Technology modernization

While there is no silver bullet, a well-defined futuristic architecture that takes into consideration not only the existing modernization use cases but also keeps into radar the technology innovation, hybrid cloud capability, core architecture principles (shown above), the end-user experience across personas (including developer, data producer, a data consumer, data governance, data platform owner, etc.), encourages self-care capability and most importantly, learns intelligently from itself for continuous improvement is the right way forward.

Outcome driven mindset

The ROI for investments in data and AI has been a concern for various large and small organizations forever. A well-defined business outcome and prioritization framework helps organizations enable incremental value delivery by mapping the business value chain & objectives to business drivers that align with underlying business decisions. These business decisions help identify data success contributors, enabling a holistic framework and roadmap for an outcome-driven data & AI mindset.

People, process, and organization

When an enterprise becomes a data-centric organization, it is imperative that the “jobs to be done” framework is driven using an outcome-driven mindset, thereby redefining people roles, processes, and organization structure. Every employee in the organization also becomes a data citizen, understanding the economics of data and constantly upskilling, thereby contributing consciously towards working, culture, and business outcomes. Process evolution involves self-care experience across personas, seamless data search and sharing processes, automated processes across the data lifecycle, processes to harness metadata and create knowledge graphs for contextual search and data correlation, data policy extraction and enforcement processes, and many more. Data quality and security-related processes deserve specific mention since TRUST in data is the cornerstone of such a data-centric organization.

Governance

With AI taking center stage, governance becomes pivotal for data and AI. As the democratization of data enables businesses to make quicker decisions, data governance has become more federated, thereby ensuring elements of governance as part of “jobs to be done” across data personas. An example of data governance is external data sharing, where data producers, consumers, and platform owners comprehend, enable, and enforce appropriate policies and contracts for a trusted data-sharing process. AI governance focuses on the "jobs to be done" by AI, thereby appropriately understanding the potential drifts, hallucinations, and underlying AI assurance needs.

Integrated support

Mature data-centric organizations have begun evolving towards an X-Ops model where their data and AI ecosystems are highly automated. These models incorporate principles around DataOps, MLOps, AIOps, data observability, BizOps, etc., thereby considerably reducing conventional support costs. A well-defined intelligent X-Ops framework ensures that most investments are aimed towards business outcomes and innovation rather than manual support for existing data and AI operations.

In conclusion, many contributing factors are needed for a robust data organization, such as instilling a forward-thinking operational model complemented by a modern data ecosystem and fostering a culture of data adoption and effective change management.

Essential requirements of a healthy data organization:

A futuristic operating model that considers all aspects of technology, innovation, people, process, business outcome mindset, governance, and integrated support.
A modern data ecosystem – Self-serving, adaptive, API-driven, federated governance, AI-enabled.
Efficient data culture adoption and change management – Smooth transitioning from data knowledge to data literacy, fluency across the organization, and ability to adapt to new working methods.
A core-flex resourcing model – The ability to handle evolving business needs and prioritization.
Innovation and AI at the core – Outside-in view, experimentation, AI first, generative AI, and enablement of other innovation themes around technology, domain, and industry.
Agile implementation – Incremental value delivery and business outcomes.
Data and AI academy – Enabling talent with upskilling and cross-skilling opportunities and creation of a Center of Excellence (CoE)
Key measurements of success and metrics – Business outcome-driven roadmap, prioritization, and well-defined OKRs.

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