Essay Sample on Organizational Background of Coca Cola

Introduction

The Coca Cola Company is an American organization that manufactures, retails, and markets nonalcoholic drinks. John Pemberton founded the company in 1886 although he sold the firm to Asa Candler in 1894. Today, Coca Cola has grown into a multinational company serving consumers worldwide. Its headquarters are located in Atlanta, Georgia. The company is a total beverage firm, offering approximately 500 brands in more than 200 countries and regions. In addition to the enterprise's Coca Cola brands, its portfolio encompasses some of the world's most valuable beverage brands like Dasani waters, Fanta, Minute Maid Juices, Sprite, and Georgia Coffee.

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The major data elements that Coca Cola obtains encompass items, customers, distributors, and employees. Such elements include personal information such as name, date of birth, and gender, postal address, telephone, email, and social network information. Other data elements include lifestyle details, transactional, communication, customer services information, and product details. Coca Cola Company uses this data in various business processes such as manufacturing, financial, marketing. The primary stewards for this data are IT professionals, system engineers, Human resource professionals, and digital marketers.

Current Data Challenges, Opportunity, and Goals

Coca Cola faces certain data challenges, which derail its projects even before they begin. First, it has trouble in evaluating data quality and reliability (Pearson & Wegener, 2013). Notably, data in Coca Cola comes from a broad array of sources such as transactional systems, mobile devices, social media, legacy applications, and web logs. This makes it difficult and inefficient to forecast the data's reliability and quality. Second, the company lacks sufficient talent and capabilities necessary to leverage the data fully. In particular, the firm lacks an understanding of which data is crucial to the business and why it is essential. Mainly, this arises from the speedy changes in the data ecosystem that is nearly impossible to keep up with. New tools, frameworks, and capabilities are evolving and maturing in a matter of months, which results in a skills gap within the company. Additionally, the company lacks employees with data analysis and data management skills. The staff also lack the knowledge in the technologies and frameworks that are best for any given data initiative (Pearson & Wegener, 2013).

The third challenge is that, as Coca Cola gathers, stores, and analyzes increasing amounts of data from new and existing sources, security becomes of concern. The entity struggles to control data access, protect its infrastructure, and secure data assets (Cai & Zhu, 2015). Undeniably, it faces day-to-day security challenges such as safeguarding against outsider and insider attacks and ensuring the privacy and security of thousands of customers, retailers, and employees' data. Finally, the company faces budgetary limitations as data technologies need large clusters of servers, which demand long provisioning and setup cycles. Such processes result in significant capital expenditures and maintenance overhead. Additionally, the growing variety, velocity, and volume of data result in unsustainable IT costs (Cai & Zhu, 2015).

To overcome these challenges, the company has set various data quality goals. First, the entity seeks to reach high levels of data accuracy within its crucial data stores. Accuracy can translate into high data quality. Further, the company has set to build data checkers, improved screen designs, better data capture processes, and policies to minimize data quality problems introduction into its information systems. Irrefutably, adopting a data management plan will help Coca Cola to develop data collection and analysis tools and frameworks that will ensure it gathers quality data necessary for its business processes. Additionally, the plan will ensure the company monitors its data quality assurance initiatives periodically to combat problems that arise efficiently.

Assignment 2: Enterprise Data Architecture

According to DalleMule and Davenport (2017), an organization's data architecture outlines how data is gathered, stored, transformed, moved, and consumed. It encompasses the regulations governing structured formats like file systems, databases, and the systems for binding data with the business processes that use it. The methodology that will be used the enterprise data architecture will be the Single version of Truth (SVOT) approach that works at the data level. The SVOT is a logical, frequently virtual and cloud-based repository that holds one authoritative copy of all critical data like customer personal information and product details. The SVOT will have robust data governance and provenance controls to ensure that the company can rely on the data (DalleMule & Davenport, 2017). Additionally, the methodology will use a common language that all business units can understand. Nevertheless, although the SVOT approach is straightforward conceptually, it needs robust data controls, governance, standards, and technology.

Using the SVOT within Coca Cola will ensure that data is controlled tightly by permanent It professionals focused on master data management and information security. The approach will ensure the company reduces risk, minimizes data management costs, and provide effective data controls and regulatory oversight (DalleMule & Davenport, 2017). Ultimately, the enterprise will benefit from increased business agility and high profitability, which will help it gain a competitive edge in the beverage industry.

