DataCraft for Business: Applied Analysis Toolkit

A detailed evaluation of Starbucks' data-driven business strategies and applications of data science techniques.

Table of Contents

Introduction

Starbucks is a popular American coffee chain that operates on a global scale. The Seattlebased business was established in 1971 by Jerry Baldwin, Zev Siegl, and Gordon Bowker. Starbucks has expanded from its original 1971 location in Seattle to include more than 30,000 retail outlets throughout 83 markets. Over the years, Starbucks has been recognised for its high-quality coffee, trendy and pleasant locations, and exceptional customer service. The selection of baked goods, teas, and other consumables offered by the company has expanded. In this research, I present and examine the ways in which Starbucks might use data science and AI to expand their business and enhance profits. How AI can help Starbucks attract and retain more customers. With the use of AI, Starbucks can not only capitalise on the food business but also address supply chain disruptions, cost management, and quality control challenges. In this evaluation, I will focus on providing a detailed analysis and description of four primary types of analysis methods.

  • Data collection
  • Data management
  • Communication and Visualisation
  • Decision support.

Business Strategy

Starbucks has over 31,000 locations in 82 countries, making it the largest coffeehouse chain in the globe. The company's success has been driven by its emphasis on quality, customer experience, and corporate responsibility, which includes initiatives such as the ethical sourcing of coffee beans and the reduction of its ecological footprint. Effective inventory management is essential for Starbucks keep up with client demand and prevent surpluses. Predicting demand and optimising inventory levels with AI-powered inventory management solutions may help Starbucks cut down on stockouts and waste. Starbucks has made substantial investments in digital technology over the years to improve the customer experience, expedite operations, and gain customer behaviour insights. In order to contend with their primary rivals like Dunkin' Donuts, Peet's Coffee, Caribou Coffee and Tim Hortons. They have already implemented the following digital transformation initiatives:

  • Mobile Ordering and Payment
  • Digital Rewards and Loyalty Programs
  • Digital Menu Boards
  • Data Analytics and AI

Starbucks uses data analytics and AI to understand customer behaviour and improve operations. AI optimises its supply chain and ensures product availability. Starbucks' digital transformation has improved the customer experience, streamlined operations, and yielded consumer behaviour insights. Focusing on digital technology has allowed the company to remain ahead of the competition and maintain its leadership position in the coffeehouse industry. It also employs data analytics to personalize the consumer experience and make recommendations based on customer preferences. The key to efficient manufacturing and minimal waste for Starbucks is precise demand forecasting. In order to effectively forecast demand, AI systems may examine past data, external factors like weather and events, and other variables. Quality assurance means making sure all of Starbucks goods and services are of the same high standard. Quality concerns may be proactively addressed with the use of AIpowered systems by analysing data from several sources, such as customer feedback and product testing.

Data Requirements

For the purpose of producing a holistic assessment of the company's performance and informing strategic decision-making, a full analysis of Starbucks would require a combination of data pertaining to the company's finances, customers, markets, and operations, as well as social media. The management of data in a large organisation may be a difficult undertaking; however, there are a number of actions that can be done to ensure that data is managed appropriately and utilised in an efficient manner. The following are some important measures that should be taken: Create a data management strategy. The goals of the company's data management as well as the policies and procedures for collecting, storing, and using data should be outlined in this strategy. Starbucks needs to determine all of the internal sources that contribute to the data that is generated. This comprises social media channels, point of sale (POS) systems, as well as platforms for customer relationship management (CRM). In addition to this, the management and use of data needs to be governed by a set of procedures, standards, and norms. The storage of data should be centralised since this will help ensure that it is handled appropriately and is easily accessible to those who require it. This is something that can be accomplished by data warehouses or by other types of centralised data storage systems. This should comprise storage that is located on the business's premises, storage that is located in the cloud, or a hybrid solution that mixes the two. They have the option of utilising industryleading cloud providers such as Amazon, Google, or Microsoft Azure. Data Requirements When it comes to the data, security is an essential component to take into consideration. Companies that lack an adequate security system leave themselves open to attack by third parties, which in turn provides an opportunity for their rivals to gain a market share advantage. Should create access controls to guarantee that only authorised individuals have access to the data, since data security prevents important firm data from being compromised. Utilising role-based access control, two-factor authentication, and encryption are all potential components of this measure.

