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Case Study : Business Analytics Introduction
  • 4

  • Course Code: DATA4000
  • University: Kaplan Business School
  • Country: Australia

Your Task

Complete Parts A to C below by the due date.

Consider the rubric at the end of the assignment for guidance on structure and content.

Assessment Description

•    You are to read case studies provided and answer questions in relation to the content, analytics theory and potential analytics professionals required for solving the business problems at hand.

Assessment Instructions

Part A: Case Study Analysis 

Instructions: Read the following two case studies. For each case study, briefly describe:

a)    The industry to which analytics has been applied

b)    A potential and meaningful business problem to be solved

c)    The type of analytics used, and how it was used to address that potential and meaningful business problem

d)    The main challenge(s) of using this type of analytics to achieve your business objective (from part b)

e)    Recommendations regarding how to be assist stakeholders with adapting these applications for their business.

1.    How data analytics helped a firm save significantly
https://www.ey.com/en_gl/consulting/how-data-analytics-helped-a-firm-save-significantly

2.    Merck’s Manufacturing Data and Analytics Platform Triples Performance and Reduces Data  Costs by 50% on AWS
https://aws.amazon.com/solutions/case-studies/merck-mantis-case-study/

Part B: The Role of Analytics in Solving Business Problems

Instructions: Describe two different types of analytics (from Workshop 1) and evaluate how each could be used as part of a solution to a business problem with reference to ONE real-world case study of your own choosing for one type of analytics and a SECOND real-world case study of your choosing for the second type of analytics.

You will need to conduct independent research and consult resources provided in the subject.

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Part C: Developing and Sourcing Analytics Capabilities

Instructions: You are the Chief Analytics Officer for a large multinational corporation in the communications sector with operations that span India, Nepal, China, Taiwan, The Philippines and Singapore

The organization is undergoing significant transformations; it is scaling back operations in existing low revenue segments and ramping up investments in next generation products and services - 5G, cloud computing and Software as a Service (SaaS).

The business is keen to develop its data and analytics capabilities. This includes using technology for product innovation and for developing a large contingent of knowledge workers.

To prepare management for these changes, you have been asked review Accenture’s report (see link below) and publish a short report of your own that addresses the following key points:

1.    How do we best ingrain analytics into the organisation’s decision-making processes?

2.    How do we organize and coordinate analytics capabilities across the organization?

3.    How should we source, train and deploy analytics talent?

To help you draft this report, you should review the following working paper from Accenture:
https://pdfcoffee.com/accenture-building-analytics-driven-organization-pdf-free.html

The report is prepared for senior management and the board of directors. It must reflect the needs of your organization and the sector you operate in (communications).

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ANSWERS

Part A

Case Study 1: Knorr-Bremse

Industry Application

The case revolves around the Banking & Manufacturing industry. While Knorr-Bremse primarily belongs to the manufacturing sector, dealing with braking systems, the study's core challenge pertains to banking relationships and fees (Kulka, 2021).

Business Problem

Knorr-Bremse faced a significant challenge in streamlining and rationalizing its global banking fees. With decentralized banking relationships grown over decades and costs typically negotiated separately with each bank and subsidiary, there was a lack of transparency and uniformity in their banking fee structures (Schmidt, 2023).

Type of Analytics Used

Knorr-Bremse employed data analysis for financial optimization. They collaborated with EY to undertake a comprehensive review of existing bank contracts, involving business accounts worldwide and tens of thousands of transactions (Celli et al., 2021). They sought to identify incorrectly billed or otherwise reducible bank charges.

Main Challenges

  •  Complexity: The decentralized nature of banking relationships made it hard to get a centralized perspective (Nibilou, 2019).

  •  Volume of Data: With subsidiaries in over 100 locations spanning more than 30 countries, analysing the vast amounts of transaction data was a herculean task (Schmidt, 2023).

