Data is a powerful resource. But extracting its value requires more than access—it needs strategy and structure. Businesses that excel in data monetisation or data products achieve success by following proven frameworks. The IFMAPI framework, outlined in Drive RAPPID Results from Data, offers a clear path to identify, evaluate, and optimise data initiatives. Let’s explore its six components and how they guide decisions between data monetisation and data products.
Understanding Data Monetisation
Data monetisation transforms raw data into measurable economic value. Organisations typically approach monetisation in two ways:
- Direct Monetisation: Selling raw or processed data to third parties, such as through licensing agreements or anonymised customer insights.
- Indirect Monetisation: Using data internally to improve processes, enhance customer experiences, or drive efficiency—examples include optimising inventory or tailoring marketing strategies.
Direct monetisation delivers immediate revenue but involves compliance risks, while indirect monetisation embeds data-driven improvements into your operations, creating sustainable value.
Defining Data Products
Data products are tools, applications, or solutions built using data to solve specific problems. These products often leverage advanced analytics, AI, or machine learning to generate actionable insights.
Examples of data products include:
- Personalised recommendation engines in retail.
- Fraud detection systems in banking.
- AI-powered chatbots for customer support.
- Real-time dashboards for operational monitoring.
Data products typically provide value by driving efficiency, enhancing decision-making, or creating new revenue streams, rather than delivering direct financial returns.
The IFMAPI Framework: Driving Measurable Value
The IFMAPI framework offers a step-by-step process to maximise value from data initiatives, whether you focus on monetisation or product development.
1. Identify Value
We start by pinpointing where data can deliver measurable value. This involves analysing business challenges, market demands, or customer needs to uncover opportunities.
- For Data Monetisation: Determine which datasets have external market value. For instance, anonymised customer behaviour data could appeal to advertisers or industry analysts.
- For Data Products: Identify pain points or inefficiencies that data solutions could address, such as streamlining supply chains or improving user experiences.
Questions to ask:
- What problems can our data solve?
- Who stands to benefit from these solutions?
- What unique insights or capabilities can we offer?
2. Forecast Value
Next, we evaluate the potential financial or operational impact of each initiative. This step helps prioritise investments based on anticipated outcomes.
- For Data Monetisation: Assess the demand for your data and identify pricing models that maximise returns.
- For Data Products: Estimate how a product will reduce costs, increase revenue, or improve efficiency.
Questions to ask:
- What returns can we realistically achieve?
- Is the opportunity scalable?
- Are there risks that could undermine success?
3. Measure Value
Measuring value requires clear KPIs and metrics to track success. Without these, it’s impossible to validate outcomes or secure further investment.
- For Data Monetisation: Track revenue generated through data licensing or sales.
- For Data Products: Monitor adoption rates, user engagement, and measurable impacts like improved customer retention or reduced operational costs.
Questions to ask:
- What metrics define success?
- How will we measure and track these metrics?
- Do we have the tools to ensure accurate reporting?
4. Attribute Value
Value attribution ensures that outcomes link directly to the relevant data initiative. This step clarifies ROI and informs future investments.
- For Data Monetisation: Tie revenue back to specific datasets or licensing deals to identify what works best.
- For Data Products: Attribute improvements in KPIs (e.g., increased sales) to the data product rather than external factors.
Questions to ask:
- How can we link results to specific initiatives?
- What tools or methodologies support accurate attribution?
5. Publish Value
Sharing results with stakeholders builds trust and demonstrates the impact of data initiatives. Transparent reporting ensures continued support for future investments.
- For Data Monetisation: Showcase revenue and market share growth to executives and investors.
- For Data Products: Use case studies, dashboards, or performance reports to communicate success.
Questions to ask:
- How do we share results effectively with stakeholders?
- Which formats—dashboards, reports, presentations—work best for our audience?
6. Improve Value
Continuous improvement is critical. We use feedback and performance data to refine initiatives, unlock new opportunities, and maximise returns.
- For Data Monetisation: Enhance data quality, explore new markets, or refine pricing strategies.
- For Data Products: Incorporate user feedback, optimise algorithms, or add new features to improve performance.
Questions to ask:
- How will we gather feedback?
- What processes ensure iterative improvements?
Making the Right Investment
The IFMAPI framework helps weigh the pros and cons of data monetisation versus data products by focusing on measurable outcomes.
| Key Considerations | Data Monetisation | Data Products |
| Organisational Goals | Focused on generating direct revenue. | Aimed at enhancing internal efficiency or customer satisfaction. |
| Data Quality and Governance | Requires compliance with privacy regulations and robust data governance. | Demands high-quality data for accurate analytics and insights. |
| Scalability | Limited by the market size for your data. | Scalable through automation or feature expansion. |
| Market Demand | Dependent on external interest in your data. | Driven by internal needs or customer requirements. |
| Return on Investment (ROI) | Although typically quicker, often less sustainable. | Long-term value creation with higher upfront costs. |
Why IFMAPI Matters
The IFMAPI framework ensures that data investments align with organisational goals, prioritise high-impact initiatives, and adapt to changing conditions. By following its six steps, you can confidently decide whether to monetise your data, develop data products, or pursue both.