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The data monetization market stands at $220 billion and keeps growing as organizations look for new revenue streams. Companies see their data assets as valuable resources that can bring most important returns with proper management and marketing. This is especially true in the age of AI and can help businesses generate new revenue while they retain control of market advantages.
Our complete guide shows you the steps your organization needs to take for successful data monetization. You will learn about assessing data assets and developing effective strategies. The guide also covers building reliable infrastructure and creating valuable data products. We provide practical examples and applicable information to help businesses turn their data into profitable solutions through internal and external monetization methods.
Understand Your Data Assets
Organizations must understand their data assets completely before they can turn them into revenue. Research shows that 55% of IT and business leaders think about their data as ‘dark’ – an untapped resource they barely know exists – even though they are willing to acknowledge its crucial value to success [1].
Conduct a complete data inventory
Data inventory forms the foundation of successful data monetization. Organizations must document their data processing activities. This includes personal data collection, storage methods, access controls, and tracking mechanisms [2]. A data inventory should track:
- Data types and collection methods
- Storage locations and duration
- Usage purposes and sharing practices
- Controllers and processors information
- Security measures implementation
- International transfer protocols
Identify ways to make money
By 2025, the datasphere will reach 175 ZB (zettabytes) [3], which opens up huge money-making possibilities. Companies can use machine learning and natural language processing techniques to learn about valuable patterns in their structured and unstructured data [1]. Many businesses now focus on making money indirectly by improving their internal processes, which leads to measurable gains in supply chain optimization and better customer service [1].
Assess data quality and availability
Data quality assessment is vital to monetization success. Organizations need to review their data quality through multiple dimensions that include relevance, accuracy, timeliness, completeness, and consistency [4]. Poor quality data creates bottlenecks and affects decision-making processes negatively [4].
Data availability is a most important challenge because team members’ varying data skills can hinder effective data use [5]. Organizations face a paradox where complex data drives informed decisions but becomes harder to access as complexity grows [5]. Companies should create a single source of truth that’s available to everyone, whatever their expertise level [5].
Develop a Data Monetization Strategy
A clear and comprehensive strategy that converts data assets into measurable financial value drives successful data monetization. Market research indicates the global data monetization market will expand to $15.50 billion by decade’s end, demonstrating a compound annual growth rate of 22.1% [6].
Define clear objectives and goals
Companies need specific and measurable objectives for their data monetization projects. A recent study shows that all but one of these business and tech leaders lack data initiatives that line up with their business priorities and have clear outcomes [6]. The focus should be on generating immediate revenue while creating lasting value through analytical insights.
Choose between internal and external monetization
Organizations can choose between two main monetization strategies:
- Internal Monetization: The organization makes use of information assets to streamline processes, guide strategic decisions, and adopt improved processes within its operations [7]
- External Monetization: The organization generates new revenue streams through strategic collaborations by selling, licensing, or sharing data assets with external partners [7]
Line up with overall business strategy
The monetization strategy must naturally fit with the organization’s broader business objectives. This requires:
Cross-functional collaboration: Success comes from coordinated efforts between IT, marketing, legal, and other departments that work together for common goals [8]. Teams should unite rather than divide and focus on delivering measurable results [9].
Technical foundation: Organizations must build a solid IT foundation with well-governed, centralized data storage, advanced analytics, and business intelligence tools [10]. Research shows 62% of organizations now invest in advanced analytics, and 60% prioritize data infrastructure modernization [6].
Risk management: The strategy should address legal risks, data protection barriers, and competitive challenges. Organizations that use improper data monetization techniques face heavy fines and reputation damage [10]. They must implement reliable data governance policies and security measures while staying transparent with stakeholders.
Build the Right Infrastructure
A reliable infrastructure plays a significant role to reshape data assets into profitable ventures. Recent studies show that organizations can accelerate their enterprise-ready data and AI products through advanced data management software and generative AI [11].
Invest in Technologies
Data monetization today needs adaptable and flexible technology infrastructure. Data mesh architectures provide an affordable way to serve data products to endpoints and track usage details while measuring compliance [11]. Companies should focus on data lakehouse technology that connects and pulls data from multiple sources through different protocols [11].
Ensure data security and compliance
Your data security strategy should protect information at every stage of its lifecycle. Companies can monetize data securely through a data-centric security model that meets changing regulatory requirements [12]. You need these key security elements:
- Data encryption (both in transit and at rest)
- Multi-factor authentication and role-based access controls
- Security audits and penetration testing
- Incident response planning
- Data loss prevention tools [13]
Gartner predicts that modern privacy regulations will cover 65% of the world’s population’s personal information by 2023 [12]. Strong security measures have become a business necessity rather than just a best practice.
Develop data governance policies
Data governance plays a significant role in monetization success. Organizations need systematic governance frameworks that align with consumer interests when dealing with data products [14]. The governance structure should:
Define Compliance Frameworks: Different data categories need specific compliance rules that cover data privacy through GDPR, PII data handling, and legal contracts [14]. The governance framework needs to adapt to regulatory changes over time [12].