Data Analysis Problems

Due to poorly managed data, the Starbacks have featured numerous examples in recent years. Over time, expansion into new markets and the opening of new stores contributed significantly to the company's overall growth. Due to the company's rapid growth, there has been a dramatic increase in the amount of data it must store and manage. It can be challenging for organisations to manage such a large amount of data if they do not have efficient data management systems in place. Before any breach occurred, they had not done enough to ensure the safety of the data and its confidentiality. During this time, there were multiple reports of data breaches due to lack of security. Customers' trust in the company will be eroded as a result of these issues, which will have a major effect on the company's reputation. In recent years, Starbucks has experienced the following real-world data issues. Below are the real world example for the data problems when unable to manage the data. In 2015, customers of Starbucks reported incidences of gift card fraud, in which the cash on their gift cards were depleted without their knowledge or agreement. It is thought that hackers gained access to users' accounts through the Starbucks mobile app, which led to the fraudulent purchase of gift cards. After then, the hackers were able to move money from the gift cards of other customers to their own accounts, effectively stealing the money.(Fox, 2015) Inaccurate sales forecasting: In 2018, Starbucks overstated its sales projection, which led to a reduction in the growth rate of sales. Because sales projections were not met, the value of the company's stock dropped by more than nine percent, as stated in an article published by CNBC. (Kate Rogers, 2018) Starbucks Coffee Korea Co was given a fine of 10 million won (about $9,000) by South Korea's communications authority as a result of the company's disclosure of the personal information of hundreds of its customers.(Starbucks fined US$9,000 for Korea personal data breach, 2018 )

Data collection

The process of acquiring data is essential to research because it opens the way for the collection of high-quality data that can then be analyzed and used to draw conclusions and make decisions based on accurate information. According to the goal of collecting data is to get rid of the problem of "data islands." No matter what kind of data it is—structured, unorganized, or discrete—without data collection, data from different sources can only be separate and useless. Data capture is the process of putting these data into the data centre, putting together the scattered data, and then analysing these data in depth. Based on how big data sources are categorised, data collection can be broken down into different groups. (Li, 2021) Few of them are listed as below.

  • Collection of system file log : Analysis of system log files is a common method of system administration. These include asynchronous data collecting, temporal peculiarities in data representation, and the use of varying message lengths and formats for reporting data.(Wei et al., 2005)
  • Network big data collection : Web data collection is the process of gathering information about the web using means such as web crawlers and open application programming interfaces. This technique is able to collect and organise previously unstructured material found on the web and save it to a single local file. It allows for the storage of media like photos, videos, and audio files, and they may be linked to their respective bodies instantly.(Li, 2021)
  • Software program access : In order to better understand various facets of a company, organisation, or system, it can be helpful to collect data for analysis using software. Because the data is collected through software, businesses have complete freedom to tailor the process to their specific needs. This gives them control over both the data and the software they use. However, software analysis requires meticulous planning, the selection of suitable software tools, and an eye for data quality and pre-processing.
  • Provide database and data file : Data file data, Database records and data stored in a dataset
  • Discrete data or random data selection

Data management

The act of arranging, storing, safeguarding, and sustaining data during its entire existence is referred to as its "lifecycle." It entails a wide variety of practices and actions with the goal of guaranteeing the usefulness, accessibility, and safety of the data. The process of processing data inside an enterprise is known as data management, and it involves both the technological and organisational components of this process. Data governance, data quality, data integration, data storage and infrastructure, data security and privacy, data lifecycle management, metadata management, and data access and provisioning are some examples of the other types of management.

Companies that manage power grids, thanks to advances in "big data" technology and the rapid development of "smart grids," have amassed a large amount of data, and it's not only data generated by the power grid itself. The planning and operation of the electrical grid is also heavily influenced by many extraneous factors, such as the economy, society, government regulations, climate, user attributes, geographic location, etc. Data comes in numerous forms, including the organised and unstructured varieties as well as the real-time, near-real-time, and historical varieties. This complicates data management and mining. (F et al., 2020)

