Data Value Model and Data Governance Valuation

A practical guide to Data Value Model, to understand, communicate and maximize the value of data product within an organization.

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Introduction to Data Value Model

How many times have we heard that data is the new oil? This comparison arises from the fact that, just as oil was fundamental to the industrial economy, data is becoming equally important in the digital economy. Just as oil was refined to obtain useful products, data is processed to generate valuable information.

However, there are some substantial differences: data is immaterial, renewable, and digitally generated, while oil is finite and material. Oil is used as a physical resource and has a universally quantifiable unit of measurement (dollars per barrel), whereas data creates value through analysis and innovation, and as a result, the measurement of their value is often underestimated and difficult to quantify.

The Data Value Model (DVM) addresses this need by presenting itself as a fundamental strategic tool for quantifying the intrinsic value of data and their derivatives: Data Products.

In this context, a Data Product represents a product or service built on the foundation of data, with the goal of creating value for the organization. Metrics, dashboards, models, applications, APIs, and other data-based artifacts are examples of Data Products used to enhance productivity, efficiency, and the quality of business work.

What makes the Data Product approach innovative is the shift from a model where data is considered a cost to a model where data is an asset. Like real products, Data Products are designed, built, maintained, and, if necessary, retired, considering the needs of internal and external customers and their potential to generate measurable value for the company.

Definition of Data Value Model

The Data Value Model (DVM) represents a fundamental methodology for economically valuing Data Products within an organization. This structured and systematic model focuses on the identification, classification, and measurement of the intrinsic value of data, especially when used to create and implement Data Products.

The DVM enables organizations to objectively assess the impact of Data Products, not only in terms of return on investment, but also in relation to business objectives and operational strategies. Through the DVM, a company can concentrate on providing the most relevant information for the business, thus optimizing the creation and management of Data Products to gain significant competitive advantages.

Metrics, dashboards, models, applications, APIs, and other data-based artifacts are examples of Data Products that are valued, and their impacts are tangible and quantifiable.

This synergistic approach between the Data Value Model and Data Products results in a high valuation of data within the organization, paving the way for strategic innovation, improved decision-making, and increased competitiveness in the market.

In order to facilitate the accurate calculation of net benefit, the DVM compares costs with benefits.

Data Product Net Business Value in the Data Value Model

Cost

Within the cost calculation, the following items are included:

1. People: the cost includes the effort of both internal and external personnel involved in the development and maintenance of the Data Product, as well as the training required to enhance skills, which is seen as an enabler for continuous improvement.
2. System: the costs typically include IT-related expenses, such as data platform maintenance, storage costs, and computational expenses. Additionally, it encompasses the costs of developments outsourced to external entities that need to be engaged in the Data Product’s creation.

Benefit

Within the calculation of benefits, the following items are included:

1. Optimization & internalization: the benefits include potential savings that can have an impact on the income statement and free up personnel to be allocated to other tasks. Examples of these benefits could include automation of activities, optimization of system costs mentioned earlier, savings from the internalization of activities, or simply from the decommissioning of Data Products and the associated costs.
2. Data Monetization: the benefits also encompass the actual sale of Data Products, which can be carried out both within the organization and externally, delivering to stakeholders a genuine service or product.
3. AI revenue boost: benefits generated through the application of artificial intelligence algorithms that enable revenue generation or cost reduction through new insights are also considered.
4. Compliance: in this case, it’s not about direct benefits but rather avoided risks that should be calculated based on the risk percentage and the potential amount of the fine or penalty.

These costs and benefits associated with each Data Product create a value map that represents the DVM.

The importance of the Data Value Model

Within the Gartner paper (1.) “Toolkit: How to Optimize Business Value from Data and Analytics Investments … Finally,” it is highlighted that, despite significant investments in data and analytics, CEOs and CIOs struggle to measure the resulting value. These difficulties are attributed to two main factors: the lack of structured methods for evaluating investments in data and analytics and the failure to align such investments with evolving market dynamics and the company’s strategic objectives.

The Data Value Model (DVM) plays a crucial role in valuing Data Products, focusing on key objectives that lead to business success:

1. Data is an asset and should generate measurable value: the DVM recognizes the intrinsic value of data as a valuable corporate asset. By treating data as strategic resources, the company can create and manage Data Products with the aim of generating measurable value. This approach allows for an objective assessment of the contribution of Data Products to the organization’s growth and success.
2. Being self-sustainable: the DVM enables units that generate Data Products to become self-sustainable through the margins they can generate via Data Products and, most importantly, by being able to demonstrate the value generated through a robust model.
3. Strategically choose to work where the value is higher: with the DVM, organizational units can identify high-value opportunities for the business. By focusing on Data Products with the highest potential to generate competitive advantages, the company can align its strategies with the market, gain a leadership position, and maintain a competitive edge.
4. Recognizability and visibility: by valuing Data Products through the DVM, teams increase their recognizability and visibility both internally and externally. Successful Data Products have a significant impact on productivity and business efficiency, earning recognition among employees and executives. At the same time, delivering high-quality data-driven products or services enhances the company’s reputation among customers and business partners.
5. Transparency in data-related investment utilization: with the DVM, the organization gains greater transparency in the utilization of investments in data-related initiatives, increasing trust in these initiatives and the teams involved.

