Dr. Milan Kumar, CIO of ZF Commercial Vehicles and global tech leader with extensive experience in digital transformation and IT leadership.
Tim Berners-Lee’s assertion, “Data is a precious thing and will last longer than the systems themselves,” is more pertinent than ever as AI advancements heighten the demand for quality data to drive business innovations. With over 2.5 quintillion bytes of data created daily, organizations are scrambling to harness this power, creating a bigger opportunity for us—data monetization.
There are two main approaches to it. Direct monetization is pretty straightforward—you sell your data or the insights you’ve extracted from it. Indirect monetization, on the other hand, is all about using data to improve existing products and services.
I’ve prepared a list of four effective strategies for you to monetize data. But of course, there are always challenges, right? We need to overcome these challenges to create new revenue streams.
Let’s get started!
Four Strategies For Successfully Monetizing Data
Next year, the EU’s data economy is expected to reach €829 billion in value, accounting for around 6% of regional GDP. This highlights data’s immense economic value, prompting us to look into monetization strategies:
1. License-Based Data Selling
Data selling is a key strategy for monetizing data. Companies must carefully analyze the value created by their data offerings and what customers are willing to pay for them to leverage this strategy effectively.
Many organizations misinterpret data monetization, either approaching it too narrowly (e.g., merely selling datasets) or too broadly (e.g., creating general benefits from data use). The key is distinguishing between creating value from data and realizing that value as financial gain.
For instance, a company gives partners access to customer data for marketing but fails to monetize it, missing potential revenue. Instead, they should package insights like targeted consumer behavior analytics and sell them as high-value products for clear financial gains.
Ignoring data’s immense potential is akin to underutilizing the company’s talent, facilities or equipment.
2. Subscription-Based Data Selling
This approach to monetizing data utilizes multiple service models, covering different stages from raw data access to insights and advanced analytics:
• Data-As-A-Service (DaaS) Model: Provides raw data access from various sources, monetized through subscriptions or access fees. Ideal customers would be organizations needing foundational data for analytics, simplifying data acquisition processes.
• Insight-As-A-Service (IaaS) Model: Delivers actionable insights from data analysis, priced higher for immediate value. Appeals to market research firms and businesses seeking informed decisions from complex data.
• Analytics-As-A-Service (AaaS) Model: Offers advanced analytics tools and real-time visualization, catering to finance, healthcare and other industries. Monetizes through premium services, supporting strategic decision-making, operational efficiency and business value creation.
Value-add benefits allow for selling at higher prices, and subscription-based models reduce friction for buyers.
3. Product Enhancement
Data is akin to any product—it’s crafted through insightful effort and, like a product, serves a distinct purpose: providing answers through information. Gartner analysts assert, “Unless data and analytics leaders create information products by design, the value of their information assets cannot be realized.”
This monetization strategy revolves around enriching a product with customer-centric enhancements. Let’s say you sell thermostats. Untimely maintenance often requires customers to pay more to fix issues. However, if you enhance your thermostats using AI to send timely service and maintenance alerts, you can demonstrate the added value to customers and justify higher prices.
The success of this strategy will depend on the math—whether customers find it more cost-effective to pay for repairs or invest in a slightly higher-priced smart product. Hence, product, marketing and sales teams must work together to showcase such improvements into clear financial benefits.
4. Process Enhancement
According to McKinsey researchers in a Harvard Business Review article, at a large national bank, a single customer data product has driven nearly 60 use cases, generating $60 million in incremental revenue and saving $40 million annually by reducing losses. The use cases included credit risk scoring and customer service chatbots, as well as numerous other process enhancements.
This example highlights the immense potential of data monetization through enterprise AI. By using advanced AI technologies to automate routine tasks (e.g., invoice processing), optimize workflows (e.g., supply chain management) and enhance decision-making (e.g., predictive analytics), businesses can significantly improve operational efficiency and productivity. Additionally, augmenting datasets with AI-generated synthetic data can help create specialized AI models for faster market launches, opening new revenue streams.
However, data monetization endeavors must be approached with caution.
Challenges And Resolutions For Data Monetization
Data holds significant business value, but monetizing it presents challenges. Here are the key obstacles and potential solutions:
1. Costly Data Breaches: In 2023, it cost an average of $4.45 million per data breach, rising 15% from 2020. Employing strong encryption, strict access controls and regular security audits is necessary to protect data and maintain user trust.
2. Ineffective Decision-Making Due To Poor Data Quality: Poor data quality may result from data silos and non-inclusion of unstructured data. Implementing clear data collection processes, using advanced data cleaning tools and adhering to robust data governance structures can significantly prevent losses and improve data-driven decision-making.
3. Operational Inefficiency Due To Inadequate Technological Infrastructure: Without robust infrastructure, data processing becomes slow, causing delays in decision-making and improvement in operations. Investing in cloud services, leveraging advanced big data technologies and building a skilled data analytics team to transform raw data into actionable insights is nonnegotiable to ensure operational efficiency.
4. Friction Due To Data Privacy And Transparency Concerns: In a 2019 survey, 63% of consumers said they believe companies lack transparency in data use, and 48% stopped purchasing due to privacy issues. Businesses need to enforce robust privacy policies, conduct audits and maintain transparent, ethical data practices to mitigate legal risks and maintain customer trust.
Ready To Take the Plunge?
As we move through 2024, data monetization has shifted from mere speculation to a cornerstone of business strategy. With 36% of executives already reaping profits from data or technology sales and another 16% planning to join them, the path to success is clear: The future is data-driven. Will you lead or follow?
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