  •  Contractual Inertia: Banking fee structures, once negotiated, were rarely questioned or updated, leading to inefficiencies (Li and Wang, 2021).

Recommendations

  • Centralized Analytics Solutions: Companies should establish centralized analytics systems to offer a comprehensive view of all banking relationships, ensuring that discrepancies in banking fees are identified and rectified promptly (Allen et al., 2020). Such centralization can also promote consistency in banking negotiations, leading to more favourable terms (Kiff et al., 2020).

  • Ongoing Review: Organizations should make it a routine to periodically review and renegotiate banking contracts (Tung, 2021). This not only ensures that terms remain favourable but also that they reflect the current financial climate and organizational needs.

  • Collaborate with Experts: Collaboration with external experts, as Knorr-Bremse did with EY, can provide fresh insights and specialized tools (Journeault et al., 2021). These experts can introduce innovative solutions and strategies tailored to the unique challenges faced by the organization, enhancing their financial efficiency.

  • Internal Benchmarking: Instead of just relying on external benchmarks, focus on internal benchmarking to identify inconsistencies and potential negotiation points within the company's different divisions (O’Grady and Biswas, 2022).

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Case Study 2: Merck

Industry Application

The primary industry here is Biopharmaceutical & Manufacturing. Merck, a global biopharmaceutical company, aims to save and improve lives with cutting-edge science.

The challenge presented in this case is related to the efficiency of global manufacturing operations, particularly in data and analytics.

Business Problem

Merck aimed to enhance the efficiency of its global manufacturing operations. They required complete visibility across production lines and sites, coupled with robust data and analytics capabilities, to pinpoint areas in need of improvement (AWS, 2023).

Their legacy data platform, MANTIS, faced performance and scalability challenges due to the growing volume and complexity of manufacturing data.

Type of Analytics Used

Merck utilized data visualization and analysis on a centralized platform, MANTIS. The platform was designed to store, visualize, and analyse global manufacturing data, allowing Merck to make more data-driven decisions.

When scalability became an issue, they migrated MANTIS to AWS to leverage its vast array of services, including data storage, data integration, and data analytics (AWS, 2023).

Main Challenges

  • Scalability: The initial on-premises MANTIS platform continually hit its scalability limits due to the exponential growth of data from over 120 source systems.

  • Data Silos: Data from multiple sources led to isolated pockets of information, making it hard to provide a unified view (Pittaway and Montazemi, 2020).

  • Legacy System Limitations: The on-premises solution was both costly and less efficient than desired, especially with the ever-growing data demands.

Recommendations

  • Migration to Cloud-based Solutions: Moving platforms like MANTIS to cloud providers like AWS can offer the scalability and efficiency needed for large-scale data analytics (Vergilio et al., 2020). Cloud migration also ensures that resources are utilized optimally, with the flexibility to scale up or down based on demand, leading to cost savings (Bandari, 2022).

  • Adopt Scalable Storage Solutions: Data storage can be made more convenient and affordable by using a centralized service, such as Amazon S3 (Serhane et al., 2020). Data consistency, accuracy, and adherence to quality standards are all aided by centralized storage solutions (Mittal et al., 2023) because of improved data governance.

  • Data Integration: Use a service like AWS Glue to easily combine data from various sources, which will result in more thorough analytics (Gupta et al., 2023). With an integrated data platform, you can see the big picture of your operations, streamline your processes, and pinpoint where you're losing time or money.

  • Monitoring and Compliance: Monitor data usage with services like Amazon CloudWatch and AWS CloudTrail to stay in line with industry regulations (Mishra, 2023) and best practices. The risks and potential legal complications associated with the company's data practices are reduced through constant monitoring and compliance checks (Saleem, 2023). 

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Part B

Introduction

Since the advent of the digital age, businesses have been flooded with data, making analytics a crucial tool for gaining insights and making decisions (William, 2023). Pattern recognition, trend forecasting, and the automation of routine tasks are all areas where analytics comes into its own (Bharadiya, 2023).

