COIT20253 Business Intelligence using Big Data Report 1 Sample
Assessment Task:
Assignment 1 is an individual assessment. In this assessment, you are assigned tasks which assess your unit knowledge gained between weeks 1 and 5 about big data and how it can be used for decision making in any industry. All students will have to write a “professional” business report with Executive summary, Table of Content (MS generated); Introduction; Discussion; Conclusion; Recommendations and References.
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In this assessment, you are required to choose one of the following industries: Healthcare, Insurance, Retailing, Marketing, Finance, Human resources, Manufacturing, Telecommunications, or Travel.
This assessment consists of two parts as follows:
Part A - You are required to prepare a professional report on WHY Big Data should be integrated to any business to create opportunities and help value creation process for your chosen industry.
Part B - You need to identify at least one open dataset relevant to the industry and describe what opportunities it could create by using this dataset. You can access open data source from different websites. Please try finding it using Google.
In Part A, you will describe what new business insights you could gain from Big Data, how Big Data could help you to optimise your business, how you could leverage Big Data to create new revenue opportunitiesfor your industry, and how you could use Big Data to transform your industry to introduce new services into new markets. Moreover, you will need to elaborate how you can leverage four big data business drivers- structured, unstructured, low latency data and predictive analytics to create value for your industry. You are also required to use Porter’s Value Chain Analysis model and Porter’s Five Forces Analysis model to identify how the four big data business drivers could impact your business initiatives.
In Part B, among several open source and real-life datasets, you will identify at least one dataset that is relevant to the industry you had chosen. The dataset can be a collection of structured, unstructured or semi-structured data. Using this dataset, you will first discuss why you chose this dataset among other datasets. Then, you will identify and present the metadata of the dataset. Using the chosen dataset, you will need to describe the opportunities it could create for the chosen industry.
The length of the report should be around 2500 words. You are required to do an extensive reading of more than 10 articles relevant to Big Data business impacts, opportunities, and value creation process.
You need to provide in-text reference of the chosen articles.
Your report must have a Cover page (Student name, Student Id, Unit Id, Campus, Lecturer and Tutor name) and a Table of Contents (this should be MS word generated).
Solution
Part A
Introduction
The integration of big data has emerged as a transformative force in today's rapidly evolving business landscape which has reshaped industries and redefined organizational paradigms. The sheer volume and variety of data available have paved the way for unprecedented insights and opportunities. University Assignment Help, This report will explore the multifaceted impact of big data on business initiatives which elucidate how four key drivers i.e., structured, unstructured, low latency data and predictive analytics used to intersect with Porter's Value Chain Analysis and Five Forces Analysis. The report aims to provide a comprehensive understanding of how big data drivers foster value creation by delving into these intricate interactions which can enhance operational efficiency and steer strategic decision-making across industries.
Big Data Opportunities
Enhanced Customer Insights and Personalization:
Big data analytics offers the power to delve into expansive customer datasets which can help to unveil new insights into preferences, behaviors, and trends (Himeur et al. 2021). Businesses can create personalized experiences that resonate deeply with their customers by harnessing this data. Personalization has cultivated a strong bond between the business and its customers from tailored product recommendations based on browsing history to precisely targeted marketing campaigns. This not only amplifies customer satisfaction but also fosters loyalty and advocacy which can be considered as a major parameter to drive sustained revenue growth. Personalized experiences have become a defining factor in competitive differentiation in industries such as e-commerce, retail, and hospitality.
Operational Efficiency and Process Optimization:
Big data's analytical prowess extends to scrutinizing intricate operational processes. Organizations can leverage this capability to identify inefficiencies, bottlenecks, and areas for improvement. Companies gain a holistic view of their workflows by analyzing operational data that can help to enable them to streamline operations along with reducing resource wastage and enhancing overall productivity. Integrating real-time and low-latency data empowers businesses to make agile decisions, ensuring prompt adaptation to dynamic market shifts. Industries spanning manufacturing, logistics, and healthcare can reap significant benefits from this opportunity, resulting in cost savings and improved service delivery.
Predictive Analytics for Proactive Decision-making:
The integration of predictive analytics into big data strategies empowers industries to foresee future trends and outcomes (Stylos, Zwiegelaar & Buhalis, 2021). This predictive prowess holds applications across various sectors, from retail to finance. By analyzing historical data and identifying patterns, businesses can forecast demand, anticipate market shifts, and assess potential risks. Armed with these insights, organizations can make proactive decisions that minimize risks and capitalize on emerging opportunities. In sectors where timeliness is paramount, such as finance and supply chain management, predictive analytics offers a competitive edge.
