In today’s “data is the new oil” era, no one can say an organization is able to achieve digital transformation without data. Executives and even the board level are talking about data-driven business strategy when they develop business objectives and goals. Harnessing data effectively can bring new value to business in the form of improved strategic planning and decision-making. It’s time to rethink how data governance and management can enable data-driven decision-making culture and enhance the relationship between the business and data in the context of digital transformation.
Meanwhile, many organizations are still facing some challenges when implementing data governance and management programs, including:
- Unclear data ownership
- Missing consideration on proportionality of cost and risk
- Ever-changing legislation on data protection
- Frequently changed business process and workflow
- Siloed department and organizational structures
- Poor data quality
- Lack of skilled talent
In ISACA’s recently issued white paper, Rethinking Data Governance and Management, a five-step method is proposed to help you implement your data governance program with a practical data framework.
Step 1: Establish a Data Governance Foundation
A good data governance foundation sets the groundwork to collect and use data. This foundation includes addressing legal, business intellectual property and customer sensitivity considerations. This can help you answer four questions:
- What data do you have and need to use?
- When are data governance practices taking place through your data life cycle?
- Who is responsible for the governance structure?
- How is data managed?
Step 2: Establish and Evolve the Data Architecture
According to The Open Group Architecture Framework (TOGAF), data architecture describes the structure and interaction of the major types and sources of data, logical data assets, physical data assets and data management resources of the enterprise. The establishment of data architecture can address data standardization issues.
Step 3: Define, Execute, Assure Data Quality and Clean Polluted Data
A good metadata strategy leads to good data quality. Metadata summarizes basic data, which can ease the process of finding and working with particular instances of data.
Step 4: Realize Data Democratization
Data democratization facilitates for organizations the sharing of data and insights across the enterprise, providing a single source of reference to search curated data and data-related expertise.
Step 5: Focus on Data Analytics
The ultimate objective of data governance program is to realize data value and gain insights for business. Data analytics is used to examine data and apply statistical methods to identify hidden patterns and unknown correlations, draw conclusions and predict the likelihood of future events and trends in the digital economy.
Data governance and data management are important for businesses that want to make use of data to create value for their stakeholders while also minimizing risk. Find more in ISACA’s white paper, Rethinking Data Governance and Management.
