Digital data monetization is a crucial concept for modern businesses, enabling them to generate economic returns from market-facing, analytics-based products, features, or experiences. This comprehensive guide, drawing insights from MIT CISR research, explores the core capabilities required for successful digital data monetization, providing students with a clear understanding of this evolving landscape.
Understanding Digital Data Monetization: A Core Concept
For decades, companies have used data to improve efficiency and reduce costs, a process MIT CISR calls "data monetization by improving." This internal focus, where data helps predict operational issues or target market segments, has traditionally generated 51% of overall data monetization returns. It's a safer pursuit, understandable to leaders and forgiving of data quality issues, with employees as primary participants.
However, a new frontier in data monetization has emerged: market-facing data monetization. This is where companies deliver analytics products, features, and experiences directly to the marketplace, accounting for the other 49% of data monetization returns. Established companies are eager to emulate digital leaders like Facebook and Spotify but often struggle to adapt without disrupting existing revenue streams or slowing their digital transformation.
Digital data monetization requires elevated analytics service quality, intimate customer interactions, and the ability to manage unintended externalities. Traditional data activities alone are not enough; success hinges on building specific capabilities.
More Than Just Data Lakes: Building Capabilities for Monetization
Many companies invest heavily in data lakes, data science programs, and sensorizing products, yet leaders often question the high costs and vague promises. These investments only pay off if they effectively build out digital data monetization capabilities. It's not just about better business intelligence; it's about mastering a whole new world of market-facing data activities.
Consider Insurance Australia Group Limited (IAG), which began its digital journey in 2015. IAG established a Hadoop-based advanced analytics platform, consolidating customer data from thirty operational systems. However, this data lake was just one component of a broader strategy to build data monetization capabilities.
IAG's comprehensive approach included:
- Centralizing the management of customer data.
- Establishing a dedicated data quality team to monitor and remediate data quality issues.
- Creating a dedicated metadata team to oversee data tagging, indicating collection methods, lifespan, and usage limitations.
- Acquiring the analytics company Ambiata for its data science talent.
- Forming a customer advocacy team to represent customer interests and ethical concerns.
- Developing a framework for collecting, managing, using, and disclosing customer information.
- Implementing a required data extract request process for employees, reviewed by a data governance team.
- Deepening data partner agreement reviews, contractual controls, and audits.
Through these practices, IAG developed robust digital data monetization capabilities, demonstrating that a data lake is only one part of a larger, integrated strategy.
The Five Core Capabilities for Digital Data Monetization Success
Research involving 315 executives identified five critical capabilities that distinguish top-performing companies in digital data monetization. Firms proficient in these areas successfully deploy valued, profitable, competitive, and innovative data and analytics products, features, and experiences.
1. Data Asset Curation
Data asset curation involves activities that enhance data quality, make data consumable, and prepare it for reuse. Companies strong in this area view data not as a byproduct, but as a crucial firm resource that must be purposely sourced, productized, and delivered. They apply "old school" manufacturing concepts like continuous improvement and control charts to engineer high-quality data products.
Key capability-building practices:
- Master data management
- Metadata management
- Data integration
- Data quality management
2. Data Factory Platform
A data factory platform refers to purposely built software and hardware designed to securely, efficiently, and pervasively ingest, transform, and disseminate data. Companies with this capability can reconfigure data for new analytics deliveries and scale data activities cost-effectively across and beyond the enterprise.
Key capability-building practices:
- Internal APIs
- External APIs
- Leading-edge data tools and techniques (e.g., cloud computing, open source database software)
3. Data Science Techniques and Talent
Data science applies scientific methods, processes, algorithms, and statistics to extract meaning and insights from data. Companies with strong data science capabilities have analytics-savvy employees across the organization, including within product units. These employees are trained and supported to make evidence-based decisions and use methodologies that foster testing and learning.
Key capability-building practices:
- Reporting and dashboards
- Visualization
- Statistics
- Machine learning and specialized analytics
- Data scientist hiring and development
- Data science training
4. Customer Understanding
Customer understanding is the accurate and actionable knowledge about customer needs and behavior. Companies achieve this by collecting information through various customer connections and analyzing it to uncover insights about demographics, sentiments, context, usage, and desires. A deep grasp of customer needs and domain areas is essential.
Key capability-building practices:
- Involvement of customer-facing employees in product development
- Customer co-creation
- Test-and-learn approaches and methodologies (e.g., A/B testing)
5. Acceptable Data Use
Acceptable data use means leveraging people, data, and analytics in ways that comply with laws and regulations, and also align with organizational values and those of ecosystem actors. Failing to use data acceptably can lead to financial, reputational, or legal penalties. This capability manifests as data governance that balances compliance with ethical concerns, guiding employee and partner data activities effectively.
Key capability-building practices:
- Employee data use oversight
- Partner data use oversight
- Customer self-management of customer data collection, use, and deletion
- Data lineage management
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The Payoff: Why Digital Data Monetization Matters
Top-performing companies, defined by superior revenue growth, profitability, innovation, agility, and time to market, engage in significantly more and higher-quality digital data monetization activities. These top performers report that information solutions contribute 20% to 26% of total company revenues, compared to just 12% to 14% for low performers.
While efforts like building data lakes and launching data science programs might seem like isolated initiatives, they are crucial steps on a company's journey to becoming digital. Business leaders should coordinate their data activities by evaluating how each effort builds these five digital data monetization capabilities and the economic returns they are designed to produce.
To assess your company's potential for successful data and analytics product deployment, evaluate its proficiency in practices such as using leading-edge data technologies, external APIs, machine learning, an analytics-savvy product unit, customer co-creation, and customer self-management of customer data. These practices, while challenging, enable the most elusive and impactful outcomes in digital data monetization.
Frequently Asked Questions (FAQ) about Digital Data Monetization
What is digital data monetization?
Digital data monetization occurs when companies generate direct or indirect economic returns from market-facing, analytics-based products, features, or experiences. This differs from traditional data monetization, which primarily focuses on internal operational improvements and cost savings.
How do digital companies differ from digitized companies in data monetization?
Digitized companies primarily use data to improve internal operations and cut costs. Digital companies, in addition to internal improvements, actively generate returns by delivering analytics-based products, features, or experiences directly to the market, requiring a more advanced set of capabilities.
Why are data lakes not enough for digital data monetization?
While data lakes are important for consolidating data, they are just one component. Successful digital data monetization requires an array of complementary practices that build capabilities like data quality, metadata management, customer advocacy, and ethical data use, which go beyond merely storing data.
What are the five core capabilities for digital data monetization?
According to MIT CISR research, the five core capabilities are: Data Asset Curation, Data Factory Platform, Data Science Techniques and Talent, Customer Understanding, and Acceptable Data Use. Proficiency in these areas leads to optimized data monetization portfolios and better business outcomes.
How does acceptable data use contribute to data monetization?
Acceptable data use ensures that data and analytics are used in ways that are compliant with laws, regulations, and ethical guidelines. It builds trust, minimizes risks of financial or reputational damage, and fosters fluid norms and policies for employee and partner data activities, ultimately enabling sustainable and successful data monetization.