From optimizing promotions to predicting consumer trends, CPG companies have to be nimble and data-driven to compete. However, efficiently leveraging real-time and historical data is a difficult task for many CPG companies. The core challenge today isn’t a lack of data, but rather its effective utilization with the right data infrastructure. The key is to know what data organizations have; clean the data and make them available to user groups in an easily accessible, readable, and highly secure form. They need to adopt the latest technologies and tools that facilitate broader, more impactful data usage and access across the organization.
In The State of Analytics Engineering Report 2023, 42% of analytics leaders say that their top concern is “Data isn’t where business users need it.”
They must explore avenues to democratize data and generate insights while breaking down existing data silos between teams, systems, and processes. Data discoverability issues, governance complexity, and consistency in data further obstruct the self-service analytics journey for business users. To democratize insights faster, the concept of a data marketplace serves as a promising solution.
What is a data marketplace?
A data marketplace is a transformative solution that facilitates the seamless creation, consumption, and sharing of data products and assets across an organization. This centralized hub enables business teams to easily access, analyze, and share quality datasets through self-service BI tools and platforms that democratize data.
By empowering business users to generate insights without relying on IT, data marketplaces help CPG companies bypass typical analytics bottlenecks. The self-service analytics portal helps break down existing silos and obstacles that hindered efficiency and cross-functional collaboration.
Who are the data consumers in an enterprise data marketplace
Principles and architecture of a data marketplace
Here are the key technical features of a data marketplace that play an important role in building the ecosystem for accessing and transforming data into insights.
FAIR Principles
The data marketplace adheres to FAIR principles — ensuring data is Findable, Accessible, Interoperable, and Reusable. Findability is optimized through robust search functionalities, accessibility is enhanced with secure access controls, interoperability is achieved through standardized formats, and reusability is facilitated by clear documentation and metadata.
Medallion architecture
- Raw data repository: The bronze layer of a medallion architecture incorporates a centralized Data Dump — a repository for raw data. This centralized storage facilitates efficient data handling and retrieval, serving as the foundation for subsequent processes.
- Data foundation: The Silver layer of the architecture includes processes for data standardization, table linings, and the integration of analytics applications and model plugs. This ensures standardized data, facilitating consistency and easing the integration of analytics tools and machine learning models seamlessly into the marketplace.
- Data filters: The gold layer helps strategically implement filters to enhance discoverability, allowing users to efficiently retrieve relevant data. These filters serve as powerful tools that streamline the exploration and selection of data within the marketplace.
Data products
- Integrated: A sales analytics dashboard that seamlessly integrates data on customer interactions and inventory levels. It ensures that users can derive insights from multiple sources without encountering silos.
- Cross-domain: A cross-domain data product that amalgamates financial data with customer satisfaction metrics and supply chain analytics. This approach allows organizations to synthesize insights from different domains, enabling a holistic perspective.
How exactly does a data marketplace help business teams?
By facilitating seamless connections between data producers and consumers, the marketplace provides tailored benefits that align with the unique needs of organizations seeking to extract maximum value from their data assets.
- Reduced time to market: The data marketplace acts as a catalyst to speed up the journey from raw data to actionable insights. This reduction in time to market enhances organizational agility, enabling swift adaptation to ever-evolving market dynamics and maintaining a competitive edge.
- Rapid reporting: By accelerating the reporting process, a data marketplace provides business users with quick access to solutions like real-time marketing measurement. This enables faster decision-making, enhancing the organization’s ability to respond promptly to emerging opportunities or address potential challenges.
- Seamless integration of ML models: A distinctive feature of the data marketplace lies in its plug-and-play model integration which simplifies the adoption of machine learning models. By leveraging ML models, CPG companies can gain predictive capabilities for foresight into future trends and outcomes.
Conclusion
In the CPG industry, the true power lies in the accessibility and actionable nature of data. The shift toward effective data utilization is imperative and the focus now is on democratizing data insights, eradicating existing silos, and overcoming discoverability issues. A tailored data marketplace emerges as a beacon of promise in this pursuit. By providing intuitive self-service analytics and a centralized platform for cross-functional collaboration, data marketplaces offer the capabilities for CPG companies to truly harness the potential of their data assets and drive better business outcomes.
At Sigmoid, we have deep experience in leveraging data, creating infrastructures; building marketplaces that enable amazing democratization and generate killer insights.
Create Powerful Data Products with Data Mesh architecture
About the author
Shankar Viswanathan is the Chief Commercial Officer at Sigmoid. He brings over three decades of expertise in building foundational capabilities for CPGs across various business domains such as sales, marketing, media, supply chain, IT, analytics, and insights. He has a proven track record of successfully leading end-to-end enterprise transformations, resulting in strong and sustained financial performance in diverse, developed, and emerging markets. At Sigmoid, Shankar is dedicated to empowering clients in harnessing the power of data analytics and AI for effective business transformation.
