In today’s data-driven world, businesses are sitting on a goldmine of internal data that often goes underutilized. Sunil Soares, founder and CEO of YDC Data Economics, emphasizes the importance of leveraging this first-party data to derive business insights, make informed decisions, and ultimately improve business performance. Presented at the Data Governance & Information Quality Conference (DGIQ), Soares’ insights shed light on the untapped potential of internal data monetization.
The Untapped Potential of Internal Data
Understanding First-Party Data
First-party data refers to information collected directly by a company from its customers and operational activities. Unlike third-party data, which is often sold to external entities, first-party data remains within the organization. This data is a valuable asset that can provide deep insights into customer behavior, preferences, and operational efficiencies. By leveraging this information, businesses have the ability to develop highly personalized and relevant customer experiences, streamline operations, and identify new revenue opportunities.
One of the biggest advantages of first-party data is its accuracy and reliability since it is derived from direct interactions and transactions within the business. This means that decision-makers can trust the data to inform strategic initiatives. However, despite the promising benefits, many organizations fail to harness the full potential of their first-party data. Often, data remains siloed within departments or underutilized due to a lack of understanding of its value. Overcoming these barriers is essential for companies aiming to stay competitive in an increasingly data-centric marketplace.
Quantifying Data Value
Soares argues that many data professionals, including data governance specialists and chief data officers (CDOs), do not fully grasp the value of their internal data. He highlights the magnitude of this underutilized resource by citing the S&P 500 (excluding FANG companies), which has an estimated $32 trillion in market value, with roughly 2% of this being unrecognized data value. This emphasizes the significant potential that lies in effectively quantifying and utilizing internal data. It’s a stark reminder that while companies may focus heavily on tangible assets, the intangible assets represented by data harbor enormous value.
Part of the challenge lies in the difficulty of measuring data’s worth using traditional metrics. Data’s value often becomes evident only when it is analyzed and applied to solve specific business problems or enhance operations. The knowledge extracted from data can open doors to innovative strategies that drive growth, optimize resource allocation, and enhance customer satisfaction. However, without an appropriate valuation framework, businesses may overlook these opportunities, relegating valuable data to a forgotten corner of their digital assets. Developing robust methodologies for data valuation is essential to bridging this gap and unlocking the hidden value within first-party data.
Demonstrating ROI of Data
The Airline Industry Example
One of the key challenges CDOs face is justifying the high costs associated with data management. Soares illustrates this with an example from the airline industry during the COVID-19 pandemic. Major U.S. airlines leveraged their loyalty mileage programs as valuable data assets. For instance, United Airlines found that its customer data represented 20-40% of the total value of its loyalty program, amounting to approximately $4.3 to $8.6 billion. Remarkably, the value of this customer data was more than the value of United Airlines itself at the peak of the pandemic.
This illustration underscores the critical need for businesses to recognize and harness the value enmeshed within their data repositories. Airline companies faced unprecedented pressures during the pandemic, yet those capable of quantifying and leveraging customer data managed to unlock significant financial value amidst the turmoil. The story of United Airlines stands as a potent testament to the untapped monetizable potential lying within the data that businesses routinely accumulate in their daily operations. It also highlights the role data value plays in stabilizing and boosting market valuations, even in challenging times.
Overcoming Cost Center Perception
Despite such compelling success stories, CDOs still struggle with shifting the perception of data management from being a cost center to a value center. Soares underscores the need for CDOs to demonstrate the ROI of data effectively. He quotes global IT spending projected to reach $4.9 trillion in 2023, contrasted with the average budget of a CDO, which is about $30 million for major corporations. This disparity highlights the need for better communication and quantification of data’s value. Accurate articulation of data’s monetary worth can reshape organizational views, transforming skepticism into strategic visionary thinking.
By employing clear, quantifiable data-driven ROI metrics, CDOs can validate the investment in data management tools and technologies. Establishing examples and case studies of tangible benefits derived from data analytics can support this shift. For example, predictive analytics in retail can lead to inventory optimizations, reducing excess stock costs and improving cash flow. Demonstrating these tangible benefits to stakeholders is crucial in altering the cost-center narrative and embracing data management as a significant driver of business profitability.
Challenges in Data Valuation
Intangible Asset Classification
One significant barrier to recognizing data as an asset is its classification as an intangible asset. Unlike tangible assets such as machinery or furniture, intangible assets like data, goodwill, brand reputation, and innovation dominate the market cap of the S&P 500 index, constituting 80% of it. However, due to data’s susceptibility to manipulation and the complexities of valuation, accounting standards often do not reflect this dominance. Traditional valuation methodologies tend to be ill-suited for intangible assets, which, by nature, are more abstract and harder to quantify.
The classification and valuation of data as an intangible asset require a nuanced understanding of its potential impact on business outcomes. Data’s value also fluctuates, depending on the context of its use, the industry, and the specific data types. For instance, customer data in the retail sector may have different valuation criteria than operational data in the manufacturing industry. These complexities necessitate the development of sophisticated valuation models tailored to capturing the unique facets of data within different business frameworks. This challenge underscores the critical role of innovative accounting and valuation standards that mirror the dynamic and multifaceted nature of data.