Communication and Visualisation

The ability to effectively communicate and visually display data is a crucial part of any data. They entail laying down information in a way that's easy to digest, so that others may share and learn from it. Data-driven decision-making, cooperation, and the ability to understand complex information are all aided by effective data communication and visualization in the workplace. If the information is presented in a style that is both aesthetically appealing and easy to understand, users will be able to quickly absorb the most important insights and make well-informed judgements. Visualization Technology: Commonly, statistical graphics and data

visualisation are regarded as relatively recent developments. Formulating a research question, collecting data, cleaning the data, selecting a chart type and tool, preparing data, and ultimately constructing a chart are the most fundamental steps in data visualisation. In today's data-driven corporate environment, data visualisation is crucial, and it has been extensively used to assist decisionmaking that is closely linked to the key revenue streams of many industrial companies.(Muskan et al., 2022; Wei et al., 2005)

Data communication: To make sure your data visualisation helps your audience reach the idea or conclusion you want them to, communication should be clear and thoughtful. Data science workers are working together more and more on big projects before sharing their thoughts with a wider audience through visualisation. work has modelled how data science teams, which often have different jobs and ways of working, share their knowledge with outside stakeholders. (Pang et al., 2022)

Decision support

Decision support in data science assists organisations and people in making decisions that are better informed and driven more by data by integrating approaches from data science with domain expertise. It is helpful in a wide variety of decision-making domains, including corporate strategy, resource allocation, risk assessment, process optimisation, and many more. These domains span many different sectors.

For instance, since evidence-based clinical decision support systems (CDSS) typically make objective judgements based on clinical data, ignoring patient preferences. A problem that needs to be thought about now is how to give a particular patient the best and most personalised medical plan. We suggest using patient experience data and clinical guidelines to provide evidence to assist patient-oriented clinical decision-making.(Wu & Xiao, 2021)

Artificial intelligence has greatly improved data analysis for decision-making during the past decade. The subject of artificial intelligence (AI) is constantly expanding, and now focused on enhancing decision-making procedures by utilising cutting-edge AI approaches and tackling difficulties such as interpretability, fairness, and ethical concerns. Traditional assistant decision-making models have long decision-making times and sluggish convergence. Big data mining technology uses artificial intelligence algorithms to quickly find hidden useful information in complicated and multi-scale enormous data. Based on the original "six database" decision model and data mining characteristics, builds a deep learning algorithmbased big data decision support model and thoroughly explains its working principle and operation method.(J. Wang et al., 2021)

Critical Analysis and Evaluation of the Applications of the Techniques

Data collection at Starbucks

As the first and most crucial stage in any endeavour, data collection is the process of gathering and evaluating information about customers, transactions, and other pertinent data points inside Starbucks locations and digital platforms. Point-of-sale systems, smartphone applications, loyalty programmes, and internet interactions are just some of the common methods through which this information is collected. While collecting consumer data can yield useful insights, businesses like Starbucks must safeguard the privacy of their customers' information and adhere to all relevant legislation. Methods of acquiring preliminary data are illustrated below. Starbucks point-of-sale (POS) system keeps track of the item(s) purchased, the total price, and the method of payment. Information on a purchase might include what was purchased, when it was purchased, and how much was paid. This data can shed light on the most popular products, pricing trends, and overall business activity. Starbucks Rewards, their customer reward programme, is very well-liked. Customers' coffee purchases may be monitored using the Starbucks app. Starbucks' precise food and drink suggestions are the result of the application of the cloud-based artificial intelligence engine Digital Flywheel. (Pakapol, 2023) Use the Starbucks mobile app to place an order, add special touches, and pay on the go. The programme records the user's movements, purchases, and current location. Customised advertisements, shop recommendations, and regional consumer behaviour data are just a few examples of how the Starbucks Mobile App might use a user's device's location.

