Digital data monetization strategies are crucial for modern businesses looking to transform their vast datasets into tangible economic value. While companies have historically used data to improve internal operations and cut costs (a process known as "data monetization by improving"), the true potential lies in market-facing initiatives. This article, drawing insights from MIT CISR research, explores the meaning of digital data monetization and outlines the five essential capabilities that drive success in this dynamic field.
Understanding Digital Data Monetization Strategies
For decades, organizations have leveraged data to enhance efficiency and reduce expenses. This internal focus, often called "data monetization by improving," helps "digitized" companies save millions. Examples include using production data to predict and fix operational issues or analyzing customer data for better market targeting. This approach is generally low-risk because it focuses on internal operations and involves employees who are often more forgiving of data imperfections. Currently, about 51% of data monetization returns come from these internal improvements.
However, a significant 49% of data monetization returns now come from delivering analytics-based products, features, and experiences directly to the marketplace. This is what we call digital data monetization. It's how established companies aim to emulate the success of digital giants like Facebook and Spotify. This transition, described as a journey to become "digital," involves creating valuable market-facing digital offerings without disrupting existing revenue streams. It requires a new set of skills and strategies, as it involves higher analytics service quality, deeper customer interactions, and managing unexpected outcomes.
Building Essential Digital Data Monetization Capabilities
Many companies invest heavily in data lakes, data science programs, and sensorizing products, yet struggle to see clear returns. These investments only pay off when they contribute to building robust digital data monetization capabilities. It's not just about better "business intelligence"; it's about mastering a new realm of market-facing data activities.
Consider Insurance Australia Group (IAG), which embarked on its digital journey in 2015 to build new data monetization capabilities. Beyond just establishing a Hadoop-based advanced analytics platform to consolidate customer data, IAG undertook several complementary activities:
- Centralizing customer data management.
- Creating a dedicated data quality team to monitor and remediate data issues.
- Establishing a metadata team to tag data with contextual information.
- Acquiring analytics talent by purchasing Ambiata.
- 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 data extract request process for employees, requiring clear intent and governance review.
- Deepening reviews and controls for data partner agreements.
These practices illustrate that building digital data monetization capabilities goes far beyond just technology; it involves a holistic organizational learning and transformation.
The Five Pillars of Successful Digital Data Monetization
MIT CISR's 2018 study of 315 executives identified five key capabilities crucial for optimizing data monetization portfolios and achieving superior outcomes. Companies proficient in these capabilities successfully deploy data and analytics products, features, and experiences that are valued by customers, profitable, competitively superior, innovative, and often first to market. These five capabilities are:
1. Data Asset Curation: Ensuring Quality and Usability
Data asset curation involves all the activities that build data quality, make data consumable for end-users, and prepare it for reuse. Companies with strong curation capability see data not just as a byproduct but as a core resource. They actively source, productize, and deliver high-quality data using processes inspired by manufacturing concepts like continuous improvement and control charts.
Key practices include:
- Master data management
- Metadata management
- Data integration
- Data quality management
2. Data Factory Platform: Scalable Data Delivery
A data factory platform is a specially designed software and hardware infrastructure that securely, efficiently, and pervasively ingests, transforms, and disseminates data across and beyond an enterprise. High-capability companies can reconfigure data to deliver analytics in novel ways and scale their data activities cost-effectively.
Key practices include:
- Internal APIs for seamless data exchange
- External APIs for market-facing data products
- Leading-edge data tools and techniques (e.g., cloud computing, open-source database software)
3. Data Science Techniques and Talent: Extracting Insights
Data science applies scientific methods, processes, algorithms, and statistics to extract meaningful insights from data. Companies excelling in this capability have analytics-savvy employees across all departments, including product units. These employees are trained and supported to make evidence-based decisions and use methodologies that foster testing and learning.
Key practices include:
- Reporting and dashboards
- Data visualization
- Statistical analysis
- Machine learning and specialized analytics
- Targeted data scientist hiring and development
- Comprehensive data science training programs
4. Customer Understanding: Knowing Your Audience Deeply
Customer understanding refers to accurate and actionable knowledge about customer needs and behavior. Companies achieve this by collecting information through various customer interactions and analyzing it to gain insights into demographics, sentiments, context, usage patterns, and desires. Strong customer understanding means deeply grasping customer needs and their domain areas.
Key practices include:
- Involvement of customer-facing employees in product development
- Customer co-creation initiatives
- Test-and-learn approaches and methodologies (e.g., A/B testing)
5. Acceptable Data Use: Balancing Ethics and Compliance
Acceptable data use ensures that people, data, and analytics are used not only in compliance with laws and regulations but also in alignment with organizational values and those of ecosystem actors. Failing to use data acceptably can lead to financial, reputational, or regulatory penalties. This capability manifests as robust data governance that effectively balances compliance with ethical concerns. High-capability companies have fluid norms and policies guiding employee and partner data activities.
Key practices include:
- Employee data use oversight
- Partner data use oversight
- Customer self-management of their own data (collection, use, deletion)
- Data lineage management
The Payoff of Digital Data Monetization
Top-performing companies, characterized by high revenue growth, profitability, innovation, agility, and speed 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 their total company revenues, compared to just 12% to 14% for lower performers.
While initiatives like building data lakes or launching data science programs might seem isolated, they are crucial steps in a company's journey to becoming truly digital. Business leaders should coordinate these data activities by focusing on how they build these five digital data monetization capabilities, thereby ensuring they produce valuable returns.
To gauge a company's potential for successful deployment of data and analytics products, features, and experiences, evaluate its proficiency in key predictors of top digital data monetization performance:
- Use of leading-edge data technologies
- External APIs
- Use of machine learning
- An analytics-savvy product unit
- Customer co-creation
- Customer self-management of customer data
These practices are challenging to master but are essential for achieving the most impactful outcomes.
Frequently Asked Questions About Digital Data Monetization
What is digital data monetization and why is it important for students to understand?
Digital data monetization is when companies generate economic returns from market-facing, analytics-based products, features, or experiences. It's crucial for students to understand because it represents a significant shift in how businesses create value from data, moving beyond internal efficiency to direct market offerings. This skill set is increasingly in demand in various industries.
How do digital data monetization strategies differ from traditional data monetization?
Traditional data monetization, or "data monetization by improving," focuses on internal operations to cut costs and increase efficiency (e.g., optimizing supply chains). Digital data monetization, conversely, involves creating new revenue streams by offering data-driven products or services directly to customers in the marketplace, much like how digital-native companies operate.
What are the main challenges companies face when implementing digital data monetization capabilities?
Companies often struggle with the elevated analytics service quality levels, intimate customer interactions, and managing unintended consequences that digital data monetization entails. Building the five core capabilities—data asset curation, data factory platform, data science talent, customer understanding, and acceptable data use—requires significant organizational learning and a strategic shift in mindset.
Can you provide a summary of the five key capabilities for digital data monetization?
Certainly! The five key capabilities are: (1) Data Asset Curation (ensuring data quality and reusability), (2) Data Factory Platform (scalable infrastructure for data processing and dissemination), (3) Data Science Techniques and Talent (extracting insights and making evidence-based decisions), (4) Customer Understanding (deep knowledge of customer needs), and (5) Acceptable Data Use (ethical and compliant data governance). Mastering these enables successful market-facing data initiatives.