In the fast-paced market, firms that implement a low-cost, agile data analytics operating model will come out on top. Hereโs how:
1. Implement a Reactive and Adaptive Data Strategy
To stay ahead, businesses need a data strategy that directly drives the business strategy. Concepts like the OODA loop (Observe, Orient, Decide, Act) can help create a responsive and adaptive approach to market changes.
2. Direct Business-Facing Operating Model
Business SMEs should have the capability to quickly define, prototype, and test data products with customers and users, utilizing UX design principles and real data. This hands-on approach ensures that data products are immediately relevant and useful.
3. Quick Creation of Business-Facing Data Products
The focus should be on data infrastructure and technology that enables the rapid creation of data products solving end-to-end business use-cases, not just intermediate data tasks. This involves:
- Data Curation over Data Transformation: Reduce data engineering, data modeling, and data architecture efforts by up to 90% by focusing on data curation.
- Containerized Runtimes: Create data products as discrete, federated components that can be managed and deployed independently across different domains.
- Composable Tech Stacks: Utilize data product templates with containerized tech stacks that integrate seamlessly across an existing complex data estate.
4. Data Product Marketplace
Develop a data product marketplace based on data mesh and data fabric concepts, featuring a searchable catalog of data products with integrated UX and data storytelling capabilities. This marketplace enables:
- Quick access to curated data sets
- ML models and usage policies
- Delivery capabilities
Eliminating the Need for Expensive Transformation Programs
Businesses can implement these strategies without spending millions on large transformation programs. The goal is to create data products without reorganizing the entire business. In 2023, firms cannot afford massive infrastructure outlays without clear links to driving business value. This agile, cost-effective approach ensures that resources are used efficiently and effectively.
Key Takeaways
- Curation vs. Transformation: Emphasize data curation to reduce complexity and effort.
- Business SMEs vs. IT: Empower business SMEs to lead data product creation.
- Delivered Through a Marketplace: Use a data product marketplace for easy access and deployment.
Addressing Practical Considerations
While the principles are clear, practical implementation may require adjustments based on the size and structure of the organization. For larger firms, a "Citizen Layer" might be needed where trained individuals act as intermediaries between business SMEs and IT to ensure smooth operations and scalability.
Industrialized Capability for Data Product Deployment
Itโs crucial to have an industrialized capability to create and deploy data products once they have been designed and prototyped. This involves a streamlined process for moving from prototype to production, ensuring quick iterations and minimal downtime.
Investing in the Right Technology
Organizations need to rethink how they use existing technologies and invest in new capabilities that support agile data product development. The focus should be on modular, scalable solutions that can adapt to changing business needs without massive infrastructure overhauls.
Conclusion
The race to move with the market in 2023 will be won by those who implement a low-cost, agile data analytics operating model. By focusing on reactive data strategies, empowering business SMEs, leveraging modern data infrastructure, and utilizing a data product marketplace, firms can achieve true data-driven success without breaking the bank. This approach ensures that businesses are equipped to navigate the ever-changing market landscape with agility and precision.