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Govinda, Senior Manager at Cognizant , has 14+ years of expertise in SAP & Non-SAP Data Analytics, delivering innovative BI solutions. getty In a rapidly evolving digital world, data has become the cornerstone of enterprise agility and innovation. Yet, merely possessing data is not enough.
For organizations to unlock true business value, they must unify disparate systems and apply artificial intelligence (AI) in meaningful, actionable ways. In this article, I'll explore the transformative power of unified data and AI and outline practical insights for organizations considering such technologies. Unified data refers to the strategic integration of all data types—structured, semi-structured and unstructured—across cloud, on-premise and hybrid systems into a single, accessible and governed architecture.
This centralization enables organizations to achieve a consistent, comprehensive understanding of their operations and customers. AI brings this unified data to life by: • Identifying patterns and anomalies to drive proactive decision-making • Enabling predictions and intelligent recommendations • Enhancing automation through natural language understanding and machine learning models From what I've seen in the industry, the overwhelming majority of enterprise data remains unstructured and underutilized. This underscores the critical role of a unified data layer as the foundation for AI success.
A range of companies are setting the pace in unified data and AI, including SAP, which recently introduced its Business Data Cloud ; Snowflake, which offers robust multicloud data sharing capabilities and simplified scaling; and Databricks , which promotes a lakehouse model that fuses data engineering with AI workload optimization. Modern businesses generate enormous volumes of data, yet much of it remains siloed within disconnected systems. These silos hinder agility, undermine collaboration and limit the impact of AI investments.
Unified data platforms address these gaps by: • Offering consistent views of business data in real time • Simplifying compliance and governance across regions and industries • Powering analytics and AI without the burden of data duplication or manual integration Based on my time in the industry, organizations adopting unified data platforms typically experience: • Faster insights through federated analytics and contextual AI • Stronger compliance postures thanks to embedded governance • Operational efficiencies from eliminating redundant pipelines and siloed tools • Strategic agility enabled by multicloud, scalable architectures When vetting software providers or partners, you should keep an eye out for the following benefits: • Seamless ERP And CRM Integration: This is particularly valuable for businesses using complex enterprise systems like SAP or Salesforce. • AI Built Into The Platform: This reduces reliance on third-party tools. • End-To-End Governance Frameworks: Be sure this has built-in support for data lineage, access control and regulation.
• Flexible Architecture: They should offer deployment across hybrid, on-premise and multicloud environments. On the other hand, there are also some red flags to watch out for and steer clear of. • Monolithic platforms with limited interoperability • Dependency on manual ETL processes or custom development for basic use cases • Analytics tools that lack context-awareness, making AI outputs less reliable • Afterthought governance features that complicate regulatory compliance I've spent a lot of time learning how to optimally use data analytics and other tech solutions.
The following are my top tips for implementation: • Align Data Strategy With Business Outcomes: Do not start with technology; start with business needs. Define high-value use cases such as churn prediction or supply chain optimization to guide platform adoption. • Choose Platforms That Support Federated Access: Minimize duplication by selecting solutions that allow for direct, governed access to data across sources without moving it.
• Treat Governance As A Core Pillar, Not A Plug-In: Look for platforms with compliance baked into data operations, not retrofitted through external tools. • Empower Users Through Accessible Tools: Support self-service analytics using intuitive platforms, enabling users across departments to act on insights. • Build A Cross-Functional Governance Council: Bring together stakeholders from IT, legal, compliance and business units to develop a shared data strategy and ownership model.
In guiding enterprise clients through their data transformations, particularly in SAP environments, I have seen that success hinges on: • Starting small with focused pilots that prove value and gain executive support • Leveraging pre-built data models and AI accelerators to shorten time to value • Prioritizing change management and user enablement just as much as technical implementation These approaches ensure that the platform investment delivers long-term strategic returns, not just short-term wins. The transition to unified, AI-ready data platforms is well underway. No matter your platform, the goal remains the same: to transform fragmented data into trusted intelligence.
The technology exists, but the mindset must follow. Enterprises that focus on aligning architecture, strategy and governance are best positioned to thrive in a world where data is both the fuel and the engine of innovation. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives.
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