DTS - Week 4 (MC)

A term that refers to the three key characteristics of big data: variety, volume, and velocity.

The challenges in managing big data include concerns about data privacy, potential biases in data sources, and the high cost of infrastructure.

Prescriptive analytics helps in suggesting actions or solutions based on insights from past and present data. It answers the question 'What should we do?' by providing actionable recommendations.

The primary goal of big data management is to ensure data quality and accessibility. This ensures that the data is accurate, reliable, and readily available for analysis and use.

The Knowledge-Based View emphasizes the importance of knowledge and intangible assets as key drivers for competitive advantage. It recognizes that knowledge is a valuable resource that can be leveraged to create unique capabilities.

The Recognition Phase in Mintzberg's decision-making model focuses on identifying and defining the problem. It's the initial step where the problem is recognized or discovered.

Analytics competitors are characterized by their use of enterprise-wide analytics applications. They leverage analytics across their entire organization to gain a competitive advantage.

The main privacy challenge in big data management is that distorting data for anonymization can significantly reduce its accuracy. This makes it difficult to balance privacy concerns with the need for accurate insights.

Blockchain immutability means that once data is recorded on the blockchain, it cannot be deleted or altered. Every transaction is permanently recorded and auditable.

In the Chief governance mode, control over the blockchain is centralized within a single organization. This gives one entity sole authority and responsibility for managing the network.

The Consortium governance mode involves shared control and decision-making across multiple organizations. This fosters collaboration and ensures that all participants have a voice.

A key drawback of the Custodian governance mode is that it can exclude key participants from the decision-making process, potentially limiting their involvement and hindering collaboration.

The Connecting strategy in blockchain governance allows for quick integration of new organizations. This is a key advantage for networks striving for growth and expansion.

In blockchain governance, the Isolating strategy focuses on reducing dependence on external actors. It emphasizes internal collaboration and seeks to minimize reliance on external services to maintain control.

Analog governance relies on centralized control, where one entity holds the power to make decisions and enforce rules. It follows traditional hierarchical structures.

Automated governance primarily uses algorithmic trust. This means decisions are made by algorithms based on pre-programmed rules and logic, not human judgment.

Augmented governance combines human involvement with algorithmic decision-making. It seeks to balance human judgment and expertise with the efficiency and precision of algorithms.

Coordination in governance focuses on organizing and aligning tasks among participants. It ensures that individual actions are properly aligned and coordinated to achieve a common goal.

Governance by algorithms focuses on understanding the impact of automated systems on human emotions and perceptions. It addresses the ethical and social implications of relying on algorithms for decision-making.

Control in governance is used to verify inputs and outputs. It involves mechanisms for monitoring, auditing, and ensuring compliance with established rules and regulations.

Poor governance in digital relationships can lead to platform disintermediation. This occurs when users bypass the platform and interact directly with each other, diminishing the platform's role.

Apple's privacy policies demonstrate governance as a strategic advantage. Their commitment to user privacy has built trust and brand loyalty, positioning them as a leader in data protection.

Loosening in blockchain governance allows for open mutual decisions among participants. It encourages greater collaboration and shared control, fostering a more decentralized and inclusive network.

Automated governance is distinguished from augmented governance by the absence of human oversight. Fully automated systems rely solely on pre-programmed algorithms for all decisions, without any human intervention.

Cybernetic incentives are commonly used in automated governance. These are feedback-based systems that reward or penalize actions based on predefined criteria, encouraging desired behavior.

Augmented governance aims to balance human and algorithmic processes. It seeks to leverage the strengths of both human judgment and algorithmic efficiency for optimal decision-making.

The challenge of mitigating cyberthreats is specific to digital governance. Digital networks face unique vulnerabilities to cyberattacks, requiring specific governance mechanisms to protect against online threats.

The level of transactivity, which refers to the frequency and complexity of interactions within a network, is a key determinant for selecting governance models in digital networks. The complexity of the network and its needs shape the appropriate governance approach.

Consortium governance modes ensure data security through consensus-based decision-making. This means that any change or modification to the blockchain requires agreement from all participating organizations, ensuring integrity and security.

Automated coordination involves omnilateral task engagement. This means that multiple participants are involved in the coordination process simultaneously, enabling a decentralized and dynamic task flow.