Data management at Starbucks

It's not just Starbucks Consolidated big data storages hold and draw data from several distributed databases that are maintained by the various dispersed business units, which is useful for making company-wide management decisions. (Milosevic et al., 2021) Due to its outdated technology, organizations confront various issues handling large financial data. Point-of-sale (POS) systems, mobile applications, consumer feedback, loyalty programmes, and social networking platforms are just some of the sources they've previously adopted for data collecting. Also, for the records keeping Starbucks certainly makes use of a powerful data storage system to manage the massive amounts of data it receives. Data such as customer profiles, transaction records, inventory records, and marketing campaign information may be stored on-premises or in the cloud. Starbucks can better understand its overall performance and pinpoint areas for improvement by combining data from many sources, including sales figures, customer reviews, and details about the company's supply chain. Starbucks uses data As the first and most crucial stage in any endeavour, data collection is the process of gathering and evaluating information about customers, transactions, and other pertinent data points inside Starbucks locations and digital platforms. Point-of-sale systems, smartphone applications, loyalty programmes, and internet interactions are just some of the common methods through which this information is collected. While collecting consumer data can yield useful insights, businesses like Starbucks must safeguard the privacy of their customers' information and adhere to all relevant legislation. Methods of acquiring preliminary data are illustrated below. Starbucks point-of-sale (POS) system keeps track of the item(s) purchased, the total price, and the method of payment. Information on a purchase might include what was purchased, when it was purchased, and how much was paid. This data can shed light on the most popular products, pricing trends, and overall business activity. Starbucks Rewards, their customer reward programme, is very well-liked. Customers' coffee purchases may be monitored using the Starbucks app. Starbucks' precise food and drink suggestions are the result of the application of the cloud-based artificial intelligence engine Digital Flywheel. (Pakapol, 2023) Use the Starbucks mobile app to place an order, add special touches, and pay on the go. The programme records the user's movements, purchases, and current location. Customised advertisements, shop recommendations, and regional consumer behaviour data are just a few examples of how the Starbucks Mobile App might use a user's device's location. Data collection at Starbucks Data management at Starbucks analytics to gain insight and direct business strategy. Understanding consumer behavior, optimizing retail operations, and creating focused marketing campaigns may need a combination of descriptive, diagnostic, predictive, and prescriptive analytics. Machine learning and data mining are only two examples of the kinds of advanced analytics techniques that may be used to dig deeper into the data in search of previously unseen patterns and trends. Its data management skills are put to use by the personalization and customer relationship management (CRM) functions. Starbucks can better serve its customers by studying their purchase, preference, and feedback data in order to provide individualised suggestions, promotions, and rewards. Starbucks relies on effective supply chain management to keep its items in stock at its shops throughout the world. Optimising stock levels, predicting sales, checking on vendors, and optimising transport are all made easier with well-managed data. A NoSQL database is the best option for the management of vast amounts of financial data because it can handle enormous amounts of data in many forms. Hadoop-based financial data cleansing, integration, and reduction enhance huge dataset processing speed and efficiency. Clustering methods and MapReduce for financial big data analysis algorithms can analyse processed data and anticipate user behaviour. (Chang & Wang, 2022)

Communication and Visualisation at Starbucks

In data mining, data visualisation is essential for making sense of massive data sets. There are a variety of proposed visualisation techniques, with t-distributed stochastic neighbour embedding being considered state-of-the-art. (Lensen et al., 2021) There are several methods that we can use to visualise our analysis for the resizable parties and the public to give a clear idea about the predictions. So in case StartBucs can have market advantages with its competitors from the market, As an example, Dashboad is one of the common methods to demonstrate the analysis. Reporting and Dashboards may convey data visually and conveniently. This helps stakeholders track KPIs and performance indicators for data-driven decision-making. data visualisation tools like charts, graphs, and maps. These visualisations help stakeholders comprehend complicated data and spot trends. The effectiveness of decision-making and productivity in an organization are both enhanced when users are pleased with the dashboards provided by business intelligence (BI) systems. Cox also shows a correlation between training decision quality and more dashboard usage; in other words, the higher the quality of the choice, the more is needed.(Sánchez-Ferrer et al., 2019) Starbucks may use data storytelling to tell stories. They may engage stakeholders and explain concepts by mixing visuals, tales, and context. Also Collaborative Platforms may use to share data insights and foster departmental conversations. These tools let stakeholders annotate, explore, and draw insights in real time. Communication and Visualisation at Starbucks Presentations and Visual Communication also use at Starbucks may give facts and recommendations. Charts, infographics, and data visualisations help convey information by using the smart application they can derectly communicate with the customers for instance the monthly coffee expensas and how many points he can add from the next month.