In summary, the Data Value Model proves to be a key element for the enhancement of Data Products, contributing to making the organization self-sustainable, strategically value-oriented, recognized, and transparent in its data operations. By treating data as a measurable and valuable asset, the company can innovate and differentiate itself in the market, positioning itself as a leader in the data-driven approach.

With the assistance of the Data Value Model (DVM), data and analytics leaders will also be able to:

1. Define a roadmap for Data Literacy and Data-Driven Business Transformation: The DVM enables the strategic alignment of data and analytics initiatives with critical priorities and corporate objectives, providing guidance for enhancing data competency and fostering a data-centric culture within the organization.
2. Evaluate Business Value and Success Factors: With the DVM, management can comprehensively assess the business value and the elements that contribute to or hinder the success of data and analytics initiatives. This evaluation helps identify any gaps in capabilities and mitigate risks associated with data value propositions.
3. Filter and Classify Data Value Propositions: Using the DVM, management can conduct an initial screening of data and analytics value propositions. This process helps identify the most promising and relevant proposals for the organization.
4. Evaluate Data Value Propositions Based on Business Performance: Thanks to the DVM, it’s possible to conduct a detailed assessment of data value propositions, categorizing them based on their net economic impact per dollar spent and aligning them with the Strategic Business Focus categories.

With these methodologies and tools at their disposal, data and analytics leaders will be able to optimize resource utilization, focus on high-value initiatives, and align the company’s efforts toward an effective, success-oriented data-driven transformation.

DVM and ROAR model integration

The DVM is based on and simplifies the ROAR framework, which stands for Risk Opportunity Appetite Return. The ROAR framework was developed by Gartner (1) to address the shortcomings of traditional investment evaluation techniques like Net Present Value (NPV) and Return on Investment (ROI). These traditional models often do not effectively consider portfolios of data and analytics initiatives, alignment with corporate strategy, and the assessment of inertia risk.

The ROAR framework not only enhances the understanding of value and risk factors but also facilitates better communication with other business leaders. It integrates with NPV and ROI models, allowing organizations to rank and prioritize data and analytics initiatives that align with core business priorities. By evaluating both business value and capability gaps, the ROAR framework helps guide investment decisions and ensure success. It’s a strategic tool for engaging stakeholders, communicating value, and managing risk.

Using the ROAR framework in conjunction with a data and analytics strategy and an operational framework allows data and analytics leaders to:

• Collaborate with business stakeholders to select valuable data and analytics proposals.
• Ensure that investments in data and analytics align with core business priorities and deliver significant net business value.
• Identify and address critical capability gaps for success.

So, the ROAR framework provides a holistic approach to optimize investments in data and analytics, bridging the gap between investment and strategic outcomes.

The implementation of this framework is based on four steps.

Step 1: Create a Value Proposition

To create the Value Proposition, the ROAR model relies on three elements:

1. Mission-critical priorities (MCPs): These are the most important business priorities and objectives set by the corporate leadership. MCPs represent critical growth milestones and business outcomes established during the corporate strategy development process. They are measurable through specific Key Performance Indicators (KPIs).
2. Strategic business focus: This element defines what the organization intends to achieve and how it plans to do so. It can range from incremental improvements in efficiency and cost optimization for existing business models to entirely transformative innovations and new business and customer models. The strategic focus is designed to ensure the organization’s success in its specific industry and competitive environment, considering macroeconomic conditions.
3. Data and analytics initiatives: These are the data and analytics-related initiatives that the organization intends to undertake to support business priorities and strategic focus. These initiatives represent the specific projects and activities that will use data and analytics to achieve business objectives.

Step 2: Evaluate the net business impact of each Data Product

The ROAR model assesses the value drivers and success contributors/inhibitors for each value proposition to calculate the Net Business Value. Among these, the value drivers (DV) can be attributed to:

1. Business Value: It measures the impact of the value proposition on the business and includes factors such as support for critical business initiatives, the magnitude of revenue impact, effectiveness in sales and cost optimization, human resource effectiveness, risk, compliance effectiveness, and the urgency and timing of financial impact.
2. D&A Value: It measures the impact of the value proposition on the data and analytics team’s capabilities. This includes factors such as expanding into new types of analytics, using advanced analytics to enhance insights, reducing the data and analytics team’s personnel, and reducing external data and analytics service costs.

