DTS - Week 3 (Articles and Lecture)

The primary goal of a data strategy is to find a balance between protecting sensitive information and using data to create value for the organization.

A Single Source of Truth (SSOT) is a centralized repository for data. It ensures data accuracy and consistency by providing a single, authoritative source of information for everyone in the organization.

Multiple Versions of the Truth (MVOTs) are tailored outputs derived from the SSOT. They cater to specific needs and perspectives, providing a more focused view of the data.

Defensive data management is about minimizing risks associated with data. It focuses on protecting sensitive information and complying with regulations.

Offensive data management focuses on using data to create value. It involves leveraging data for insights, innovation, and competitive advantage.

The "3 V's" of big data are: Volume, Variety, and Velocity. They represent the vast volume, diverse types, and speed at which data is generated.

In Mintzberg's decision-making model, the 'Design' phase involves brainstorming and developing alternative solutions to address strategic problems.

Blockchain enhances interorganizational governance by removing intermediaries and establishing trust through decentralized mechanisms.

Blockchain governance benefits include: automated verification, peer-to-peer network control, and 'trustless trust.'

"Trustless trust" means relying on the inherent security and transparency of the blockchain system rather than on individual actors or institutions.

The 'Custodian Mode' of governance involves centralized control but operates across organizational boundaries. It is often seen in industry consortia.

Contracts can be insufficient for governing large networks due to their scalability limitations and high enforcement costs. They may also be difficult to adapt to dynamic changes in the network.

A key characteristic of defensive data management is its focus on compliance and fraud detection. It ensures that data is used in accordance with regulations and protects against malicious activities.

MVOTs provide flexibility for department-specific data analysis. They enable tailored views of data that are relevant and understandable within specific contexts.

Blockchain governance automates contingency planning and enforcement, significantly reducing costs associated with manual processes. It allows for quick and efficient responses to changes in the network.

Excluding competitors in blockchain governance can lead to weak network ties and potential antitrust issues. A more open and inclusive approach is generally preferred.

Blockchain safeguards participants in interorganizational transactions through automated verification and enforcement mechanisms. It ensures accountability and reduces the risk of fraud.

Scalability in blockchain governance is enabled by seamless coordination through transparency. All participants can easily see and verify transactions, facilitating efficient collaboration.

The primary challenge with "open data sources" in big data management is their potential lack of security and vulnerability to manipulation. Data quality and provenance can be difficult to verify.

Adapting governance mechanisms dynamically is crucial for developing a balanced data strategy. This allows for flexibility in meeting changing business needs and regulatory requirements.

Contingency planning in blockchain governance involves outlining "if-then" scenarios for rapid, automated adjustments in response to unexpected events or changes.