In short, data monetization is a strategic process of converting organizations’ raw data into sellable data products or services to increase revenue, reduce costs, and/or increase profits. One example of a data monetization strategy could be a retailer providing or selling its customers’ purchasing behavior data to suppliers. While the concept is not new, the heightened recognition of data's intrinsic value extends beyond the organization itself to also include its stakeholders. This, coupled with advancements in modern technologies and analytics capabilities, has contributed to making the process more efficient and accessible for a broader range of organizations. The same capabilities, combined with the trend of knowledge-based management on the data consumer's side, have increased the demand for external data sources. This article covers two real-life examples of data monetization, explains the key benefits of data monetization, and describes how to monetize your data in practice.
In this chapter, we explore two real-life cases—one highlighting success from a retailer's perspective and the other providing insights into the challenges perceived through the eyes of the supplier.
Let's begin with the success story. In this instance, the retailer faced substantial internal demands to establish a comprehensive data platform to meet diverse reporting and analytics requirements. The platform featured a modern architecture, incorporating different layers of data (further details on the potential architecture are provided at the end of this article). Leveraging the existing data within the platform, the retailer crafted substantial datasets for its suppliers, encompassing crucial information such as sales figures, forecasts, and assortment details.
Upon publishing this data, suppliers received a wealth of insights through inquiries, gaining an understanding of the specific metrics sought by suppliers. With this accessible data, suppliers could strategically plan their production, manage inventory levels, and assess their performance in comparison to competitors. The granular data also proved invaluable for evaluating the efficacy of geographically targeted marketing campaigns. Suppliers, equipped with insights into their comparative market standing, could make informed decisions regarding product development and pricing adjustments to secure a more competitive market positioning—benefiting both the suppliers and the retailer.
The second example highlights the indispensable role of data for suppliers and the significant efforts and uncertainties they are willing to navigate to gain necessary insights. A prominent brand specializing in clothing and sporting goods aimed to position itself as a "winner" in major cities. The strategic rationale was grounded in the belief that trends originating in these influential cities would eventually spread throughout the global market. For any company, the critical measurement of strategy execution is important for effective planning and the subsequent steps in the pursuit of achieving its goals.
The majority of the brand's products were typically sold through retailers. These retailers often operated with geographically specific organizations that placed centralized orders for products, later distributing them to physical shops or selling them through their e-commerce channels. The challenge arose when the brand lost visibility in the specific locations where its products were sold, making it challenging to accurately measure the execution of its strategy to dominate major cities.
To assess the effectiveness of their strategy, the brand turned to questionnaire surveys. However, this method encountered challenges regarding timeliness and could never attain the precision or detail found in near real-time sales data directly obtained from retailers. It became evident that there was a distinct opportunity for retailers to monetize their data, not only generating revenue but also potentially improving their relationship with suppliers.
Benefits: Why to monetize your data?
There are a variety of reasons to share your data. Some briefly mentioned in the previous chapter. Broadly, we can split them into two benefit categories: internal and external.
Often, analytics and data initiatives induce substantial benefits for the organization. However, quantifying these benefits is not always straightforward. For example, the justification for investing in a data platform should demonstrate tangible, measurable outcomes to inform decision-making. Typically, the advantages of investing in a data platform involve enhanced data accuracy, improved accessibility, better decision-making, and the ability to generate more expansive reporting in less time. While all these benefits hold true, they can be challenging to quantify in monetary terms. Introducing data monetization into the discussion provides more easily measurable benefits, injecting financial considerations and making investments in a data platform more appealing.
When sharing data with third-party organization with a monetary value attached, ensuring accuracy and maintaining data in optimal condition becomes crucial. Data analysts frequently encounter challenges when an organization's data is scattered and the quality of master data is suboptimal. The act of selling data compels organizations to prioritize and enhance their data strategy and data management processes, bringing about numerous internal benefits in addition to monetary value.
Data monetization opens the door to additional revenue opportunities within an organization. The influx of increased or entirely new revenue sources empowers organizations to fortify their internal data development initiatives. As a byproduct, gaining buy-in for analytics at the management level and instilling trust in data through enhanced data management processes makes internal projects more appealing for organizations. Moreover, with a well-functioning data platform, the initial costs associated with exploring new ideas and creating proof-of-concepts are significantly reduced.
Beyond internal benefits, data monetization proves instrumental in fostering valuable connections with external stakeholders. It serves as a platform for strategic partnerships, enabling collaboration with companies, suppliers, and industry collaborators. Sharing insights and datasets facilitates mutually beneficial collaborations, fueling transformative joint ventures.
Sharing specific data with suppliers enhances collaboration, optimizing supply chain management for improved negotiation power, reduced costs, and heightened operational efficiency.
In terms of regulatory compliance, data monetization exemplifies ethical practices. Compliant data sharing not only positions a company as a responsible entity but also reinforces its reputation, building trust with external stakeholders, including regulatory bodies.
How to implement data sharing strategy
One good way to add data monetization capabilities to your data platform is to use medallion architecture to organize data in a data lakehouse logically. The aim is to incrementally and gradually improve the quality, structure, and information value of the data as it flows through each layer. Without delving extensively into the details of data lakehouse or medallion architecture, the visual representation below illustrates the positioning of data-sharing capabilities within this solution. The data-sharing layer is situated atop the gold-level data, featuring tables/views specifically crafted to provide value for the organization's stakeholders.
The design of shared data is dependent on the organization's stakeholders. For those with a limited number of stakeholders, customizing data for specific needs is a feasible effort. In such instances, tailored tables or views can be created, incorporating logic and metrics relevant to that particular stakeholder or segment of stakeholders.
Organizations dealing with a multitude of suppliers, numbering in the hundreds or thousands, face a more complex scenario. Here, data sharing is often executed at a granular level, leaving the suppliers with the possibility and responsibility to draw insights from the shared data. Suppliers typically access the same views or tables. In such cases, the paramount concern becomes ensuring robust security measures. Implementing secure views with row-level policies is often the most effective approach to upholding data integrity and confidentiality.
To conclude the topic, there are significant benefits of monetizing organizations’ data. Internal and external benefits range from better data management processes to increased revenue and improved supplier relationships.
Adding data-sharing capabilities to your data platform can be efficiently achieved through the implementation of a medallion architecture, allowing for synergies from internal data development work. Brightly is here to guide you in your data platform endeavors!