Dr Milan KumarCIO of ZF Commercial Vehicles and global technology leader with extensive experience in digital transformation and IT leadership.
Tim Berners-Lee’s claim“Data is a precious thing and will outlast the systems themselves,” is more relevant than ever as advances in AI increase the demand for quality data to drive business innovations. 2.5 quintillion bytes of data created daily, organizations are striving to harness this power, creating a greater opportunity for us: data monetization.
There are two main approaches to this. Direct Monetization It’s quite simple: you sell your data or the information you have extracted from it. Indirect monetizationon the other hand, is about using data to improve existing products and services.
I have prepared a list of four effective strategies to monetize your data. But of course, there are always challenges, right? We need to overcome these challenges to create new revenue streams.
Here we go!
Four Strategies for Successfully Monetizing Data
Next year, the EU data economy is expected to reach 829 billion euros In value, they represent about 6% of regional GDP. This highlights the immense economic value of data, which prompts us to examine monetization strategies:
1. Sale of Licensed Data
Selling data is a key strategy to monetize data. Companies must carefully analyze the value created by their data offerings and the price customers are willing to pay to effectively leverage this strategy.
Many organizations misunderstand data monetization, either by approaching it too narrowly (e.g., simply selling data sets) or too broadly (e.g., creating general benefits from using data). The key is to distinguish between creating value from data and realizing that value in the form of financial gain.
For example, a company gives its partners access to customer data for marketing purposes, but fails to monetize it, which means missing out on potential revenue. Instead, it should aggregate information such as targeted analytics of consumer behavior and sell it as high-value products for obvious financial gains.
Ignoring the immense potential of data means underutilizing the company’s talent, facilities or equipment.
2. Sale of data by subscription
This data monetization approach uses multiple service models, covering different stages from raw data access to advanced insights and analytics:
• Data as a Service (DaaS) model: Provides access to raw data from various sources, monetized through subscriptions or access fees. Ideal customers would be organizations requiring fundamental data for analysis, simplifying data acquisition processes.
• Insight-As-A-Service (IaaS) model: Delivers actionable insights from data analysis, at a premium price for immediate value. Targeted at market research firms and businesses looking for informed decisions from complex data.
• Analytics as a Service (AaaS) model: Delivers advanced analytics and real-time visualization for finance, healthcare, and other industries. Monetizes through premium services, supporting strategic decision-making, operational efficiency, and business value creation.
Value-added benefits enable higher price points, and subscription-based models reduce friction for buyers.
3. Product improvement
Data is like any product: it is developed through informed effort, and like a product, it serves a specific purpose: to provide answers through information. Gartner Analysts state: “Unless data and analytics leaders create information products by design, the value of their information assets cannot be realized.”
This monetization strategy involves enriching a product with customer-centric improvements. Let’s say you sell thermostats. Late maintenance often forces customers to pay more to fix issues. However, if you enhance your thermostats with AI to send timely service and maintenance alerts, you can demonstrate value to customers and justify higher prices.
The success of this strategy will depend on the calculations: will customers find it more profitable to pay for repairs or to invest in a slightly more expensive smart product? Product, marketing and sales teams must therefore work together to translate these improvements into clear financial benefits.
4. Process improvement
According to McKinsey researchers in a Harvard Business Review article, at a large national bank, a single customer data product generated nearly 60 use cases, generating $60 million in additional revenue and saving $40 million per year by reducing losses. Use cases included credit risk scoring and customer service chatbots, as well as many other process improvements.
This example highlights the immense potential for 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 improve decision-making (e.g., predictive analytics), businesses can significantly improve operational efficiency and productivity. Additionally, enriching datasets with AI-generated synthetic data can help create specialized AI models for faster time-to-market, opening up new revenue streams.
However, data monetization efforts should be approached with caution.
Challenges and solutions for data monetization
Data has significant business value, but monetizing it presents challenges. Here are the main obstacles and potential solutions:
1. Costly data breaches: In 2023, it costs on average $4.45 million per data breach, a 15% increase from 2020. The use of strong encryption, strict access controls, and regular security audits are necessary to protect data and maintain user trust.
2. Ineffective decision making due to poor data quality: Poor data quality can result from data silos and the failure to include unstructured data. Implementing clear data collection processes, using advanced data cleansing tools, and adhering to robust data governance structures can significantly prevent loss and improve data-driven decision-making.
3. Operational inefficiency due to inadequate technological infrastructure: Without robust infrastructure, data processing becomes slow, leading to delays in decision-making and operational improvement. Investing in cloud services, leveraging advanced big data technologies, and building a skilled data analytics team to transform raw data into actionable insights is essential to ensure operational efficiency.
4. Frictions due to data privacy and transparency issues: In a 2019 survey, 63% consumers said they believe companies lack transparency in how they use data, and 48% Businesses have stopped buying due to privacy concerns. Companies must enforce rigorous privacy policies, conduct audits, and maintain transparent and ethical data practices to mitigate legal risks and maintain customer trust.
Ready to take the plunge?
As we approach 2024, data monetization has moved from speculation to a cornerstone of business strategy. 36% of executives are already reaping profits from data or technology sales and another 16% plan to join them, the path to success is clear: the future is data-driven. Will you lead or follow?
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs, and technology leaders. Am I eligible?