In recent years, automation and predictive analytics have emerged as two of the most popular forms of analytics.

Automation Analytics

  • Definition: Asadov (2023) defines automation analytics as "the application of data and analytics tools to optimize business processes, minimize the need for human intervention, and maximize productivity."

    The e-commerce, financial, and manufacturing sectors are prime examples of industries that can benefit greatly from this method of processing data (Singhal et al., 2023).

  • Real-World Application: Automation analytics are used to improve sourcing, production, and logistics at eBay's order fulfilment centres (Shcherbakov and Silkina, 2021). Products are moved, items are picked from shelves, and orders are packaged quickly all with the help of robots and conveyor systems.

    Automation's contribution to accelerating and improving order processing has been highlighted by the recent implementation of the Kiva system, which employs robots to transport entire shelves to workers (French et al., 2021).

  • Impact: According to Zhu et al. (2022), automation analytics can help businesses like eBay save money, improve accuracy, and use their resources more efficiently. Human resources are then freed up to contribute more strategically to the company.

Predictive Analytics

  • Definition: Predictive analytics is defined as "the use of past data, statistical algorithms, and machine learning to make predictions about potential future events or developments" (Lee et al., 2022). It forecasts potential outcomes, enabling businesses to make pre-emptive moves.

  • Real-World Application: YouTube, a worldwide media hub, uses predictive analytics to provide individualized suggestions for its massive user base (Kadoić and Oreški, 2021).

    YouTube is able to make recommendations based on a user's viewing history and preferences (Alfano et al., 2021). Success of the platform can be attributed, in part, to the fact that users enjoy the individualized service they receive and continue to use it (Choi et al., 2023).

  • Impact: By predicting market trends, customer preferences, and potential challenges, predictive analytics gives businesses a leg up on the competition (Bharadiya, 2022). This bodes well for the longevity of video-sharing sites like YouTube, as well as the precision with which advertisers can target their messages.

Synergistic Benefits of Convergence

In today's interconnected digital landscape, the real power emerges when Automation and Predictive Analytics converge. By automating predictive models, businesses can respond in real-time to emerging trends, ensuring not only proactive decision-making but also instantaneous action, enhancing overall operational agility (Chakraborty et al., 2020).

Conclusion

Both Automation and Predictive Analytics have proven instrumental in driving business success in various sectors. Automation analytics, with its focus on streamlining processes, plays a crucial role in sectors with high data throughput, ensuring efficiency and cost savings.

On the other hand, Predictive Analytics, with its foresight capabilities, enables businesses to stay ahead of the curve, catering to customer preferences and market trends proactively.

As the business landscape continues to evolve, it becomes imperative for organizations to embrace these and other forms of analytics. They not only offer a competitive advantage but also pave the way for innovation, growth, and long-term sustainability. 

Part C

Developing and Sourcing Analytics Capabilities

In the dynamic landscape of the global communications sector, multinational corporations face the relentless challenge of staying competitive and innovative (Brondoni, 2020).

Essential to this endeavour is the strategic harnessing of data and analytics, which offers unparalleled insights, drives decision-making, and propels operational efficiency (Panibratov. and Klishevich, 2020).

As the digital age continues to evolve, the ability to effectively integrate analytics into organizational processes and manage its capabilities becomes a defining factor for success (Sinha et al., 2020).

This section delves into the importance of ingraining analytics in decision-making, organizing these capabilities, and sourcing the right talent to ensure a data-driven future for corporations in the communications arena.

Ingraining Analytics into Decision-making

Embedding analytics into the decision-making fabric of an organization is no longer a luxury but a necessity in today's data-driven world (Tam et al., 2022).

When analytics is deeply rooted in the decision-making process, businesses can extract actionable insights from vast data reservoirs, enabling them to anticipate market trends, fine-tune strategies, and optimize operations (Ray Barua, 2019). Such an approach fosters a proactive rather than reactive stance in the face of challenges (Kayode-Ajala, 2023).