Decision support at Starbucks

Decision support is an integral part of Starbucks' data management practises, playing a critical part in the company's efforts to leverage data insights to guide decision-making processes. The ability to choose the best course of action is what we mean when we talk about decisionmaking. Data-driven decisions made with the help of AI are now helping millions of people. Data Driven Decision Making provides guidance on how to use data-driven decision-making approaches for improved resource management across a variety of technical processes involved in managing IoT system resources.(Kavitha & Chinnasamy, 2021) Strategic and operational choices at Starbucks are likely influenced by data analysis and insights. This means sorting through data in search of trends, patterns, and possibilities that may then be used to guide decision-making at a variety of organisational levels. It is possible that Starbucks will make use of techniques from predictive analytics in order to anticipate future trends, customer behaviour, and market demand. They are able to create educated forecasts that help decision-making, such as inventory management or product introductions, by analysing historical data and utilising statistical models. Predictive analytics systems must contain several ways for analysing and predicting time series to answer corporate leaders' management challenges, especially while making managerial decisions. Automated and software tools use forecasting methodologies categorised by the analytical group based on source data attributes.(Romanenkov et al., 2020) Many enterprises, from small-medium to large, value business intelligence systems (BIS). There is much research on BIS acceptance, implementation, and key success factors (CSFs), but less on BI software tool selection. (Gina & Budree, 2020) Starbucks may use business intelligence for decision-making. These technologies aggregate, analyse, and visualise data, giving stakeholders real-time access to KPIs, reports, and dashboards.

Critical Review of the Commercial and Open-source Software

Software for Data collection

LabView Data collection, instrument control, and industrial automation are just few of the many applications that make use of LabVIEW (Laboratory Virtual Instrument Engineering Workbench). Importantly is a is a licensed software that has to be learned before it can be used to its full capacity. Through the use of compatible hardware and interfaces, LabVIEW may be used to collect information from a wide range of sources, including Starbucks. Most classic Data collection systems are utilised on fixed devices or situations. This sort of data gathering system was typically more costly since each channel could only be linked to the sensor of the original developed type and could not be applied to other comparable tests, making it difficult to handle complicated testing system needs. Virtual instruments make data collecting systems generic and software-oriented. Many LabVIEW-based testing systems exist. The adaptability and autonomy of data collecting systems have increased. (Du et al., 2017) LabVIEW is compatible with a large variety of sensors and equipment for data gathering. also may be used to communicate with sensors that measure environmental conditions such as humidity, pressure, and temperature in a Starbucks. Instrument Control facilitates interaction and data exchange with devices like barcode scanners, scales, and Starbucks' coffee machines. It's now possible to collect information from these gadgets to track production, inventories, and sales. Because of the proliferation of IoT devices, LabVIEW may now be used to collect data from those installed in Starbucks cafes. Internet-of-Things sensors might track things like equipment health, energy use, or consumer traffic. Data may be continuously acquired and stored with LabVIEW's real-time data logging capabilities. LabVIEW might be used at Starbucks to keep track of readings from a wide range of sensors and devices over time for the purposes of quality assurance and process improvement.. (LabVIEW Documentation, 2023)

OpenDAQ

OpenDAQ is the finest open source software alternative for a complete data collection system, including both software and hardware. It offers a cost-effective solution for acquiring data from various sensors and instruments. OpenDAQ can be used in Starbucks to capture data from user interactions, such as feedback surveys, customer loyalty programmes, or mobile apps. With its open-source nature, OpenDAQ allows for customization and integration with specific hardware requirements. It provides a user-friendly interface for configuring and visualising acquired data, enabling Starbucks to gather valuable insights on customer preferences, satisfaction levels, and operational performance for decision-making and improving the overall customer experience.

Software for Data management

MYSQL The most commonly used relational database, stores business data in the teaching management system. MySQL's database is compact and quick. Starbuks requires speed to manage more data in less time. MySQL also provides some great qualities. MySQL prefers multithreaded programming. Its threads are from the 6th International Conference on Communication and Electronic Systems. may also be installed and used on several operating systems, enabling inter-system migration. features encryption and authorization, making it more secure and stable. thread-based memory allocation system that runs quickly and is stable. MySQL handles huge databases well in practise.(X. Wang et al., 2021)

Amazon Web Services (AWS)

Similarly, Amazon Web Services (AWS) may help Starbucks improve its data management. Aside from Amazon Redshift's speedy data warehousing and analysis, Amazon S3 provides safe and scalable object storage. Data transportation and processing processes may be orchestrated with the aid of AWS Data Pipeline, while AWS Glue assists with data preparation and transformation. AWS Glue Data Catalogue is a centralised metadata repository, and Amazon Athena enables interactive querying of Amazon S3 data. When used together, the AWS products provide Starbucks with a turnkey data management solution for streamlined data storage, processing, analysis, and governance. Starbucks may improve its data-driven skills and obtain useful insights by using these technologies.

