Data Monetization Trends: Insights From 1000 Organizations

An analysis of more than 1000 organizations generating measurable value from data, compiled by West Monroe, reveals several compelling trends that are influencing the future of data monetization and the future of the economy itself. Businesses are increasingly acknowledging the untapped potential of their data assets. As such, the landscape of data monetization is undergoing a rapid evolution, encompassing concepts and technologies like data marketplaces and AI. The following are some of the most significant trends in data monetization today:

AI and Advanced Analytics

One of the most exciting trends in data monetization is the use of AI and advanced analytics. Organizations are leveraging machine learning and natural language processing techniques to extract valuable insights from structured and unstructured data. For instance, some organizations use advanced text analytics and a variety of harvested web content to identify critical patterns and trends. This demonstrates how integrating multiple data sources, even those with relatively small volumes, can create high-value data products.

For example, The North Face successfully leveraged AI-driven natural language processing (NLP) technology from EasyAsk to enhance its online search functionality, allowing customers to use natural language queries. This improvement led to more accurate and relevant search results, significantly enhancing the customer experience. As a result, The North Face saw a 23% increase in online revenue, demonstrating the powerful impact of AI and advanced analytics in data monetization by driving higher conversion rates and sales through improved customer interactions.

Shift from Selling Data to Generating Economic Benefits

Data monetization is no longer limited to selling data. Organizations are now focusing on using data to improve internal processes, leading to measurable returns. This indirect form of data monetization can optimize supply chains, enhance customer service, and improve product development. Direct data monetization, on the other hand, entails externalizing data for commercial gain. Companies have successfully monetized data by enhancing their online search capabilities leading to improved customer experience and loyalty.

Walmart is a notable example of a company that generates measurable economic benefits from data. By incorporating social media trends into its search engine algorithm, Walmart was able to significantly reduce online shopping cart abandonment rates by 10–15%. This strategic use of data improved the customer shopping experience by making product searches more relevant and timely, but it also directly translated into increased sales and reduced lost revenue. This case highlights how leveraging data insights can lead to tangible financial gains and enhanced operational efficiency.

Monetizing Unstructured Content

Many organizations possess vast amounts of unstructured data that present opportunities for monetization. Techniques like text mining, sentiment analysis, and natural language processing can unlock the value hidden in this data. For example, some companies analyze project communications and documentation to gain greater foresight into project outcomes. This showcases the immense potential of mining unstructured data to derive actionable insights.

A lesser-known but fascinating example of generating immense value from unstructured content is the organization Thorn. Thorn uses advanced text analytics on a broad array of harvested web content and other unstructured data sources to combat human trafficking. By analyzing online ads, social media posts, and other digital footprints, Thorn’s technology identifies patterns and connections that help law enforcement agencies locate and rescue victims and apprehend traffickers. This innovative use of unstructured data generates significant social and economic benefits by addressing a critical issue, as well as demonstrating the transformative power of data analytics in creating real-world impact.

A Healthy Dose of Healthcare Data

The healthcare industry is rich with data that, when de-identified and aggregated, can provide significant value to various stakeholders. The pharma and hospital sectors are finding tremendous success in monetizing de-identified data, which is valuable for research, investment, and improving healthcare outcomes. This trend highlights a focus on drug research, driving down healthcare costs, spurring innovation, and improving patient care.

Allina Health is a compelling healthcare example. By integrating and analyzing unstructured data from clinical notes, patient feedback, and operational records, Allina Health has been able to identify and reduce unwanted clinical variations. This data-driven approach has significantly improved patient outcomes, such as lowering heart failure readmissions and enhancing stroke care. Financially, these initiatives have contributed to a $30 million impact out of a total $125 million in improvements. This case illustrates how effectively managing and monetizing unstructured healthcare data can lead to both enhanced patient care and substantial economic benefits.

Data Bartering and Trading

Data bartering and trading allow organizations to acquire valuable data without direct monetary transactions. Grocery stores and other retailers often use loyalty programs to exchange customer data for discounts and other benefits. Retailers can use this data to personalize marketing efforts, optimize inventory, and enhance customer experiences. Data bartering and trading offer a cost-effective way to enhance data assets, drive business growth, and foster business partnerships.

