In our recent discussions with an organization, we discovered they had vastly overspent on their transformation budgets. This oversight only came to light well after the fact, leading to frantic cost-cutting measures, including canceling all their Management Information (MI) workโwork critical for building base-level prescriptive metrics that measure essential factors like budget and project overspend. This situation underscores a fundamental challenge businesses face in becoming truly data-driven: cultural business change.
Data and analytics are more than just technological endeavors; they are integral to business change and product management. They either help make or save money for an organization. Ignoring this critical aspect is perilous. Genuine data product management, supported and driven by the business at the highest levels, is essential to identify valuable use cases and create end-to-end products that business users and customers will use and pay for.
To effectively leverage data and analytics for driving business success, it is crucial to focus on business-facing data products. These products should not only aid in decision-making but also ensure basic aspects like cost management are efficiently handled. However, achieving this requires more than just implementing data technology; it necessitates a comprehensive change capability.
This change capability encompasses more than just data mesh or data fabric. It involves an agile, OODA-style (Observe, Orient, Decide, Act) customer/user feedback process, coupled with good design and user/customer-driven UX. All these elements must be viewed as part of an end-to-end process that drives company strategy at the business level. Focusing on any of these aspects in isolation will not suffice in making a significant impact.
Key Recommendations for Effective Data-Driven Transformation:
- Immediate Implementation: Start by addressing genuine business problems with clear value. Use LLMs to support or solve these issues and gain practical insights beyond the hype.
- Wider Market Evaluation: After initial implementation, evaluate how LLMs can disrupt and enhance the business and market generally. Identify broader use cases and opportunities.
- Operational Model Review: Assess how the operational model may need to change to fully leverage the potential of GenAI and AI in general. Ensure the organization is prepared for the necessary transformation.
Before embarking on extensive projects involving generative AI and LLMs, it is wise to first consider how these technologies will drive business revenue and organizational efficiency. Start by identifying the problems and potential opportunities, then proceed accordingly.
Conclusion
The journey to becoming a truly data-driven organization requires more than just adopting new technologies; it involves a fundamental shift in culture and approach. Ensuring that data and analytics are integral to business strategy, driven by genuine data product management, and supported by comprehensive change capabilities is key to success.
Call to Action
If your organization needs guidance on this journey, we at Dataception are here to help. Reach out to us to explore how we can support your data-driven transformation efforts. Together, we can drive meaningful change and unlock new opportunities for growth and efficiency.
Contact us at info@dataception.com to get started.