Embarking on the journey of data modernization is akin to setting sail into uncharted waters, where every decision made in the initial phases can chart the course for success or steer towards unforeseen challenges.
In Part 1 of our 2-part series, we’re diving into the data modernization journey with step-by-step advisement on the major considerations you should take as you roadmap a path to unified data, cloud-based systems, faster processes, and even pave the way for building generative AI into your ecosystem.
What Is Data Modernization?
Data modernization is the strategic process of upgrading an organization’s data infrastructure to keep pace with modern, digital-first business needs. It involves replacing legacy systems with scalable, flexible solutions that unlock real-time insights and business agility.
This journey moves organizations away from siloed, outdated systems toward cloud-based data lakes, microservices, and serverless architectures. Core elements include data integration, cleansing, warehousing, advanced analytics, cloud adoption, and governance. Together, these practices eliminate silos, improve data quality, and position data as a true strategic asset for innovation and decision-making.
Why Modernization Matters – The Cost of Staying Legacy
Staying with legacy systems is risky and costly:
1. Hidden Financial Impact: Poor data quality costs the U.S. economy trillions. Organizations lose an average of $15 million annually due to bad data, with 15% of revenue at risk and employees spending 27% of their time fixing data issues.
2. Operational Inefficiencies: Bad data slows decision-making and increases costs, leading to duplications and manual reconciliation.
3. Compliance Risks: Inaccurate data threatens compliance with regulations like GDPR and HIPAA, while data breaches harm trust.
4. Missed Opportunities: Organizations can miss up to 45% of potential leads due to poor data quality, giving competitors who modernize an advantage.
A cohesive data modernization strategy is essential for moving from reactive cleanup to proactive innovation.
Why Modernize Now?
The necessity of data modernization has never been clearer. Legacy systems hinder innovation, and poor data quality is costly, with U.S. businesses losing around $3.1 trillion annually. Gartner estimates that these issues cost the average organization $15 million each year, with employees spending up to 27% of their time correcting errors.
With over three-quarters of organizations using AI in some capacity, modernizing data platforms is crucial for reliable AI and advanced analytics. McKinsey’s 2025 survey indicates that 21% of organizations have redesigned workflows due to generative AI, while 27% review all AI-generated outputs for quality. Without trusted, unified data, these investments struggle to deliver value.
Key Steps in the Data Modernization Journey
Below, we outline the critical stages you should follow to make your data modernization initiative a success.
Assessment and Planning
In the initial phase of a data modernization journey, organizations typically conduct an in-depth assessment and planning process. This involves scrutinizing the current state of their data infrastructure, including databases, data warehouses, and analytics tools, to identify any inefficiencies, limitations, or areas for improvement.
Through this assessment, organizations gain a comprehensive understanding of their data ecosystem and pinpoint specific pain points and bottlenecks that need to be addressed. Subsequently, they define clear goals and objectives for their data modernization efforts, outlining the desired outcomes and milestones. This planning phase lays the groundwork for the entire modernization process, providing a roadmap that guides subsequent steps and ensures alignment with overarching business objectives.
How much is bad data costing your business? Learn how to tell and what do about it.
Data Governance and Compliance
Following the assessment and planning phase, organizations embark on establishing robust data governance and compliance frameworks as a pivotal step in their data modernization journey. This involves defining and implementing policies, procedures, and controls to ensure the quality, security, and compliance of their data assets. Organizations identify regulatory requirements such as GDPR, CCPA, HIPAA, etc., and design governance structures to meet these standards effectively. Additionally, they deploy data management tools and technologies to enforce governance policies and maintain data lineage, ensuring transparency and accountability across the data lifecycle. By prioritizing data governance and compliance early in the modernization process, organizations lay a solid foundation for managing and safeguarding their data assets responsibly, thereby mitigating risks and building trust with stakeholders.
Data Integration and Consolidation
After establishing robust data governance and compliance frameworks, organizations proceed to integrate and consolidate their disparate data sources, marking a crucial step in their data modernization journey. This entails harmonizing data from various internal and external sources to create a unified, comprehensive view. Leveraging techniques such as Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT), organizations ingest, transform, and load data into centralized repositories like data warehouses or data lakes like Snowflake.
By consolidating data, organizations streamline access and analysis, enabling more efficient decision-making and insights generation. This consolidation effort not only enhances data accessibility but also facilitates scalability and agility, setting the stage for further modernization initiatives.
Find out how Staples reduced storage and server costs by 19% and improved system performance eight-fold with Mastek’s expertise.
Cloud Migration
Following the integration and consolidation of data sources, organizations often embark on the journey of migrating their data infrastructure to the cloud, representing a pivotal step in their data modernization efforts. This involves evaluating the benefits of cloud migration, such as scalability, flexibility, and cost-effectiveness, and selecting a suitable cloud provider based on factors like data security, performance, and compatibility with existing systems.
With a well-defined migration plan in place, organizations execute the migration process, employing strategies such as rehosting, re-platforming, or refactoring to move data and workloads to the cloud seamlessly. By migrating to the cloud, organizations gain access to a wide array of cloud-based services and technologies, enabling them to enhance agility, reduce operational overhead, and accelerate innovation in data management and analytics.
Learn the five core phases of data migration with our whitepaper, Five Stages of a Headache Free Data Migration.
About Basecamp
Basecamp is a comprehensive solution offered by Mastek, designed to assist enterprises in their data modernization journey. It facilitates the construction of a roadmap for data infrastructure and the application of best practices tailored to each organization's unique technology landscape. With Mastek's dedicated data modernization team, customers can achieve ROI in as little as four hours, regardless of their current stage in the modernization process.
Basecamp supports various transitions, such as moving from on-premise to cloud or hybrid models, unifying disparate systems, integrating cloud data storage, and preparing for AI implementation. By leveraging Basecamp, businesses can quickly define the optimal path forward for their data infrastructure, ensuring scalability and flexibility to meet evolving business demands.
How a Basecamp Session Works
Your Basecamp whiteboarding session begins with a half-day workshop based on the Data Adoption Framework to gauge your organization’s Data readiness. We work with you and your key stakeholders to learn about your business, objectives, operations and infrastructure and to identify gaps in your data roadmap (or build it new). This session is a great way to build consensus around your data competence, capability, and commitment.
After the workshop, we analyze the collected data and schedule a one-hour feedback session where we provide you with a full report of your Data Roadmap, projected total cost of ownership (TCO) and recommendations for the next steps.
Who Should Attend?
This is by no means an exhaustive list, but if you’re wondering who should participate in the Basecamp session, we recommend the following stakeholders:
- CDO
- CIO
- Data Engineering Lead
- Analytics Lead
- Head of Infrastructure
- Business user
The benefits of Basecamp are manifold for customers seeking to streamline their data infrastructure. Mastek's experienced consultants guide clients through a proven discovery and design process, identifying the most effective strategies and providing recommendations for implementation. Basecamp offers a range of services, including a review of current state architecture and technology, clarification of desired future states, exploration of best practices and relevant use cases, and high-level recommendations to address current challenges.
Ultimately, customers receive valuable deliverables such as a high-level roadmap and clear recommendations for the next steps. With Mastek's expertise in data-driven decision-making and a focus on scalability and success, Basecamp delivers accelerated time-to-market, deep experience, proprietary tools and methodologies, agile and scalable infrastructure, and high value through an on-shore/off-shore model, ensuring quality services at an affordable investment.
Ready to get started? Contact us today for a demo and discovery call.
