How do you assess the value of your data?

Creating a Data Value Assessment can be a powerful tool to measure the worth of data assets and guide for a data strategy. Here’s a step-by-step framework to conduct a comprehensive Data Value Assessment, tailored for leaders in data management and strategy.

1. Define Assessment Objectives

Why it matters: Clarify the purpose behind assessing the data value. Common objectives include: Identifying data assets with the highest potential value. Prioritizing data governance and quality improvement. Assessing data's contribution to strategic goals (like revenue growth or operational efficiency). Benchmarking current data utilization for future improvements.

2. Identify Data Value Dimensions

Break down data value into key measurable dimensions. For each, consider questions like:

• Business Relevance: How aligned is the data with current business goals and key performance indicators (KPIs)?
• Revenue Potential: How directly does the data contribute to revenue-generating activities?
• Efficiency Impact: Does the data improve operational efficiency, reduce costs, or streamline processes?
• Risk Reduction: Does the data help mitigate risks, such as compliance, regulatory, or cybersecurity risks?
• Customer Experience: How does the data enhance customer interactions and experiences?

3. Develop a Scoring Model for Each Dimension

• Scoring: Assign scores from 1-5 (or another scale) for each dimension to rank data sets. Consider criteria such as:
• Quality & Accuracy: Is the data clean, accurate, and reliable?
• Accessibility: Is the data easy to access and integrate into workflows?
• Timeliness: Is the data up-to-date and delivered at a pace that meets business needs?
• Uniqueness: Does this data provide a unique insight, or is it redundant with other data sources?

Example: Use a weighted scoring model to give more importance to dimensions that align with strategic goals.

4. Conduct Data Inventory and Categorization

• Inventory: List and categorize data assets (e.g., customer data, financial data, operational data).
• Categorization: Use tags or categories based on data type, source, owner, sensitivity level, and value relevance.

5. Calculate the Data Value Index

• Aggregate scores from each dimension to calculate a Data Value Index for each data set.
• Rank the data assets based on their total scores, creating a prioritized list of data assets by value.

6. Financial Value Calculation (Optional)

For a financial view, assign a monetary value to data by estimating how it contributes to revenue, cost savings, or other metrics.

Methodologies for calculating financial value may include:

• Revenue Attribution: Identify revenue directly or indirectly associated with certain data sets.
• Cost Avoidance: Calculate potential savings from data use in areas like risk reduction or operational efficiency.

7. Evaluate and Visualize Results

• Use data visualization to illustrate the value of data assets, highlighting: High-value data assets that require more attention or investment. Low-value data assets that may not warrant extensive management efforts. Areas where data quality or governance needs improvement.

8. Formulate Recommendations

• Data Quality Improvement: Focus on high-value assets first to ensure they are accurate, complete, and accessible.
• Invest in High-Value Areas: Allocate resources for assets that show high business relevance, revenue potential, and risk reduction.
• Sunset Low-Value Data: Consider retiring or archiving data that does not score well across the dimensions.

9. Develop an Ongoing Data Valuation Plan

• Establish a regular cadence for reassessing data value, as business objectives and data relevance may change over time.
• Include periodic updates to the scoring model to reflect evolving business goals or changes in the data landscape.

Sample Outcome for Data Value Assessment

• Top-Ranked Data Assets: Revenue-generating customer transaction data, compliance data (mitigates regulatory risk), supply chain data (improves efficiency).
• Medium-Ranked Assets: Historical employee performance data, archived financial reports.
• Low-Ranked Assets: Legacy customer contact data, unstructured files with limited usage.

This approach creates a clear, objective view of data’s business impact and sets a course for optimizing data strategy. By focusing on value-driven data management, organizations can unlock new growth potential, reduce risk, and gain a competitive edge.