departmental projects for data analytics held accountable for success in their advancing stage. At this stage, by using partners externally, AUDI received analytics competence (Porter et al., 2015). The IT department played a passive role during this stage of change management by only responding to the individuals who initiated data analytics projects.
Enabling: the second stage of AUDI's journey towards change management, analytics competencies were built in the IT department and also in the hub for digital innovation. By isolating activities technologically, the IT department data analytics was enabled by insourcing sometimes or carrying out tasks taken by digital innovation hub or external consultancies previously. Through a more advanced technology infrastructure, the development of analytics services was done. During this stage, individuals who provide analytics services were operating still largely on center-based cost. But the executive management centrally reduced the amount budgeted, which was granted for analytics. Through business cases developing for several analytics services, the marketing and sales department's strategy and data analytics unit engage on calculating payments with a view of establishing a profit-centered within AUDI.
AUDI's organizational mandate of establishing a data analytics stage journey is leveraging. According to the AUDI organization, the departments leverage competencies analytics of the digital innovation hub and the IT department to provide centrally analytics-as-a-service. At this stage, the IT department is seen being fully responsible for the technical tasks,