Couple of decades back, when “Big Data” was still trying to become a buzz word, my world revolved around Informatica workflows and Business Objects reports.
Massive ETL batch jobs ran overnight during off business hours. The close to real time data was “data as of yesterday”.
Over the last 20 years, I have witnessed technology shifting dramatically. We have come a long way with the age old reporting of Business Objects to building unified analytics platforms using modern technologies like the Microsoft Fabric.
While the technologies have evolved from on-premise servers to limitless cloud compute, the core challenge remains.
Having better tools does not automatically mean you have a better strategy. In fact, the ease of modern cloud platforms often leads to data chaos, governance nightmares, and skyrocketing costs if not implemented or managed well.
A modern data strategy requires a fundamental change in how people work and make decisions, beyond simply migrating from on-premise systems to cloud platforms like Azure or GCP.
Having said that, my first key element of a modern data strategy is:
- Data as a Product (DaaP) – the essential shift in the mindset of people
The greatest failure of a data strategy is not the code or the infrastructure, but the mindset of people on how they process and consume data.
Measuring success today is about the business value delivered, rather than merely completing overnight batch processes
The modern enterprise data demands that we replace the technical completion mindset with business value. The focus is shifting from projects to products.
- Shift in Identity – Data engineers no more build just pipelines. They are entire product owners who build reliable and reusable data assets that deliver measurable ROI for the business
- Decentralized ownership – Centralized IT teams are moving away from being gatekeepers of centralized data warehouses. Data ownership is moving closer to the respective business domains like the Supply chain team owns the supply chain data product.
If your data strategy is still defined by success of ETL jobs rather than downstream consumption and business impact, it is high time you change the mindset of your team.
- The unified “Lakehouse” architecture
In the traditional data warehousing architecture, it was mostly structured data stored in databases. The unstructured data mostly resided in file servers and processing them for insights was a big challenge and expensive until the emergence of big data tools like Hadoop or NoSQL databases. This created data silos even though the main motto of a traditional data warehouse was to eliminate data silos.
A modern strategy eliminates this.
Whether it is Snowflake or Databricks Lakehouse, the modern standard is a unified Lakehouse. Data can be stored at a much affordable cost and retrieval is very efficient and queried with performance and ACID compliance of a traditional data warehouse. This also enables the system to be ready for Analytics, AI & ML on the unified Lakehouse.
- AI & ML readiness – the value engine
In 2025, if your data is just able to give you insights about your last quarter’s performance, it is already obsolete.
The modern data strategy ensures that all foundational work, Governance, Architecture, and Data Quality, is ready for AI-assisted decision-making. Today, data is optimized for algorithmic use, including training complex models and large language models, while the platform itself supports advanced AI workflows:
- AI-ready infrastructure: Data pipelines now feed Feature Stores and Vector Databases, extending beyond traditional BI dashboards.
- Algorithmic optimization: Foundational data is prepared and structured to support model training, including both structured and unstructured data.
- Context for Generative AI: Technologies like Vector search provide rich context for Gen AI applications and large language models.
This pillar ensures your multi-million dollar investment in data infrastructure does not just produce reports but it produces predictive insights and powers intelligent automation.
- Data Governance – Automated & Adaptive
Traditionally, data governance relied on centralized control, with IT teams acting as strict gatekeepers. They managed the entire data warehouse, whether SAP BW, on-prem Oracle, or early cloud storage, overseeing every ETL process, defining user roles, and approving all access requests. Governance was top-down, focused primarily on compliance and risk avoidance. This approach ensured control, but it often slowed decision-making and limited the agility of business teams.
Over time, governance has evolved into a more adaptive model that balances security with accessibility. Modern platforms, whether integrated cloud services or third-party data governance tools like Collibra, Alation, or Microsoft Purview, automate routine governance tasks, reducing the manual burden on IT teams.
- Safe self-service access: Business users can access and analyze data independently, with confidence that controls and policies are enforced automatically. This empowers teams to act faster while maintaining compliance.
- Automated protection of sensitive data: Personally identifiable and protected information is detected and labeled automatically, while masking and column-level security ensure that data is safeguarded without slowing workflows.
- Real-time visibility and accountability: Data lineage and audit trails track who accessed what, when, and how, providing transparency and enabling faster troubleshooting or compliance reporting.
These changes have transformed the role of centralized IT. Teams now serve as enablers rather than enforcers, guiding users, maintaining governance, and ensuring that data remains both secure and usable. This shift allows organizations to leverage data effectively while reducing risk and operational bottlenecks.
- Data Democratization – for immediate action
I remember the days of generating pixel-perfect reports in Business Objects or waiting for an SAP BEx query to execute. The user’s interaction with data was always mediated and the latency meant they were always acting on “one-day lag data”. A modern data strategy must treat data consumption as a core utility like internet or electricity. It must be safe, readily available and intuitive to consume.
Business users are becoming tech savvy than ever before, and they want to answer their own analytical questions without writing lines of codes or opening a support ticket. This is where real time intelligence plays a key role and large vendors like Microsoft are investing heavily in RTI in tools like Fabric.
True democratization relies on two aspects primarily – simplified access (low code / no code tools like Power BI / Tableau) and unified business logic powered by a robust semantic layer which ensures single version of truth. It pushes decision-making to the edges of the organization, accelerating agility and ensuring that the data platform’s value is realized the moment a user acts.
We have progressed from the rigid batch-processing of the 1990s and 2000s to the limitless elasticity of the cloud. However, as veterans know, scalability is not just about compute power but it is about cognitive load and process flexibility. Ultimately, a truly modern data strategy scales when it empowers your people, not just your pipelines. It is the roadmap for turning collected bytes into competitive intelligence.
Antony Savari
Senior Vice President – Data & AI
Antony brings more than two decades of dedicated expertise in Information Technology and Data Analytics. His spans hands-on engineering to enterprise strategy, with deep experience across SAP Analytics and cloud-native data ecosystems. Known for building robust data cultures and guiding enterprises through AI transformation, he combines technical depth with visionary leadership to help organizations turn data into lasting business impact.