Data strategy interpretation

Entering the era of the digital economy, data and digital transformation have become defining keywords of the times. Enterprises, organizations, and institutions are constantly exploring pathways and measures for digital transformation to keep pace with the development of the era. The issuance of national policies regarding data inclusion in financial statements signifies that data has made the leap from a natural resource to an economic asset. As the primary production factor in the digital economy era, data is poised to become a significant support for government and corporate reporting, as well as fiscal revenues.

International Institute For Advanced Data Management Study, starting from the perspective of formulating a data strategy, explores the standards for setting up such strategies and constructs a cyclical model for acquiring, utilizing, and creating data value. This effort provides both theoretical foundations and practical references for fields such as data management, data governance, and digital transformation.

This article will take the formulation of a corporate data strategy as an example and cover the following topics:

• Definition of Data Strategy

• Methods for Formulating Data Strategy

• Implementation Pathways for Data Strategy

• The Value Proposition of Data Strategy

A data strategy is a set of guiding principles and actionable plans that place data at the core, designed to facilitate the digital transformation and optimization of an organization, ultimately achieving decision-making and development that are powered by data. It serves as the guiding framework for enterprises and other institutions undergoing digital transformation.

A data strategy should adhere to the principle of placing the ecosystem user at the center, being driven by trusted data, and aiming for sustainable development.

The data strategy encompasses five main components: data acquisition strategy, reciprocity principles, scenario integration, service expansion, and boundary extension. These five key parts are integrated into the process of formulating a data strategy. The data acquisition strategy, coupled with reciprocity principles, ensures the ethical and mutually beneficial procurement of data. The deep integration into business scenarios allows for continuous collection of user data in everyday contexts, thus broadening the scope of data. Service expansion extends the reach of business operations and diversifies data types, bolstering brand influence. The extreme extension looks ahead to the future of the enterprise, reverse-engineering the identification of critical value data to guide businesses in determining the right directions for obtaining pivotal data assets.

The formulation of a data strategy necessitates a clear strategic positioning, leveraging the Business Model Canvas to analyze and investigate the company's business model, thereby creating a strategic blueprint that forms the foundation for subsequent data strategy development.

The analysis of a company's business model encompasses eight primary sections.

1. Analysis of the Current Enterprise Situation

• Clarify the existing corporate ideology system, articulating the enterprise's mission, vision, and shared long-term value pursuit.

• Analyze the current business operations, market attractiveness, and competitive edge.

• Conduct a thorough diagnosis of internal and external issues within the enterprise.

• Identify gaps compared to benchmark companies and learn from their experiences.

• Achieve overall strategic consensus among stakeholders in the organization.

2. Development of Data Acquisition Strategy and Winning Strategies

• Formulate Short-Term Data-Driven Strategies

• Design Mid-Term Ecosystem Scenario Building Strategies

• Establish Long-Term Boundary Extension Strategies

• Apply a bidirectional iterative analysis using the BDN (Business Data Network) model

1. Inventorying Organizational Capabilities

• Identifying the optimal operational model for the organization

• Determining current participants in data management

• Establishing and selecting the structure for a data management team

• Enhancing Organizational Systems

2. Perfection of organizational system

• Developing structured and process-oriented data standards and regulations

• Creating a performance evaluation system that integrates data business metrics

3. Cultivating a Data Culture

• Promoting data awareness

• Enhancing data literacy

• Encouraging data-driven decision making

• Advocating for data sharing and transparency

• Ensuring data governance and security

4. Implementing Data Governance

• Assessing maturity in data governance practices

• Evaluating data governance capabilities

The evolution of digital transformation is divided into six distinct phases, where a data strategy can

• Drive enterprises to complete the informatization process encompassing business datafication and the conversion of data into business processes, thereby accelerating digital transformation.

• Continuously extract value from data by transforming raw data into valuable data resources, solidifying it into data assets, enhancing data value density, and boosting corporate competitiveness.

• Enrich organizational data culture, strengthen internal cohesion, refine management structures, and improve operational interfacing efficiency.

• Propel the construction of a data ecosystem, seamlessly integrating upstream and downstream supply chains, reducing costs, and attracting customers through in-depth business scenario designs to expand revenue streams, ultimately achieving cost reductions and improved efficiency.

A data strategy acts as the top-level architecture guiding digital transformation, committed to unlocking the potential of data value and aligning with the high-return trends in big data. By leveraging the release of data value, it drives asset upgrades and expansion within enterprises and other institutions, fostering the growth of the digital economy.

Digitalization is a process that begins with germination, grows continuously, and matures over time. Digital transformation is not merely a leap from 0 to 1, but rather a progression from 1 to N. Therefore, assessing digital transformation involves identifying which phase the transformation is in, pinpointing areas of deficiency, and determining where improvements should be made. The purpose is not to judge the success or failure of digital transformation, but rather to assist organizations in their ongoing growth and development.

In approaching data, researchers at the International Data High Academy consistently maintain a rigorous and meticulous attitude, enhancing their data sensitivity, exploring the relationship between humans and data, and continuously integrating theoretical knowledge about data into practice. Their efforts yield tangible results and contribute to the creation of a world-class platform for knowledge exchange on data management theories and best practices.