Software for Communication and Visualisation

Power BI While there are numerous open-source, free software options available, none compares to the strength and versatility of power bi. Power BI's visualisation features let you easily transform your data into several visual representations, including but not limited to bar charts, column charts, pie charts, scatter plots, matrices, and scatter plots. Excel files, SQL Server datasets, comma-separated values (CSV), and many other formats are all supported by Microsoft Power BI. You'll also need to manually assemble a dataset. Power BI's transform data capability allows us to tailor the data to our needs. Power BI allows users to simultaneously update many sheets. Power BI's filter function adds a new dimension to data visualisation. In order to access data from its online repository, Power BI has a web function. (Singh et al., 2023) When it comes to Communication and visualising information, Microsoft Power BI may be a huge help to Starbucks. Power BI allows Starbucks to make dashboards and reports that are both engaging and informative, which improves the company's ability to share and explain Software for Communication and Visualisation complicated data. With the help of real-time reporting, Starbucks can keep tabs on crucial metrics and KPIs in order to make informed decisions and disseminate relevant data in a timely fashion. Power BI's collaborative capabilities encourage teams to work together by letting them create dashboards and reports simultaneously. To make sure that everyone has access to the most up-to-date information, Starbucks can also provide reports and dashboards to interested parties both inside and outside the company. Power BI's mobile apps allow workers to view dashboards and insights on the go, improving their ability to collaborate and make decisions. By allowing users to pose queries to data in natural language, Power BI facilitates both transparent communication and in-depth analysis. Starbucks' internal communication and cooperation is further improved by the integration of other Microsoft technologies such as Excel, SharePoint, and Teams. Overall, Power BI enables data-driven decisions throughout Starbucks by improving communication and data visualisation.

Software for Decision suppo

IBM Cognos Analytics is an all-inclusive business analytics tool that can be used by any company to better understand and utilise their data. Data exploration, dynamic dashboards, and in-depth analytical tools are just a few of the things it provides. Businesses may improve their operations, choices, and outcomes with the help of Cognos Analytics. The platform encourages cooperation and information sharing by facilitating collaboration and the dissemination of ideas. It has an intuitive interface and can be easily connected to other databases. IBM Cognos Analytics equips businesses to make better use of their data through visualisation, reporting, and predictive analytics. (ibm.com, n.d.) Starbucks would benefit immensely from using IBM Cognos Analytics, which is a powerful decision support tool. Data from a wide variety of sources can now be accessed and analysed within Starbucks thanks to Cognos Analytics. Users are able to explore data in new ways using the software's interactive dashboards, reports, and reporting tools. In order to spot patterns, possibilities, and trends, Starbucks can use sophisticated analytics tools like predictive modelling and forecasting. Cognos Analytics' collaboration and sharing features allow the Starbucks crew to work together to analyse data and make choices. IBM Cognos Analytics provides Starbucks with a powerful set of tools for data-driven decision support, which has a significant impact on the company's bottom line.

Conclusion

Any business, Including Startbucks from startups to established corporations, stands to greatly benefit from adopting the technology covered in this assignment. The industry as a whole is shifting its focus to technological advancements in order to increase revenue. Also, it's challenging to go without modern conveniences like computers and data analysis. By applying data acquisition to collect customer information and purchase data, Starbucks may leverage data technologies to enhance sales growth. The ability to retrieve and sort data in preparation for analysis depends on how well it is managed. Decision support tools provide data-driven decision making for demand forecasts, pricing optimisation, and tailored suggestions, while visualisation tools help discover sales patterns and client preferences. Starbucks can improve sales and customer satisfaction by using these data tools to target advertisements, streamline stock management, and enrich the shopping experience. In order to maximise revenue growth, Starbucks uses data-driven decision making, which is enabled by data collection, management, visualisation, and decision support tools.

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