At the heart of this transformation is fostering a data-driven culture. This means moving beyond the traditional reliance on intuition or past experiences and championing evidence-based decision-making (Wolffe, 2020). Leadership plays a pivotal role in this shift, setting the tone by consistently using and valuing analytics in strategic decisions.

Moreover, it is crucial to democratize data access, ensuring that teams across the organization can leverage analytics tools and insights relevant to their functions (Awasthi and George, 2020).

By doing so, an organization not only enhances its agility but also empowers its employees to innovate and contribute more effectively to the company's goals (Takang and Amaechi, 2023). As analytics becomes an integral part of the organizational ethos, companies are better positioned to harness the full potential of data, ensuring sustainable growth and competitive advantage.

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Organizing and Coordinating Analytics

Successfully leveraging analytics requires an organized and coordinated approach. As businesses grow and data sources multiply, a structured framework becomes indispensable (Noy et al., 2019).

The first step is deciding between a centralized and decentralized analytics model. While a centralized model consolidates analytics expertise into a singular team serving the entire organization, a decentralized approach embeds analytics professionals within specific departments or business units (Li et al., 2021).

Regardless of the chosen model, clear communication channels are paramount. Teams must collaborate seamlessly, understanding both the technical aspects of analytics and the business challenges they aim to address (Koi-Akrofi et al., 2019). This synergy ensures that the insights derived are actionable and aligned with business objectives.

Furthermore, establishing robust analytics governance is crucial. This involves setting standards for data quality, ensuring data security, and defining access permissions (Duggineni, 2023).

With myriad tools and platforms available, standardization also plays a pivotal role. Adopting standardized tools fosters compatibility, facilitating cross-team collaboration and reducing the learning curve for new members.

In essence, the organization and coordination of analytics are not just about tools and models, but about fostering a culture where data insights are seamlessly integrated into the workflow, driving enhanced decision-making across the enterprise.

Sourcing, Training, and Deploying Talent

In today's data-driven world, sourcing the right analytical talent is paramount. With the exponential growth in data generation, organizations need experts who can not only interpret this data but also translate insights into actionable business strategies.

Sourcing Talent

The first challenge is determining whether to hire in-house or to outsource (Zhong et al., 2022). In-house talent provides better integration with company culture and ensures dedicated focus on company-specific challenges (Sankar and Kedas, 2023).

However, outsourcing or partnering with specialized firms offers flexibility and access to a broader skill set, especially for specific, short-term projects (Rowe et al., 2023). Leveraging online platforms, attending industry conferences, and partnering with educational institutions can also aid in attracting top-notch talent (Bane, 2023).

Training and Development

Once onboarded, continuous upskilling becomes essential (Berglund, 2022). The analytics field is evolving rapidly, and professionals must stay abreast of the latest techniques, tools, and methodologies (Kend and Ngyuen, 2020).

Regular workshops, certifications, and courses can facilitate this learning. Mentorship programs, where seasoned experts guide newer entrants, can also be instrumental in knowledge transfer and skill development (Hussey and Campbell-Meier, 2021).

Deploying Talent

Aligning talent with business needs is crucial. Analysts should not just be confined to back offices crunching numbers; they should be integrated into business teams, understanding real-world challenges and contributing to strategy (Waizenegger et al., 2020). This alignment ensures that data insights are immediately applicable, driving swift decision-making.

Furthermore, fostering a conducive environment for these professionals is crucial (Diaz et al., 2020). Flexibility in work arrangements, providing challenging projects, and ensuring a clear career progression path can aid in retaining this often in-demand talent (Tsine, 2022).

While sourcing the right talent is a starting point, the real competitive advantage lies in continuously nurturing this talent, aligning it with business objectives, and ensuring that the insights generated are seamlessly woven into the fabric of the organization's decision-making processes (Vaz, 2021). 

 

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