A prime example of data bartering is the collaboration between the agricultural company John Deere and various technology firms. John Deere, known for its farming equipment, collects vast amounts of data from its machinery, including soil conditions, crop health, and equipment performance. Instead of selling this data outright, John Deere often trades it with technology firms in exchange for advanced analytics tools and software solutions. This exchange allows John Deere to enhance its precision agriculture services, providing farmers with actionable insights to improve crop yields and operational efficiency. Meanwhile, the technology firms acquire valuable agricultural data to enhance their products and services.

Data Marketplaces

Data marketplaces are revolutionizing data monetization by enabling organizations of all sizes to participate in the data economy. These platforms allow companies to monetize their corporate data, providing a way for businesses to leverage their data assets and generate new revenue streams. As data governance practices evolve and organizations become more aware of the value of their data, data marketplaces are likely to grow in popularity. This shift not only monetizes and democratizes access to valuable datasets but also fosters innovation and collaboration across industries.

Databricks is one of several technology providers that now offer a data marketplace. Its Databricks Marketplace platform enables organizations to share and monetize their data assets, as well as access a wide variety of datasets for analytics and machine learning purposes. Companies from diverse industries, such as finance, healthcare, and retail, can leverage Databricks Marketplace to enhance their data-driven initiatives. For instance, a fintech company looking to develop new financial products or services can purchase anonymized transaction data shared by a financial services firm on the marketplace. In return, the financial services firm could access datasets related to market trends or consumer behavior to refine their own offerings.

Government and Open Data

Businesses are increasingly using government open-data platforms for commercial purposes. To enhance their data assets, companies are leveraging open data from government sources. Applications ranging from market research to product development and beyond can utilize this data. Open data from government sources can be a goldmine for businesses looking to enhance their data assets.

The City of Chicago’s open data initiative is a prime example of monetizing government data. By making datasets on vacant buildings, crime statistics, public health, and transportation publicly available, the city enables businesses, researchers, and developers to leverage this information for various purposes. Real estate developers, for instance, can use data on vacant buildings and neighborhood demographics to identify prime locations for new projects, while tech startups can create innovative applications like real-time transit updates using public transportation data. This initiative helps foster economic development and innovation, along with enhanced transparency and civic engagement, ultimately benefiting both the public and private sectors.

A Shift From Free Data to Licensing Insights

Businesses that have traditionally shared data for free are now exploring the economic benefits of licensing it. Licensing data allows organizations to retain ownership while generating revenue. This approach can be particularly beneficial for companies with valuable proprietary data that others are willing to pay for. Giving away data can become a slippery slope. Reconsidering this strategy, or cutting it off at some point, and moving towards licensing data or insights can create new revenue streams and ensure that data is used in a controlled and compliant manner.

Dollar General is a notable example of a company that transitioned from giving away data to licensing it. Initially, Dollar General shared its sales, inventory, and shopping basket data with its Consumer Packaged Goods (CPG) partners as part of their business relationships. However, recognizing the economic potential of this data, the company shifted to a licensing model. By doing so, Dollar General now generates revenue from its data assets, creating a self-funding data lake. This strategic move monetizes their valuable information and provides their partners with actionable insights, enhancing the overall value proposition for both parties.

Web Content Harvesting

Organizations can gather large amounts of data from the internet through web content harvesting, which they can then analyze and monetize. This approach is useful for competitive intelligence, market research, and other data-driven strategies. By systematically collecting and analyzing web content, organizations can generate new monetizable datasets and gain a competitive edge.

Sigma-Aldrich, now part of Merck, is a great example of a company effectively using web content harvesting. With over 180,000 products, pricing them optimally within the marketplace was a daunting manual task. To address this, Sigma-Aldrich employed web data collection agents to collect, aggregate, and structure competitor product pricing information in real-time. They directly feed this harvested data into their pricing models, enabling them to dynamically adjust prices and maintain competitiveness. This approach streamlines their pricing strategy and also provides a significant competitive edge in the market.

These major trends and examples highlight the diverse and innovative ways organizations are leveraging data to create new value streams. As the data economy continues to expand, businesses that embrace these trends will be well-positioned to unlock the full potential of their data assets and thrive in their own markets.