Innovation and New Revenue Streams:
Big data serves as a wellspring of inspiration for innovation. Industries can leverage data-driven insights from customer feedback, market trends, and emerging technologies to create novel products and services. By identifying gaps in the market and understanding unmet needs, businesses can design solutions that resonate with consumers. These innovations not only open new revenue streams but also position organizations as market leaders. Industries as diverse as technology, healthcare, and agriculture can leverage this opportunity to foster disruptive ideas that cater to evolving demands.
Value Creation using Big Data
Enhanced Decision-making and Insights:
Big data equips industries with a wealth of information that transcends traditional data sources. By amassing vast volumes of structured and unstructured data, businesses can extract actionable insights that drive informed decision-making (Ajah & Nweke, 2019). From consumer behavior patterns to market trends, big data analysis unveils previously hidden correlations and emerging opportunities. This heightened awareness empowers industries to make strategic choices grounded in empirical evidence, mitigating risks and optimizing outcomes. In sectors such as retail and finance, data-driven insights enable precision in understanding customer preferences and forecasting market shifts, ultimately shaping successful strategies.
Operational Efficiency and Process Optimization:
The integration of big data analytics facilitates the optimization of operational processes, delivering heightened efficiency and resource allocation. Through data-driven analysis, industries identify inefficiencies and bottlenecks that hinder productivity. This leads to targeted process improvements and streamlined workflows, translating into resource and cost savings. Moreover, real-time data feeds enable agile adjustments, enabling swift responses to market fluctuations. Industries such as manufacturing and logistics reap substantial benefits, achieving seamless coordination and reduced wastage through data-informed process enhancement.
Personalized Customer Experiences:
Big data revolutionizes customer engagement by enabling hyper-personalization. By analyzing vast datasets comprising customer behavior, preferences, and transaction history, businesses can tailor offerings to individual needs (Shahzad et al. 2023). This personalization extends to tailored marketing campaigns, product recommendations, and service interactions, enhancing customer satisfaction and loyalty. In industries like e-commerce and telecommunications, personalized experiences not only foster customer retention but also amplify cross-selling and upselling opportunities, consequently elevating revenue streams.
Innovation and New Revenue Streams:
Big data serves as a catalyst for innovation, propelling industries to develop groundbreaking products and services. By decoding customer feedback, market trends, and emerging technologies, businesses gain insights that steer novel offerings. This innovation not only fosters market differentiation but also creates new revenue streams. Industries ranging from healthcare to entertainment tap into big data to identify gaps in the market and devise disruptive solutions. This adaptability to evolving consumer demands positions businesses as pioneers in their sectors.
Porter’s Value Chain Analysis
Porter's Value Chain Analysis is a strategic framework that helps organizations dissect their operations into distinct activities and examine how each activity contributes to the creation of value for customers and, consequently, the organization as a whole (Ngunjiri & Ragui, 2020).
Porter's Value Chain Components:
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Now, applying this analysis to the impact of four big data business drivers - structured data, unstructured data, low latency data, and predictive analytics - can offer valuable insights into how these drivers influence various stages of the value chain.
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Support Activities:
1. Firm Infrastructure: Big data impacts strategic decision-making. Structured data provides historical performance insights, guiding long-term planning. Unstructured data can uncover emerging market trends and competitive intelligence, influencing strategic initiatives.
2. Human Resources: Big data assists in talent management. Structured data aids in identifying skill gaps and training needs. Unstructured data, such as employee feedback and sentiment analysis, offers insights into employee satisfaction and engagement.
3. Technology: Technology plays a pivotal role in handling big data. The integration of structured and unstructured data requires robust IT infrastructure. Low latency data ensures real-time data processing and analysis capabilities, enhancing decision-making speed.
4. Procurement: Big data enhances procurement processes (Bag et al. 2020). Structured data supports supplier performance evaluation, aiding in supplier selection. Unstructured data assists in supplier risk assessment by analyzing external factors that may impact the supply chain.
Applying the Value Chain Analysis: To illustrate, let's consider a retail business. The impact of big data drivers can be observed across the value chain. Structured data aids in optimizing inventory management and supplier relationships in inbound logistics. Low latency data ensures real-time monitoring of stock levels and customer preferences in operations. Predictive analytics forecasts demand patterns in marketing and sales which can create tailored promotions and inventory adjustments. Post-sale service benefits from unstructured data insights into customer feedback which aids in improving customer satisfaction.