While the Success Contributors/Inhibitors (SCI) measure success and inhibition factors that represent gaps in the capabilities needed to deliver value from the value proposition. These drivers include factors related to data, analytics, technology, organization, costs, and go-to-market for data and analytics-based products.

Rita Sallam (Feb 2022): Toolkit: How to Optimize Business Value from Data and Analytics Investments … Finally

The ROAR model uses value and success drivers to calculate the “net business value score” for each value proposition. The net business value score represents the potential impact on business growth and allows for the comparison of different value propositions. This score can be positive when the value outweighs the risks or negative when the risks outweigh the value.

Assessments of value and success drivers enable data and analytics leaders to understand the organization’s existing and missing capabilities, close key gaps, and increase the likelihood of achieving the desired value. These assessments help communicate the vision and value narrative with other business leaders and guide the development of data and analytics strategy and operating model.

This step essentially incorporates the DVM into the ROAR model, where it verifies the alignment with MCPs and the focus on the business strategy, as evident in the next step.

Step 3: Identify the investment strategies for data and analytics initiatives

The ROAR model enables the assessment, evaluation, and classification of any number of individual data and analytics initiatives and value propositions. An example of the result of this evaluation is depicted in the figure below. The chart displays value propositions for each category of corporate strategic focus, plotted based on:

• Y-Axis: Net business value per dollar spent = VD — (SCI*TCO of three years).
• X-Axis: Three-year TCO.
• Bubble Color: Category of corporate strategic focus (Low — Cost optimization and efficiencies, Medium — Efficiency and growth, High — Differentiation, High growth, and Transformation).

TCO: Total Cost of Ownership — Estimate of expenses over the entire lifecycle of the Data Product.

VD: Value Drivers derived from Business Value and D&A Value.

SCI: Success Contributors/Inhibitors.

Rita Sallam (Feb 2022): Toolkit: How to Optimize Business Value from Data and Analytics Investments … Finally

The investment portfolio options in the ROAR model are based on:

1. Net Business Value Score: A higher score is better and is based on the assessment of business value and the risk of success contributors and inhibitors completed in the previous step, as well as a specified three-year Total Cost of Ownership (TCO) input for each value proposition.
2. Alignment with Corporate Strategic Focus: This is an input in the ROAR model and reflects the average score of corporate strategic focus across the investment portfolios. The model allows for simulating portfolio recommendations with different ranges of corporate strategic focus and investment levels.

The ROAR model identifies optimal portfolio options with the highest net business value score per dollar spent within the specified range for corporate strategic focus.

Rita Sallam (Feb 2022): Toolkit: How to Optimize Business Value from Data and Analytics Investments … Finally

Step 4: Integrate the ROAR model within the operational framework of the Data and Analytics (D&A) strategy

Integrating the ROAR model or, especially in the early stages, the Data Value Model (DVM), into the operational framework of the Data and Analytics (D&A) strategy supports management in defining stakeholder objectives, value propositions, and assessing capabilities and deficits to create a prioritized set of D&A investments.

Indeed, after mapping data and analytics value propositions to mission-critical priorities and conducting a thorough assessment of the business impact of these value propositions, you can systematically evaluate what is required from a perspective of data, analytics, and technology capabilities, organization, cost, and go-to-market. Addressing these aspects will minimize risk and increase the likelihood of successfully achieving the expected business value. With this information and insights, you will have the data needed to build or refine a data and analytics strategy, operational model, and roadmap, as well as communicate the expected value and what will be required to realize that value to business leaders.

Such integration should not only be considered during the strategy definition phase but should also be maintained by tracking the net business value of the data products implemented, allowing for a comparison with the expected value and verifying the actual achievement of results.

Rita Sallam (Feb 2022): Toolkit: How to Optimize Business Value from Data and Analytics Investments … Finally

Data Governance as a Data Product and its Data Evaluation

One of the most current and business-oriented definitions of Data Governance is the one proposed by Nicola Askham: (3.)”Data Governance is all about proactively managing your data to support your business achieve its strategy and vision”. In this sense, Data Governance can be understood as a strategic Data Product to enhance data usage within an organization. Like any Data Product, Data Governance aims to create measurable value by providing guidelines, procedures, and rules to ensure effective and efficient data management, use, and quality.