Implement Stewardship: Data stewards should take ownership of data products’ metadata to ensure accuracy and effectiveness [14]. Their oversight helps maintain quality standards and builds stakeholder trust through clear governance principles [14].
AI automation reduces data security and compliance risks by identifying and analyzing potential threats’ severity, scope, and root cause before they affect business operations [15].
Create and Launch Data Products
Companies need a systematic approach to design, testing, and pricing when they convert raw data into marketable products. Research demonstrates that organizations embedding core data knowledge across their teams take a crucial step toward building an informed culture that propels development [16].
Design data-driven products or services
Product development succeeds when it begins with thorough research that reveals user needs. Companies risk failure when they skip research because building products from scratch takes much time, effort, and resources [17]. The core team should think about these design elements:
- Data refinement and preparation
- User experience optimization
- Integration capabilities
- Scalability requirements
- Security implementation
Companies that embrace self-service analytics watch their data naturally flow into every discussion. These conversations start with simple questions and grow through discovery and “aha moments” [18].
Test and refine offerings
A systematic approach validates and refines data products effectively. Companies that use advanced analytics are 1.7 times more likely to give effective pricing guidance to their frontline sellers [19]. The testing process should emphasize:
Data Quality Validation: Organizations need relevant, homogenized, and well-categorized data to achieve meaningful results [16].
User Feedback Integration: Companies with current data respond quickly to situations and opportunities. This approach allows teams to adapt and learn together [16].
Establish pricing models
These are the most common pricing strategies you can use for data products:
Pricing Model Description Best For Per Unit Metered usage pricing Transactional data services Site Licensing Unlimited usage pricing Enterprise solutions Step Pricing Batch pricing with tiers Expandable offerings Research shows that profits grow with more pricing steps. Companies keep the steps minimal and with good reason too [20]. A simple pricing structure with three tiers and less than five add-ons helps companies control their pricing 30% better [19].
Your organization needs to understand the improved value and what customers will pay to monetize successfully [21]. Product owners should match pricing metrics to their customer’s value perception and keep the packaging structure simple [19].
Conclusion
Data monetization success just needs a step-by-step approach that includes a full picture of assets, strategic planning, resilient infrastructure development and careful product creation. Companies excel in these areas and position themselves to capture the most important value from their data resources while they retain control of security and compliance. Strategic pricing models, complete testing procedures, and strong governance frameworks are the foundations for environmentally responsible data monetization programs that deliver measurable results.
Organizations ready to start their data monetization experience should see this initiative as an ongoing process. The process requires continuous refinement and adaptation to market demands. Success stories show that companies achieve substantial returns on their data investments when they implement these proven approaches. Business leaders can visit to learn more about implementing these strategies effectively when they need expert guidance through their data monetization journey. Market opportunities continue to expand for organizations that execute data monetization programs with precision and strategic focus.
References
[1] – https://www.forbes.com/sites/douglaslaney/2024/09/26/data-monetization-trends-insights-from-1000-organizations/
[2] – https://redcloveradvisors.com/how-to-do-an-effective-data-inventory/
[3] – https://www.lesusacanada.org/viewpoints-data-monetization-and-valuation-in-the-age-of-ai-recap/
[4] – https://www.dataversity.net/data-quality-assessment-measuring-success/
[5] – https://corebts.com/blog/data-accessibility-for-modern-businesses/
[6] – https://deloitte.wsj.com/cio/unlocking-your-data-monetization-strategy-70acae61
[7] – https://zeenea.com/what-is-data-monetization/
[8] – https://www.stibosystems.com/blog/a-data-monetization-strategy-get-more-value-from-your-master-data
[9] – https://www.sas.com/en_us/insights/articles/data-management/5-ways-data-monetization-can-inform-data-strategy.html
[10] – https://www.trianz.com/insights/data-monetization-strategies-for-revenue-generation
[11] – https://www.ibm.com/think/insights/how-to-accelerate-your-data-monetization-strategy-with-data-products-and-ai/
[12] – https://www.forbes.com/councils/forbestechcouncil/2020/04/14/exploring-secure-data-monetization
[13] – https://dialzara.com/blog/data-monetization-ethics-10-best-practices/
[14] – https://www.linkedin.com/pulse/data-governance-monetization-amrita-priyadarsini-vacwc
[15] – https://www.ibm.com/think/insights/data-monetization-strategy
[16] – https://www.phocassoftware.com/resources/blog/data-refinement-when-data-is-less-like-gold-more-like-manure
[17] – https://adamfard.com/blog/data-driven-design
[18] – https://www.tableau.com/learn/articles/how-to-build-a-data-driven-organization
[19] – https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-art-of-software-pricing-unleashing-growth-with-data-driven-insights
[20] – https://courses.cs.washington.edu/courses/cse544/11wi/projects/kumar_kushal_moorthy.pdf
[21] – https://mitsloan.mit.edu/ideas-made-to-matter/what-everybody-should-know-about-data-monetization