Porter’s Five Forces Analysis
1. Competitive Rivalry:
Big data drivers have a profound impact on competitive rivalry within an industry. Structured data enables companies to analyze market trends along with customer preferences and competitive benchmarks which fosters strategic differentiation (Suoniemi et al. 2020). Unstructured data can provide insights into brand perception and competitive positioning such as social media sentiment. Businesses can anticipate shifts in customer demands by leveraging predictive analytics which can enhance their ability to innovate and stay ahead of competitors. Low latency data ensures real-time decision-making that allows businesses to respond promptly to competitive moves.
2. Supplier Power:
The utilization of big data drivers can reshape the dynamics of supplier power. Structured data aids in supplier evaluation which facilitates data-driven negotiations and contract terms. Unstructured data provides insights into supplier reputations that helps businesses make informed decisions. Low latency data enhances supply chain visibility which can reduce dependency on single suppliers (Singagerda, Fauzan & Desfiandi, 2022). Predictive analytics anticipates supplier performance and potential disruptions which allows proactive risk mitigation strategies.
3. Buyer Power:
Big data drivers impact buyer power by enabling businesses to tailor offerings to customer preferences. Structured data allows for customer segmentation and customized pricing strategies. Unstructured data offers insights into buyer sentiments that can influence marketing and product strategies. Predictive analytics helps forecast consumer demand which can allow businesses to adjust pricing and supply accordingly (Bharadiya, 2023). Low latency data ensures quick responses to changing buyer behaviors and preferences.
4. Threat of Substitution:
Big data drivers can influence the threat of substitution by enhancing customer loyalty. Structured data-driven insights enable businesses to create personalized experiences that are difficult for substitutes to replicate (Sjödin et al. 2021). Unstructured data offers insights into customer feedback and preferences which can provide support for continuous improvement and product differentiation. Predictive analytics anticipates customer needs in order to reduce the likelihood of customers seeking alternatives. Low latency data ensures quick adaptation to market shifts that can reduce the window of opportunity for substitutes.
5. Threat of New Entrants:
The incorporation of big data drivers can impact the threat of new entrants by raising barriers to entry. Structured data enables established businesses to capitalize on economies of scale and create efficient operations which makes it challenging for newcomers to compete. Unstructured data provides insights into customer preferences to support brand loyalty. Predictive analytics helps incumbents anticipate market trends which enable preemptive strategies against new entrants. Low latency data facilitates real-time responses to emerging threats which can reduce the vulnerability of established players.
Conclusion
The integration of big data drivers into business strategies represents a pivotal juncture in the ongoing digital transformation. The confluence of structured and unstructured data along with the power of low-latency data and predictive analytics can alters the fundamental fabric of industries. From optimizing processes to driving innovation, big data's imprint is visible across the value chain and competitive dynamics. As organizations harness this potential, they position themselves to thrive in an era where data-driven insights are the cornerstone of informed decision-making and sustainable growth. By embracing big data's capabilities, businesses are poised to navigate challenges, seize opportunities, and unlock the full spectrum of possibilities presented by the data-driven future.
Part B
Dataset identification
The dataset includes several parameters which are related to the retail industry. The dataset focused on date-wise CPI and employment rate with the weekly holiday. The dataset can help to identify the consumer price index along with the employment rate in the retail industry and the impact of holidays on them. The dataset is openly available and consists of three data files in which the considered dataset is the ‘Featured data set’ (Kaggle, 2023). It can be identified as one of the most suitable datasets that have provided structured data in order to analyze different outcomes.
Metadata of the chosen dataset
The selected dataset pertains to the retail industry and encompasses parameters such as Store, Date, Temperature, Fuel_Price, and various MarkDown values (MarkDown1 to MarkDown5), along with CPI (Consumer Price Index), Unemployment rate, and IsHoliday indicator. This metadata provides crucial insights into the dataset's composition and relevance within the retail sector.
The "Store" parameter likely represents unique store identifiers, facilitating the segregation of data based on store locations. "Date" captures chronological information, potentially enabling the analysis of temporal trends and seasonality. "Temperature" and "Fuel_Price" suggest that weather conditions and fuel costs might influence retail performance, as these factors impact consumer behavior and purchasing patterns.
The "MarkDown" values could denote promotional discounts applied to products, aiding in assessing the impact of markdown strategies on sales. Parameters like CPI and Unemployment offer a macroeconomic context, possibly influencing consumer spending habits. The "IsHoliday" parameter indicates whether a given date corresponds to a holiday, offering insights into potential fluctuations in sales during holiday periods.
