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Artificial Intelligence is rapidly becoming a strategic priority for organisations across industries. However, successful AI adoption depends on far more than deploying advanced models or investing in generative AI tools. Fragmented data, inconsistent definitions, weak governance, security gaps, and undocumented business processes can prevent enterprises from scaling AI effectively.
In this episode of the Analytics Insight Podcast, Priya Diyalani speaks with Amit Chandak, Chief Analytics Officer at Kanerika, about what it takes to become an AI-ready organisation. He explains why trusted data is the foundation of reliable AI, how enterprises can overcome data silos, and why governance, security, master data management, and process documentation are essential for long-term AI transformation. Here are the key excerpts:
Kanerika is a data analytics organisation focused on helping businesses extract actionable insights from their data. Over the past decade, the company has worked with organisations to make their operations increasingly data-driven through predictive and prescriptive analytics.
Our company works with technology ecosystems including Microsoft, Databricks, Snowflake, and cloud platforms. It also develops internal tools designed to help organisations transform and analyse data using AI.
Our company also specialises in data migration, helping enterprises move from legacy infrastructure to modern platforms such as Microsoft Fabric, Power BI, Databricks, Snowflake, and dbt. Its objective is to accelerate these transformations so that organisations can modernise their data infrastructure in weeks or months rather than years.
AI readiness begins long before an organisation selects an AI model or deploys a generative AI application. The foundation is high-quality, organised, and accessible data. However, data alone is not enough. Organisations also need to document their business processes. AI can generate forecasts and recommendations, but businesses need clearly defined processes to determine what actions should follow those insights.
An AI-ready organisation therefore needs reliable data, documented processes, governance, lineage, and consistent business information. Without these foundations, even sophisticated AI systems can struggle to produce reliable outcomes.
Master data management is another critical requirement. The same product, customer, or business entity may have different identifiers across systems, making cross-platform analysis difficult. Organisations must establish consistent definitions and relationships between their data assets.
Metadata and standardised KPI definitions are equally important. Different departments may interpret terms such as sales or revenue differently. Establishing common business definitions helps ensure that AI systems operate using consistent information.
A unified data foundation should bring information from different systems together, clean and standardise it, establish common business definitions, and apply appropriate governance. This creates a consistent environment for analytics and AI.
Centralised data platforms can also help organisations understand customer behaviour more comprehensively. Website visits, product interactions, purchases, and other customer signals can be connected to identify opportunities that may otherwise remain hidden.
Once the data is clean, connected, and governed, AI can analyse these signals and identify potential business opportunities. Building a connected, centralised, clean, and governed data environment is therefore a critical starting point for enterprise AI.
Enterprises do not need to choose between AI experimentation and governance. They need environments that support experimentation while embedding security and accountability into the process. I believe these rules can determine who can access specific information, at what level of detail, and whether particular data needs to be masked.
Security can operate at multiple levels, including row-level, entity-level, and column-level controls. Personal information can be protected through data masking, while specific users can be prevented from accessing sensitive information altogether.
Traditional approaches often focused on restricting developer access to sensitive information. Modern AI environments have changed this model because business users can increasingly analyse data, build models, and develop AI applications themselves.
Modern data platforms such as Microsoft Fabric and Databricks are increasingly integrating analytics, AI, and governance capabilities into unified environments. Such platforms can help organisations apply centralised security and governance controls across different data consumption scenarios. The aim is to ensure that AI transformation does not introduce new risks through unauthorised access or inappropriate use of enterprise data.
For organisations beginning their AI transformation, three priorities stand out.
First, businesses should build a trusted data foundation by integrating fragmented data, improving quality, implementing master data management, and establishing consistent business definitions and data lineage.
Second, they should embed governance and security from the beginning. Access controls, data masking, privacy protections, and role-based permissions should be incorporated across every AI and analytics endpoint. The central lesson is that AI transformation is not simply a technology implementation. It is an enterprise-wide journey that requires trusted data, strong governance, documented processes, secure infrastructure, and organisational readiness.
Listen to the full discussion on the Analytics Insight Podcast.
