Azure for AI-Ready Data
Oakwood’s roadmap to successful AI readiness.
As organizations race to harness the power of artificial intelligence (AI), we’ve found that one reality remains clear: the success of AI initiatives is fundamentally tied to the quality, accessibility, and governance of organizational data. At Oakwood, we believe that without a unified, AI-ready data estate, even the most advanced AI algorithms can fall short of delivering actionable insights. Today, we’ll take a closer look into how we typically work with organizations to help them prepare their data for AI using Microsoft Azure’s robust suite of tools and services. By outlining a practical roadmap to data readiness, we aim to provide technical guidance and real-world scenarios to ensure your organization is equipped to maximize AI’s potential.
As we work with clients, the first step in this journey is to assess the organizations current data estate. Before we dive into modernization efforts, it’s essential that our team understands the existing landscape. We must identify data silos, where data is stored, and how fragmented it might be across various departments. For example, a retail company might discover that their customer data resides in multiple sources, such as an on-premises CRM system, spreadsheets maintained by individual sales teams, and a third-party e-commerce platform. Furthermore, evaluating data quality is equally critical. This may involve assessing whether data is complete, up-to-date, and free of inconsistencies—for instance, ensuring that customer records don’t have duplicate entries or conflicting information. Lastly, we believe that assessing data governance practices is a must. This typically includes reviewing whether policies for data classification, security, and compliance are already in place. It’s here that Azure tooling proves to be a valuable asset. Microsoft Purview, for example, provides a unified data governance solution that scans, catalogs, and classifies data across on-premises, multi-cloud, and SaaS environments. Purview even has the ability to classify customer Personally Identifiable Information (PII) in a dataset while mapping its lineage to ensure compliance with GDPR and other regulations.
Once the current state is assessed, the next step we often take is the consolidation and modernization of the entire data estate. AI thrives on accessible and scalable data platforms, which makes the integration of fragmented data sources into a modern, cloud-based architecture critical. Migrating data to Azure—whether structured or unstructured—is a key action. For example, when working with a financial services company we might implement Azure Data Factory to orchestrate data migration from legacy systems like Oracle databases to Azure Data Lake Storage, where raw data can be stored in a scalable and cost-effective manner. For organizations dealing with real-time data, Azure Event Hubs and Azure Stream Analytics enable ingestion and processing of data streams on the fly. A logistics company could use these tools to process live GPS data from their fleet, allowing for real-time route optimization. Additionally, establishing a data warehouse using Azure Synapse Analytics creates an integrated service for combining big data and data warehousing capabilities. A healthcare provider, for instance, could centralize patient records and analytics in Azure Synapse to support both operational reporting and predictive analytics. Azure Database Migration Service simplifies the process for lift-and-shift migrations, such as migrating an entire SQL Server database to Azure SQL Database or transitioning to a globally distributed database like Azure Cosmos DB for applications with high availability requirements.
With a consolidated data estate, implementing robust governance is our next priority. Effective governance ensures that AI models are built on reliable and compliant data. Microsoft Azure tools play a pivotal role in this process. Microsoft Purview extends its capabilities by integrating with Power BI, enabling data classification and sensitivity labeling. For instance, a pharmaceutical company could enforce sensitivity labels on proprietary research data to ensure only authorized personnel have access. Role-based access control (RBAC) through Microsoft Entra ID (formerly Azure Active Directory) secures sensitive data and manages user permissions effectively. A manufacturing firm might leverage Entra ID to ensure that only specific engineers can access sensitive design schematics stored in Azure Blob Storage. To protect data during migration and storage, Microsoft Defender for Cloud provides advanced security measures, such as detecting potential vulnerabilities in storage accounts or virtual machines. For example, an e-commerce platform migrating customer purchase histories to Azure could rely on Defender for Cloud to identify and mitigate security risks during the transition.
With governance in place, we then work with organizations to optimize their data estate for AI workloads. Preparing data for machine learning models and ensuring scalability are critical steps in this phase. Tools such as Azure Databricks allow for the creation of feature stores, enabling the reuse of machine learning features across projects. A telecommunications company might use Azure Databricks to standardize and store features such as customer churn indicators for multiple predictive models. Raw, unstructured data can be stored in Azure Data Lake Storage Gen2, which supports scalable analytics and machine learning workflows. For instance, a media company could use Azure Data Lake to store video and metadata for AI-driven content recommendation systems. To optimize query performance, Azure Synapse’s Serverless SQL Pools offer ad-hoc querying capabilities without the need to pre-provision infrastructure. A marketing agency might leverage this capability to analyze large campaign datasets on demand, identifying trends and optimizing ad spend. Azure Machine Learning orchestrates end-to-end workflows, from data preparation to model deployment, while Azure Cognitive Services provides pre-trained AI models for text, image, and speech analysis. For example, a global retailer could use Azure Cognitive Services to build a chatbot that understands multiple languages and provides customer support.
The final step in Oakwood’s engagement roadmap involves monitoring, iterating, and scaling the organization’s data estate. This journey to data readiness does not end with deployment. Ongoing monitoring and optimization ensure that AI initiatives continue to deliver value over time. Azure Monitor tracks the performance and health of data pipelines, storage solutions, and AI models. For example, a financial institution might use Azure Monitor to ensure their fraud detection models are running optimally, while Azure Log Analytics centralizes and analyzes logs to troubleshoot and improve system performance. Tools like Power BI can continuously visualize and analyze data to ensure that insights remain relevant and actionable. For instance, a retail chain could use Power BI to track sales trends across regions and adjust inventory levels in real time based on predictive analytics.
To summarize, preparing your data for AI is no small feat, but the experienced data engineers at Oakwood – comgined with Microsoft Azure’s comprehensive suite of tools makes it achievable. By following this trusted roadmap, we ensure our client’s data estate is unified, governed, and optimized to unlock the full potential of AI. From assessing the current state to consolidating, governing, optimizing, and monitoring your data, each step builds on the previous to create an AI-ready foundation.
If your organization is ready to take the next step in its AI journey, Oakwood’s experts can help. With deep expertise in data strategy and implementation, Oakwood is here to guide you toward an AI-ready future. Contact us today to get started!