Business opportunities through the chosen dataset
The analytical findings indicating a lower average unemployment rate on holidays and a higher average Consumer Price Index (CPI) during holiday periods hold significant implications for the chosen industry. These insights unveil a range of strategic opportunities that the industry can capitalize on to drive growth, enhance customer experiences, and optimize its operations.
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Figure 1: Consumer price index comparison
(Source: Author)
Increased Consumer Spending: The lower average unemployment rate on holidays suggests a potential uptick in consumer spending power during these periods. This provides a prime opportunity for the industry to design targeted marketing campaigns, exclusive offers, and attractive promotions. By aligning their product offerings and marketing strategies with consumers' improved financial situations, businesses can drive higher sales volumes and revenue.
Customized Product Assortments: The availability of higher disposable income on holidays opens the door to curating specialized product assortments. Retailers can introduce premium and luxury items, cater to aspirational purchases, and offer exclusive collections that cater to elevated consumer spending capacity. This approach enhances the perceived value of products and creates a unique shopping experience.
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Figure 2: Unemployment rate comparison
(Source: Author)
Strategic Inventory Management: Capitalizing on the lower unemployment rate on holidays can drive retailers to anticipate increased foot traffic and online orders. This presents an opportunity for strategic inventory management. Businesses can optimize stock levels, ensure the availability of popular products, and align staffing resources to accommodate higher consumer demand, ultimately enhancing customer satisfaction.
Enhanced Customer Engagement: With a heightened CPI during holidays, businesses can strategically invest in enhancing customer experiences to match the anticipated premium pricing. This could involve personalized shopping assistance, concierge services, or engaging in-store events. Elevated customer engagement fosters brand loyalty and differentiates the business in a competitive market.
Dynamic Pricing Strategies: The observed correlation between higher CPI and holidays enables the adoption of dynamic pricing strategies. By leveraging these insights, the industry can implement flexible pricing models that respond to demand fluctuations. This approach optimizes revenue generation while maintaining alignment with consumer expectations and market trends.
References
Ajah, I.A. & Nweke, H.F., 2019 ‘Big data and business analytics: Trends, platforms, success factors and applications’, Big Data and Cognitive Computing, vol. 3, no. 2, p.32. https://doi.org/10.3390/bdcc3020032
Bag, S., Wood, L.C., Xu, L., Dhamija, P. & Kayikci, Y., 2020 ‘Big data analytics as an operational excellence approach to enhance sustainable supply chain performance’, Resources, Conservation and Recycling, vol. 153, p.104559. https://e-tarjome.com/storage/panel/fileuploads/2020-01-12/1578836612_E14164-e-tarjome.pdf
Himeur, Y., Ghanem, K., Alsalemi, A., Bensaali, F. & Amira, A., 2021 ‘Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives’, Applied Energy, vol. 287, p.116601. https://doi.org/10.1016/j.apenergy.2021.116601
Kaggle (2023). Retail Data Analytics. Kaggle. Available at: https://www.kaggle.com/datasets/manjeetsingh/retaildataset
Ngunjiri, J.N. & Ragui, M., 2020 ‘Effect of value chain on competitive advantage in the insurance industry in Kenya’, International Academic Journal of Human Resource and Business Administration, vol. 3, no. 8, pp.172-193. https://iajournals.org/articles/iajhrba_v3_i8_172_193.pdf
Shahzad, A., Kayani, H.U.R., Malik, A.A., Raza, M.A. & Saleem, A., 2023 ‘BIG DATA SECURITY, PRIVACY PROTECTION, TOOLS AND APPLICATIONS’, Pakistan Journal of Science, vol. 75, no. 02, pp.353-372. https://doi.org/10.57041/pjs.v75i02.850
Singagerda, F., Fauzan, A. & Desfiandi, A., 2022 ‘The role of supply chain visibility, supply chain flexibility, supplier development on business performance of logistics companies’, Uncertain Supply Chain Management, vol. 10, no. 2, pp.463-470. http://dx.doi.org/10.5267/j.uscm.2021.12.005
Sjödin, D., Parida, V., Palmié, M. & Wincent, J., 2021 ‘How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops’, Journal of Business Research, vol. 134, pp.574-587. https://doi.org/10.1016/j.jbusres.2021.05.009
Suoniemi, S., Meyer-Waarden, L., Munzel, A., Zablah, A.R. & Straub, D., 2020 ‘Big data and firm performance: The roles of market-directed capabilities and business strategy’, Information & Management, vol. 57, no. 7, p.103365. https://doi.org/10.1016/j.im.2020.103365
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