The primary systems of the company are operational, management, and strategic-levels systems. Operational-level systems support operational managers by recording elementary transactions and activities of the entity, for example, receipts, sales, credit decisions, payroll, and the flow of raw materials and products in the factory. The management-level systems support non-routine decision-making, monitoring, and administrative activities of middle managers. These systems avail periodic reports instead of instant data on the operation. Finally, strategic-level systems assist senior management to solve and address strategic problems and long-term trends within the company and in the external environment (Orozco, Tarhini, & Tarhini, 2015).

The Transaction Processing System (TPS), Management Information Systems (MIS) and Decision Support System (DSS) play a vital role in the flow of data within the major systems of the organization. First, TPS help operational managers to solve problems and make decisions. The MIS then takes data from the transaction processing system and summarizes it into a sequence of management reports that are to be utilized by the middle-level managers (Orozco et al., 2015). MIS simply avails managers with feedback on daily transactions. Additionally, it avails data and support for effective decision-making. Finally, the DSS give information, approaches or analysis techniques to the senior managers to assist them to make decisions. The DSS are utilized for support of unstructured decisions and analytical work (Orozco et al., 2015).

The major data stewards at the operational level are the operational managers while those in the management level are the middle line managers. In the strategic level, the data stewards are the senior managers and strategic management staff.

Having an SVOT model and a data management function in the organization can be a start. However, it can never be fully efficient in the absence of a coherent enterprise data strategy for classifying, governing, analyzing, and moving the company's data assets (DalleMule & Davenport, 2017). Irrefutably, without such a strategic plan, the firm can struggle to protect and leverage its data. Once the model and function are aligned with the enterprise data strategy, then the company gains vital capabilities that support managerial decision-making and eventually enhance the entity's financial performance.

Assignment 3: Data Governance Policy

The purpose of this data governance policy is to establish the practices and principles for the effective management and use of Coca Cola's corporate data. The policy will also ensure that the data is secure and reliable while accessible within a clear system of controls. Additionally, the policy will ensure that the company decision making, reporting, and planning are informed by secure, accurate, and reliable data. Further, the policy will articulate the responsibilities for the stewardship of the firm's data and information systems supporting its implementation.

Mainly, various best practices for the governance policy will help bring success. First, considering that data governance is an iterative process, the company will need to take a holistic approach but start small (Cave, 2017). In particular, it will be best, to begin with, the people and culture and then advance to the data governance, stewardship, and technology processes. The company will need to balance out its strategic data goals and tactical compromises to ensure the overall data governance program is moving towards the desired direction successfully. The second best practice is to obtain top management support since data governance requires funding for technology tools and projects. Notably, without support from the executive level, then the project is set to fail. Thus, the company should analyze its stakeholders and get on board key decision makers who represent core line of businesses and functional areas. Moreover, with them as champions, the data governance policy gains a high possibility of being successful (Cave, 2017).

The third practice will involve establishing, collecting, and reporting on metrics to measure the progress. Examples of metrics may include changes in data management costs, a number of decisions made, data value, and the maturity of the data management process (Cave, 2017). Mainly, measuring immediate returns will give positive feedback, sustain engagement, and lead to additional support. The metrics need to communicate business value, which should be quantifiable. Finally, it will be important to define data stewardship by identifying and building a data steward team that encompasses experts from all business areas. Undeniably, this will go hand-in-hand with linking incentives to award and re-enforce participation. Furthermore, data governance is collaborative in nature, which depends heavily on a prioritized and leveled commitment from all business processes.

The policy will also dictate on the accessibility of data. In particular, the corporate data will be accessible for authorized use in accordance with a transparent and clear control framework. The accessibility control frameworks will be developed in accordance with the company's Privacy Policy to secure sensitive data (Mohanapriya, Bharathi, Aravinth, Gowrishankar, Ramkumar, M., Mohanapriya, & Ramkumar, 2014). Additionally, an internal control system for data governance and management will be developed to ensure employees comply with the contractual, legal and regulatory requirements related to data access. In particular, the firm will have the ability to know who can access what data and what users are doing with that access. Further, consistent access revalidations will be conducted to verify that the users still need their type of access. In case of any inconsistencies, modifications will be done promptly and accordingly.

Similarly, it will be crucial to manage the quality of Coca Cola's data. Moreover, high quality data is important to accurate reporting and evidence-based decision-making (Mohanapriya et al., 2014). The company will define data quality...