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1. Why do we need a Data Strategy?

Digital transformation projects often fail to meet expectations, with over 70% not achieving their goals. The World Economic Forum estimated that in 2020, the volume of digital data reached 44 zettabytes and is expected to grow rapidly to 175 zettabytes by 2025 (1 zettabyte = 1 trillion gigabytes). According to a McKinsey survey in 2019, nearly 70% of digital transformation projects fail. Gartner, a leading technology consulting firm, revealed that over 80% of companies do not achieve their expected digital transformation success.

Data is often compared to crude oil. Just as crude oil needs to be refined before use, data must be properly managed, stored, and secured (Data Protection and Security). Data governance ensures data policies, procedures, and standards are in place to maximize data's value and safety. Effective data engineering and platform technologies support the safe and beneficial use of data (Data Engineering and Platform). Proper data management and analytics help filter, categorize, and synthesize data to make it suitable for different tasks, ensuring both quality and quantity (Data Management and Data Analytics).

A Data Strategy helps organizations succeed in their digital transformation by managing and leveraging vast amounts of data. This concept has emerged in the last decade due to the massive increase in data generation and storage. Organizations need strategies to manage and utilize data effectively, integrating technology with data.

2. What is a Data Strategy?

A Data Strategy creates value from data assets. It is a new field with diverse concepts, adapted from Harvard University and the University of California, Berkeley. Data Strategy can be divided into two main areas: defensive and offensive.

• Defensive Data Strategy: Focuses on data governance and data quality, including privacy and compliance with laws and regulations. It ensures data security and manages data access, quality, and standards.
• Offensive Data Strategy: Uses data to enhance competitiveness, service, and profitability. It includes both internal and external data, structured and unstructured data, and employs various data analysis techniques like descriptive, predictive, prescriptive analyses, and machine learning/artificial intelligence.

3. What are the benefits of a Data Strategy?

A Data Strategy helps drive organizations with data and supports evidence-based decisions, moving away from gut feelings or external expert opinions that may not fully understand the organization’s data. High-quality data can be leveraged to create more value.

• For government agencies: High-quality data can be used for analysis, research, and transparent operations. Efficient digital authentication systems and timely, targeted, temporary policies improve budget efficiency.
• For private organizations: Improves decision-making, customer and market understanding, product development, service enhancement, business process optimization, and revenue generation from data. Proper data governance and security systems allow safe data sharing and utilization.

4. How to create a Data Strategy?

1) Understand the context: Grasp the organization's mission, vision, laws, and regulations to align the data strategy with organizational goals. Evaluate the current data infrastructure using SWOT analysis to identify strengths, weaknesses, opportunities, and threats. 

2) Ideation: Brainstorm with executives and staff to generate data use cases. Prioritize these use cases based on criteria for quick wins and high impact.

3) Plan and execute: Develop detailed plans for each use case, including budget, management, and timelines. Compile success factors and lessons learned for future use cases. Implement the data strategy through dashboards, data analytic models, machine learning, and AI.

5 How to select use cases?

Understand the organization’s strategy, urgent plans, and pain points from both internal and external perspectives. Use cases should address strategic goals and provide significant, timely benefits. Choose use cases that can be implemented within planned timeframes and budgets to ensure the data strategy is effective and beneficial.