DTS - Week 3 (Matching)

Big Data and Business Analytics

• Companies must wisely manage data to remain competitive
• Data theft is common, and flawed or duplicate data sets exist within organizations
• IT departments are often behind the curve
• A coherent strategy balances defensive (security, governance) and offensive (predictive analytics) data management
• Data strategy is rarely static; it must dynamically adjust based on competitive pressures and overall corporate strategy
• The framework balances "defensive" and "offensive" data uses
• Defensive data management minimizes risks (compliance, privacy, fraud)
• Offensive data management supports business objectives (revenue, profitability, customer satisfaction)
• Data strategy can be influenced by factors like industry regulations (e.g., financial services, healthcare) and competition
• Data strategy needs to balance the standardization and flexibility of data

Single Source of Truth (SSOT) vs. Multiple Versions of the Truth (MVOTs)

• SSOT: A logical, often virtual, cloud-based repository with one authoritative copy of crucial data (customer, supplier, product)
• Requires robust data provenance and governance controls; uses a common language for key data elements
• MVOTs: Created from the transformation of SSOT data; specific to business needs
• Allows different groups within an organization to transform, label, and report data, creating specific controlled versions of the truth
• Crucial for data architecture and flexibility

Data Architecture: SSOT & MVOTs

• Implementation of a data architecture that supports both is critical, focusing on data level and useful information transformation
• A company's position on the offense-defense spectrum rarely stays static
• Good data strategy requires high-quality, granular, standardized data, and controlled creation of multiple versions of the truth

Data Governance, Data Management

• Good governance is needed for high-quality, standardized data
• Ensuring proper data definitions and consistent rules helps organizations avoid inconsistencies in transformation of data
• Feedback loops are important for improvements in data transformations
• Data management can be centralized or decentralized, dependent on whether a company's main focus is defense or offense

Single CDO

• A single CDO (Chief Data Officer) across the organization is suitable for data defense
• Consistent data policies, governance, and standards are important for centralized control
• Maintaining uniformity in data practices is crucial for effective strategy execution and data protection

Decentralized Management

• Decentralized management is better for offensive strategies
• A CDO in each business unit permits agility and customization of data reporting/analytics
• Preventing data silos is crucial to reducing redundant systems and duplicated work
• Decentralized budgets are more focused on minimizing risk and reducing costs, compared to centralized budgets

Data-Driven Decisions

• Data-driven decisions are generally better decisions, relying on evidence rather than intuition
• Data analysis enables managers to make decisions based on evidence rather than intuition
• Businesses focusing on data-driven decisions perform better financially and operationally

This quiz explores the critical aspects of managing big data and business analytics in organizations. It covers the balance between defensive and offensive data management strategies and the importance of adapting to competitive pressures. Assess your understanding of these key concepts to enhance your knowledge in this vital field.