The role of data products in data-driven decision making
Nathi Dube, Director, PBT Innovation at PBT Group
In today’s competitive business landscape, the ability to make data-driven decisions offers businesses a significant advantage. Insights gleaned from high-quality, well-curated data can drive smarter strategies, improved customer experiences, and long-term customer loyalty. Yet, simply having data isn’t enough; businesses need data products. They need purpose-built tools that transform raw data into actionable insights, accessible when and where they’re needed.
Understanding how businesses can start building data products, which data architectures best support them, and how they empower a culture of data-driven decision making become vital for success.
Building data products
Creating effective data products begins with more than just technology. It comes down to changing the business mindset and approach.
1. Establish a data-driven culture: A data-driven culture is essential to building data products that drive meaningful insights. This culture should encourage treating data as a core part of the business process, not a secondary output. Every new system feature or project should prompt data owners to consider key questions, such as: What insights do we aim to gain? Are we capturing all necessary data elements for these insights? Is data timely and analysable? Embedding these considerations helps ensure that data is not an afterthought but a primary asset.
2. Implementing DataOps for faster value delivery: Borrowing from DevOps practices, DataOps emphasises a collaborative approach that shortens time-to-value for data projects. By enabling continuous development, feedback loops, and stakeholder collaboration, DataOps helps deliver data products efficiently, ensuring they align with evolving business needs. Additionally, DataOps supports rigorous governance, balancing agility with data security and quality.
3. Domain-centric teams and ownership: Effective data product creation also depends on well-structured domain teams. These teams should be self-contained, with cross-functional skills that allow them to handle their domain’s data needs autonomously. Each team is responsible for developing, managing, and continuously improving its data products. Within each domain, a designated product owner oversees the data product lifecycle, ensuring that it evolves to meet both domain-specific analytical needs and the broader business’ requirements.
4. Data platform teams for architecture and innovation: While domain teams handle data products, data platform teams play a crucial role in setting up the architectural patterns that support these products. Whether real-time analytics or batch processing is needed, data platform teams must create an environment that empowers domain teams to innovate without technical constraints. By providing a flexible, scalable platform, these teams enable the efficient development and deployment of data products tailored to each business domain.
Supporting data architectures
Selecting the right data architecture can significantly impact the success of data products and their ability to drive value across the business. Two prominent architectures, Data Mesh and Data Fabric, offer distinct approaches to managing and using data at scale.
Data Fabric is a centralised architecture that automates data integration, bringing data from various sources into a unified space. Its centralisation simplifies data access and control, particularly useful for organisations prioritising data consolidation.
For its part, a Data Mesh embraces a decentralised, domain-oriented approach, placing data ownership in the hands of domain teams who are closest to the data and understand it best. Here, domain teams create and manage their own data products, which can be shared across the business or used within the domain. This architecture supports a collaborative data culture by giving each business unit autonomy while upholding governance standards.
With Data Mesh, data products evolve alongside changing business needs, encouraging continuous improvement and experimentation. The decentralised model empowers teams to take ownership of their data, fostering an environment of accountability and innovation that supports strategic decision making.
Driving a data-driven decision making culture
Data products can be transformative tools that not only make data accessible but also actively encourage data-driven decisions at every level of the business.
1. Enhancing decision quality and speed: By building data products that meet specific business needs, domain teams can deliver actionable insights on demand. When a business decision needs to be made, domain-specific data products provide reliable, validated insights, enabling timely, informed choices. This ensures that decisions are not just backed by data but are derived from a nuanced understanding of the data, handled and analysed by those closest to it.
2. Creating efficiencies through cross-domain collaboration: The shared use of data products across domains fosters efficiencies and eliminates redundancy. Teams can rely on vetted data from other domains, enabling them to make informed decisions without reinventing the wheel. This transparency and shared access reduce silos, allowing each business unit to leverage trusted insights from across the business.
3. Iterative improvements for continuous insight enhancement: Data products are not static; they are refined and improved through iterative development, ensuring they remain relevant as business needs evolve. Regular updates and adjustments improve the quality and accuracy of insights over time, building confidence among data users and encouraging wider adoption. As these products mature, they reinforce the business’ commitment to data-driven thinking, gradually embedding this mindset across all departments.
4. Empowering domain teams with accountability and authority: Since domain teams are directly responsible for creating and managing data products, they are empowered to innovate and experiment within their areas of expertise. This level of ownership reinforces accountability and encourages proactive data stewardship, aligning with a broader business goal of making data-driven decisions.
A shift to data product thinking, supported by an enabling architecture like Data Mesh, equips businesses to respond faster to market changes and make smarter, data-driven decisions. By fostering a data-centric culture, implementing effective architectures, and empowering domain teams with autonomy, businesses can create a framework where every decision is informed by trusted data products.