Novel Metrics for Data Valuation
To remedy this, Soares proposes that companies begin to benchmark and assign value to data through novel metrics such as the data monetization index and the intangible asset index. These metrics help in quantifying the value of various data-related intangibles like customer data, employee data, reference data, reports, and critical data elements. By adopting these new metrics, companies can better appreciate the latent value in their data, inform strategic decisions, and drive growth. These indices offer a structured approach to measuring data’s worth, providing a clearer picture of its contribution to business objectives.
The data monetization index, for example, can quantify how effectively data is being converted into revenue, highlighting areas where additional investment in data analytics could yield significant returns. The intangible asset index, on the other hand, can evaluate the overall impact of various categories of data on a company’s market value and operational efficiency. These metrics are instrumental in bridging the gap between traditional asset valuation and the emerging significance of data in the digital age. Companies adopting these indices stand to benefit from an enhanced understanding of their data assets, leading to more informed and impactful business strategies.
Case Studies in Data Valuation
iRobot and Amazon
Soares examines a case study involving iRobot, a company known for its automated home cleaning devices. These devices gather valuable user data, creating floorplans of consumers’ homes. When Amazon attempted to acquire iRobot, the deal was halted by the Federal Trade Commission (FTC) due to concerns about Amazon’s growing data monopoly, valuing iRobot’s consumer data at over $1.5 billion. This case highlights the importance of data valuation methodologies in assessing the worth of acquisitions and investments. The significant valuation attached to iRobot’s consumer data underscores the strategic importance of user-generated data in the tech industry.
The FTC’s intervention also brings to light the regulatory implications of data-centric acquisitions. As companies amass vast reserves of user data, regulatory bodies are increasingly scrutinizing such deals to ensure fair competition and prevent data monopolies. The iRobot case serves as a reminder that data’s value is not only a corporate asset but also a point of regulatory consideration. It highlights the need for businesses to navigate the intricate landscape where data valuation intersects with antitrust regulations, emphasizing the critical role of robust data governance in mitigating risks associated with large-scale data acquisitions.
Fitbit and Google
Soares compares this valuation methodology to that of Fitbit, acquired by Google. Google’s analysis concluded that the cost per Fitbit user was $60, providing a solid ROI and justifying half a billion-dollar investment in ADT, a home security company. This comparison underscores the importance of data valuation methodologies in assessing the worth of acquisitions and investments. By meticulously evaluating the per-user value, companies like Google can make informed decisions about strategic investments, aligning them with their broader objectives.
The Fitbit acquisition exemplifies how detailed data valuation can reveal opportunities for synergies between different business units or products. In this case, the user data from Fitbit provided Google with valuable insights that could enhance its health initiatives and integrate with its existing product ecosystem. Such evaluations are crucial for companies aiming to derive maximum value from their acquisitions, highlighting the strategic imperatives of adopting a rigorous data valuation framework. This approach not only justifies substantial investments but also paves the way for future growth driven by data synergies and innovations.
Shifting Corporate Culture and Practices
Recognizing Data as a Valuable Asset
To unlock the full potential of internal data, business leaders must shift their perspective on data from viewing it purely as a cost center to recognizing it as a valuable asset. This paradigm shift will require significant changes in corporate culture, accounting standards, and data governance practices. By adopting new metrics and valuation methodologies, companies can better appreciate the latent value in their data and leverage it to drive business growth. A culture that prioritizes data as an integral asset can foster innovation, improve decision-making processes, and sustain competitive advantages.
Effective communication and training programs are essential to instilling this new perspective across all levels of the organization. Cross-functional collaboration can also play a pivotal role in breaking down silos and promoting unified data strategies. Leaders must champion the integration of data insights into business processes, ensuring that data governance frameworks are robust and flexible enough to adapt to evolving business needs. As organizations embrace this cultural transformation, they can create a dynamic environment where data-driven decision-making becomes a cornerstone of their business strategy, driving sustained growth and innovation.
Implementing Effective Data Governance
In today’s data-centric era, businesses are often overlooking the invaluable potential of their internal data reservoirs. Sunil Soares, the founder and CEO of YDC Data Economics, underscores the critical importance of harnessing this first-party data to generate meaningful business insights, guide strategic decision-making, and enhance overall business performance. Speaking at the Data Governance & Information Quality Conference (DGIQ), Soares elucidated on the often-overlooked opportunities that lie within internal data monetization.
By effectively leveraging their own data, companies can gain a competitive edge, optimize operations, and drive growth. Soares highlighted that this internal data, which is directly sourced from within the organization, can provide a unique perspective that is often more reliable and specific than third-party data sources. The key takeaway from his presentation is the significant value businesses can unlock through proper data governance and quality management practices. This approach not only ensures data integrity but also maximizes the monetization potential of internal data, turning it into a robust asset for long-term success.