
What is Data Modernization? Explaining Its Importance to Your Organization
By Thayer Tate
Your organization’s data is a goldmine for driving business forward, but are you properly utilizing it? For many organizations, especially those looking to grow, legacy data systems can stand in the way. Siloed information, outdated databases, and manual reporting processes often prevent teams from making timely, informed decisions.
The good news is that your organization has plenty of options to push beyond legacy data systems. Through data modernization, you can transform your outdated data environments into modern, scalable infrastructures that leverage cloud platforms, real-time pipelines, and advanced analytics.
So, how can you shift your organization’s data modernization strategy? In this blog, we’ll discuss the key components of a modern data architecture, including cloud storage and data lakes, and how to decide which path is best for your business’s unique data needs and challenges.
Why is Data Modernization Important?
The speed of today’s business world is constantly accelerating. While legacy systems were defined by the innovation they brought to data management, many simply can’t keep up with the current pace. With an estimated 80B zettabytes of data projected to be generated in 2025, your organization must meet the demands for scalability, agility, and rapid analytics.
Data modernization can allow your team to:
- Eliminate data silos that may prevent holistic analytics.
- Enable faster insights for data-backed decision-making.
- Support AI and machine learning initiatives at your organization.
- Lower overall costs by reducing manual labor required by legacy systems.
- Empower team members with self-service business intelligence (BI) tools.
Key Platforms to Consider When Crafting Your Data Modernization Strategy
Depending on your organization’s needs, your data modernization strategy could fall into several different platforms. Choosing the right foundation to build off is crucial, as each will come with a unique set of advantages and limitations.
- Data Lakes: Data lakes drive data modernization forward by providing ample space to store data in its raw, native format. This data can be structured or semi-structured, which allows for schema-on-read (defining data structure as you query it). Another benefit of this data modernization technique is that it is AI-friendly due to its flexible format.
This makes data lakes ideal for teams investing in advanced analytics. Potential drawbacks, however, include complexity in governance and diminished data quality if your internal process isn’t organized.
- Data Warehouses: Data warehouses are centralized systems that help you unlock structured data analysis and business intelligence. Its strengths when it comes to data modernization lie primarily in speed and efficiency. Warehouses can provide fast, optimal querying on top of seamless integration with BI tools such as Tableau.
This can help your organization create reports in a shorter amount of time. It is important to note, though, that data warehouses can be less effective for unstructured data, and upfront schema design could reduce overall flexibility.
- Lakehouses: Data lakehouses are a hybrid approach to data modernization. They are a combination of data lakes and warehouses that aim to combine flexibility with performance and reporting. They’re ideal for organizations that are looking for a “best-of-both-worlds” approach.
Strengths include a unified architecture for all data types, support of both advanced analytics and BI initiatives, and built-in scalability and compliance through tools such as Databricks or Snowflake. This data modernization strategy requires a skilled team for implementation, and it can also be overkill for smaller or simpler data needs.
- Cloud Platforms: Finally, your organization could tackle data modernization and migration through the sea of cloud platforms. Providers like AWS, Azure, and Google Cloud offer the infrastructure and native services needed to implement lakes, warehouses, or lakehouses.
No matter which strategy you go with, always consider the overall costs, as cloud platforms can range in price. Align your platform with business priorities, data volume, and team capabilities to avoid surprises and achieve the best results.
Tips for Choosing the Right Data Modernization Strategy
Because there is no one-size-fits-all solution to data modernization, there are questions you should ask yourself when building the right strategy.
- Are you focused on reporting, predictive analytics, or dashboards? What types of data will you need to store?
- Will you need real-time insights from your data?
- Is the goal to support AI or BI initiatives?
- Do you operate in a regulated industry?
- How do you plan to integrate your data?
For example, an organization focused on BI reporting may benefit from a warehouse approach, while an organization building an AI model may need to follow a lakehouse strategy. Either way, both AWS and Azure provide end-to-end solutions, such as Azure Data Factory, Azure Data Lake Storage, AWS Glue, and more, to support your desired data modernization strategy.
The Benefits of Smart Data Modernization
Done properly, data modernization comes with many benefits for your organization:
- Faster Decision-Making: Once you’ve properly assessed and reorganized your organization’s data, you can empower your team with real-time analytics and self-service BI capabilities.
- Lower Operational Costs: By streamlining your data pipelines and storage tools, you can reduce IT overhead and maintenance costs over time.
- Innovation: A modernized data architecture allows you to set the stage for AI, automation, and future digital initiatives.
To conclude, while legacy systems provided a solid foundation for data management, the time has come to rethink how you access your critical data. Whether you’re looking to create a new data modernization strategy or are exploring options for modernization and migration, align your architecture to your end goals, not just your data.
At SOLTECH, our experienced team helps clients like you evaluate their current data stack, identify opportunities for improvement, and architect a tailored roadmap to data modernization. To learn more, schedule a consultation with us today.
FAQs
What is data modernization?
Data modernization is the process of upgrading legacy systems to scalable, cloud-based architectures that enable real-time insights, improved data access, and support for advanced technologies like AI and automation.
What are the key components of a data modernization strategy?
A strong data modernization strategy includes assessing current infrastructure, defining business goals, selecting the right data platform, ensuring governance and compliance, and planning for seamless integration, migration, and long-term scalability.
How long does a typical data modernization project take?
Depending on your organization’s unique needs, the typical data modernization project could take anywhere from 3-6 months to a year or more.
Thayer Tate
Chief Technology OfficerThayer is the Chief Technology Officer at SOLTECH, bringing over 20 years of experience in technology and consulting to his role. Throughout his career, Thayer has focused on successfully implementing and delivering projects of all sizes. He began his journey in the technology industry with renowned consulting firms like PricewaterhouseCoopers and IBM, where he gained valuable insights into handling complex challenges faced by large enterprises and developed detailed implementation methodologies.
Thayer’s expertise expanded as he obtained his Project Management Professional (PMP) certification and joined SOLTECH, an Atlanta-based technology firm specializing in custom software development, Technology Consulting and IT staffing. During his tenure at SOLTECH, Thayer honed his skills by managing the design and development of numerous projects, eventually assuming executive responsibility for leading the technical direction of SOLTECH’s software solutions.
As a thought leader and industry expert, Thayer writes articles on technology strategy and planning, software development, project implementation, and technology integration. Thayer’s aim is to empower readers with practical insights and actionable advice based on his extensive experience.