The Data Balance Sheet

In today's digital age, data has become the golden ticket to success for organizations. As Chief Data Officers (CDOs), you have the power to increase your organization's market cap by effectively managing and utilizing your data assets. However, building a solid data balance sheet can be a daunting task. This blog post aims to provide you with the necessary tools to achieve this goal successfully. We will discuss the five themes that make up a data balance sheet and provide examples to clarify the concept.

1. Percentage of Total Data Assets (%)

The percentage of total data assets represents the proportion of a specific data category in relation to the entire data asset inventory. This percentage is calculated by dividing the value of a particular data category by the total value of all data assets and expressing it as a percentage. For instance, United Airlines pledged their customer loyalty program with an implied valuation of $21 Billion as collateral to raise $5 Billion cash during the COVID-19 shutdown in 2020. The percentage of total data assets for the loyalty program is 2.3%. As a CDO, it's essential to focus on high-value data categories that are in high demand and can generate substantial revenue for your organization.

2. Percentage of Total Data Liabilities (%)

The percentage of total data liabilities represents the portion of a specific category of data that is acquired via customer consent, employees, and third-party data providers. It's calculated by taking the value of a particular data liability category and dividing it by the total value of all liabilities, expressed as a percentage. It's crucial to ensure that your organization is acquiring data responsibly, legally, and with appropriate consent.

3. Percentage of Data Source Equity (%)

The percentage of data source equity represents the portion of a specific data category that is under the sole ownership and control of your organization. This ownership can add value to your organization's market cap. It's critical to identify and invest in high-value data categories that can help your organization differentiate itself from the competition. A prime example of effective data source equity is Apple's App Store. The App Store has earned Apple billions of dollars as developers pay a commission fee to Apple for every app sold on the store.

4. The Importance of Data Quality, Trust, and Value

Apart from the three themes mentioned above, CDOs must also prioritize data quality, trust, and value. Data must be accurate, reliable, consistent, and of high quality to generate value for your organization. Building trust with customers, employees, and partners is crucial for long-term success. Finally, CDOs must ensure that their data products provide value to their customers and partners. Data products must be relevant, timely, and address customers' needs.

5. Role of AI and Data Catalog

AI and data cataloging play a pivotal role in managing and harnessing data assets efficiently. AI can automate labor-intensive tasks, such as classifying data into appropriate categories and learning data curation patterns from user interaction, making data management more streamlined. Additionally, data cataloging technologies provide centralized inventories of data assets, enabling easy enrichment and mass sharing of information with the right stakeholders. This, in turn, supports critical business goals like creating executive reports for data-driven decision-making.

Conclusion:

In conclusion, building a solid data balance sheet can lead to increased market cap and competitive advantage for your organization. The three themes that make up a data balance sheet, as well as data quality, trust, and value, should be prioritized. It's crucial to focus on high-value data categories that can generate substantial revenue and invest in data source equity. As a CDO, managing and utilizing your data assets effectively will be key to your organization's success.