To quantify the benefits of Data Governance, it is essential to identify and monitor key metrics that reflect the value generated by data governance practices. Below are some examples of metrics that help quantify the benefits of Data Governance:

1. Increase in Revenue / Asset Value: Measures the impact of Data Governance on revenue growth and the value of company assets by improving data understanding and using information to drive new sales and business activities.
2. Cost Reduction: Evaluates the savings in financial and human resources achieved through the reduction of data duplications, duplicate management processes, and errors associated with poor data quality.
3. Compliance Support and Cost Reduction: Measures the savings achieved by meeting compliance objectives, avoiding penalty costs, and negative impacts on the company’s reputation.
4. Impact Analysis Support: Evaluates the ability of Data Governance to provide useful information for impact analysis, improving understanding of data changes and decisions.
5. Integrated Management Support: Measures how Data Governance facilitates communication and collaboration among different business departments, avoiding inefficiencies and ensuring prudent data management.
6. Data Repository Improvement: Evaluates the ability of Data Governance to enhance data quality and usability within repositories, facilitating access to authoritative information and deeper analysis.
7. Increase in Data Confidence: Measures the level of confidence and security in data-driven decisions, reducing the risk of errors and inaccurate decisions.

By quantifying the benefits of Data Governance through these metrics and integrating the Data Governance Value Proposition into the ROAR model, you can compare the benefits and demonstrate its crucial role in guiding the company toward strategic and value-driven data management. Data Governance as a Data Product becomes an essential driver of business success and the optimization of data-driven resources.

Let’s now try to map the direct costs and benefits using the Data Value Model (DVM):

Cost

Let’s consider the costs of a Data Governance initiative:

• People: We need to consider the effort expected from the Data Governance team and the effort expected from collaborators such as Data Owners and Data Stewards based on the average percentage of time dedicated to Data Governance activities;
• System: Let’s consider the costs of licenses for a Data Governance tool, as well as the costs for creating the infrastructure and application maintenance for processes and tools;

Benefit

The following items are included in the calculation of benefits:

Optimization & internalization: We consider among the benefits of the initiative:

• Reduction in the effort of data stakeholders necessary for data comprehension thanks to the creation of a company-wide shared Data Catalog and Data Dictionary. Considering the number of users with data access and the usage KPIs of the Data Catalog, the economic benefit can be analyzed and quantified.
• Internalization of knowledge, resulting in a reduction in the budget allocated to external data stewards.
• Reduction in project-related effort (and also risk) related to Impact Analysis, thanks to Data Lineage;

Data Monetization: Within the context of Data Monetization, Data Governance should be seen as an enabler that allows the sale of Data Products through key elements:

• A Data Marketplace where one can showcase properties and sell their Data Products.
• Key Quality Indicators (KQI) related to the created Data Products.
• Defining processes for proper Data Product lifecycle management.

Compliance:

• The regulatory aspect depends greatly on the industry to which it applies. However, we can start from the assumption that nowadays, all industries have a competent authority that, among other things, verifies that various organizations are aligned with guidelines.
• The legal aspect serves to mitigate the risks associated with possible legal actions by clients, which may be due to non-compliant data processing or incorrect data that can cause objective harm to the client as well as brand damage.

These costs and benefits associated with each Data Product create a value map that represents the Data Value Model (DVM).

Conclusion

The Data Value Model (DVM) presents itself as a fundamental tool for understanding and maximizing the value of data within an organization. It allows for the identification of Data Products, which are products or services built on data with the goal of creating measurable value and guiding the company toward strategic data management.

The DVM stands out for its innovative approach, where data is no longer considered a cost but a valuable corporate asset. Data Products are treated as real products, designed, built, and managed to generate tangible value for the organization, representing a value-oriented approach.

The DVM simplifies the ROAR model proposed by Gartner, making it easier to apply in the early stages of developing a Data Strategy.

This article also highlighted how Data Governance, often seen as a necessary cost, can be considered a strategic Data Product. Data Governance provides guidelines and rules to ensure effective data management, use, and quality, contributing to the creation of measurable value. Key metrics allow quantification of the benefits of Data Governance, such as operational cost reduction, increased sales through customer data analysis, improved operational efficiency, and regulatory compliance.

In conclusion, the Data Value Model and Data Governance as Data Products are essential for creating a data-centric corporate environment where information becomes a strategic lever for success. Implementing a DVM enables companies to maximize the value of data, enhance transparency and internal collaboration, gain a competitive edge, and drive innovation to address future challenges. Only with a conscious and value-focused approach to data can organizations achieve prosperity and sustainability in an increasingly data-driven business landscape.

References

1. Rita Sallam (Feb 2022): Toolkit: How to Optimize Business Value from Data and Analytics Investments … Finally
2. Saul Judah (Aug 2022): Data and Analytics Governance as a Business Capability: A Gartner Trend Insight Report
3. Nicola Askham (Jul 2021): High level Data Governance checklist