This article was originally published on LinkedIn
In recent years, data has stood as the lifeblood of enterprises, fueling critical decision-making and strategic initiatives. Organizations across various industries increasingly recognize the potential of data monetization to create new revenue streams. This shift is evident, as big and small businesses explore innovative ways to sell data and generate income beyond their traditional models. If an organization hasn’t started doing this yet, they will soon. To further underscore the significance of this initiative, Deloitte’s projections show that this is a $2.1B market with 22% YoY growth (CAGR). For the past few months, I’ve sat down with several CDOs & CIOs spanning a wide range of industries like healthcare, tech, financial services, and retail. These leaders have been grappling with operationalizing their data monetization processes. In these conversations, I’ve also observed certain patterns in how organizations are monetizing their data. The way I see it, there are two types of monetization paradigms being pursued.
- Indirect data monetization, which involves internal and external data & insights into existing products to improve customer experience & productivity. This is happening more organically as data science efforts are helping organizations embed data and insights into their existing product lines.
- Direct data monetization, which focuses on building and selling data as a product to generate top line growth to help customers buy and use data to make informed decisions. This monetization has several channels of execution including data marketplaces, product-led growth and sales-led execution motions for large enterprises.
The direct monetization trend in particular is gaining momentum, with high-performing organizations leading the way. However, challenges accompany this growth, and addressing them is crucial for sustained success. The most pressing challenges within the realm of direct data monetization that I’ve seen can be distilled into these themes:
- Data Distribution Complexity: In the data economy landscape, potential data buyers leverage data marketplaces to explore and acquire data products. However, as transactions conclude, and customers are prepared to receive data from sellers, the distribution process emerges as a critical concern. Essential data, vital for operations, is typically not uploaded directly to data marketplaces. They are shared directly among heterogeneous data warehouses. Unfortunately, this approach results in inefficiencies, elevated costs, and a growing set of challenges in maintaining service level agreements (SLAs). This problem is further exacerbated when data sellers have to aggregate data from local and remote offices, and then distribute data in different regions, in different clouds. In one instance, we’ve heard that data duplication occurred nine times when servicing customers globally, and different data product "copies" were not in sync.
- Data loss prevention: Another downside of data sharing and data monetization is data loss prevention. Contracts between sellers and customers reveal clauses forbidding buyers to “copy” or “resell” the data. In other cases, monetization is based on the consumption of the data. Ensuring data security and preventing unauthorized use or resale is a significant challenge, requiring constant visibility into data changes. In fact, The meteoric rise of generative AI and the risks it presents to data privacy, security, and governance further underscores the critical need for strategic approaches to data management within the data commerce landscape. If you want to see how we’re keeping confidential information protected, check out our data automation guide.
- Customer Analytics: In the era of mainstream data commerce, the meticulous tracking and reporting of revenue metrics such as ARR (Annual Recurring Revenue) and MRR (Monthly Recurring Revenue), alongside insights into product usage, conversions, and marketplace disbursement data across diverse channels, are becoming imperative for sales and customer success teams. For RevOps teams, establishing a trustworthy and unified view of customers within data marketplaces is not just important but a crucial element, particularly from a Business Intelligence (BI) reporting standpoint.
- Lead synchronization from different data marketplaces to your CRM: With the proliferation of data marketplaces, data sellers tend to list products across multiple platforms to maximize exposure. However, this approach comes with a drawback: the manual effort and overhead required to capture and synchronize leads into the CRM tool of choice. Additionally, manual data entry could lead to data duplication and other discrepancies.
Today, many organizations are addressing these issues by deploying human resources and constructing do-it-yourself (DIY) tech stacks. However, these solutions come with their own set of complexities, highlighting the need for more streamlined and user-friendly options in the data monetization landscape. So, if these fixes won’t work, what will? In order to better understand the needs of an organization that is trying to monetize their data, let's break this down into two personas.
- A VP of Alliance/BD, Marketing Ops or Sales Ops would need a data automation platform that integrates external databases into a harmonized data model, automates lead synchronization in real-time, and reconciles conflicting fields with user-driven rules.
- CIOs and CDOs require a secure data automation & traceability platform that integrates heterogeneous data warehouse data into a harmonized model, facilitates cost-effective data distribution, and ensures data security and loss prevention through smart contracts.
In the data monetization landscape, finding a practical solution for these challenges is crucial. If you’re looking to seamlessly automate your data monetization processes, Vendia can help. The journey doesn’t end here - stay tuned for more data commerce content on the horizon!