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Though they may have been more than functional in the past, legacy data platforms can become a burden to your organization and prevent it from realizing its full potential. That’s why legacy data modernization can effectively transform your organization’s obsolete data systems into modern platforms that are scalable, efficient, and better equipped to handle today’s data needs.
Data modernization is the process of moving away from clunky legacy infrastructure, setting the stage for long-term business growth and a stronger competitive posture. This is vital because legacy data platforms often struggle to meet the demands of modern analytics, machine learning (ML), and business intelligence (BI) tools.
However, modernization involves much more than moving information from one platform to another; in many cases, this just ends up relocating one mess to new technology. Instead, you need to refactor pipelines, processes, and data models to take advantage of modern capabilities, positioning your organization for success. You must also optimize and upgrade the organization’s entire data infrastructure.
With that in mind, the following guide will help you learn more about the importance of data modernization, including common challenges that you may face when transitioning to legacy systems and how to set the stage for a successful modernization journey.
In this article, we will cover:
Legacy data systems are often rigid and limited in their ability to integrate with newer technologies. They often require design tradeoffs for limited functionality, an unnecessary sacrifice. A few of the challenges that these systems can create for your business include the following:
The first inclination of many business leaders is to migrate their data to a newer system. And though that can solve isolated technology issues, it’s not the best approach if numerous components of your IT infrastructure have become antiquated.
Data modernization takes a more holistic approach that optimizes your entire infrastructure to ensure that the new systems are stable and secure. Modern solutions are built for speed and efficiency, providing your team with faster data processing and analytics capabilities. They’re also likely to be equipped with advanced governance features that ensure data quality and compliance, which can be particularly valuable if you operate within a highly regulated industry.
Furthermore, modern platforms readily integrate with other business systems and emerging technologies. A nimble solution will give you the freedom to explore and adopt dynamic tools such as artificial intelligence (AI) and ML.
The bottom line is that data has become a critical driver of innovation. By effectively leveraging the information you collect, your business will gain a competitive edge and gain access to real-time insights that accelerate decision-making processes.
Data modernization journeys can be daunting without a sound strategy. Before setting off, you should take care of the following:
Use data analytics to better understand the state of your current data environment. You’ll need to evaluate the architecture, quality, and performance of existing systems. Identify gaps in your technology suite and be honest about the limitations of your legacy infrastructure.
Consult with employees from various levels of your organization. Team members using the data regularly can provide a different perspective than the C-suite. Cumulatively, your team will be able to pinpoint critical areas of concern and help you better understand the scope of the data challenges you are facing.
Data modernization can take months, even years, to complete. To keep the initiative focused, you must set clear, measurable objectives to serve as mile-markers that will ensure your modernization efforts are purposeful and targeted.
Align the modernization objective with broader business goals, such as reducing costs or improving performance. Link each goal to quantifiable metrics so that you can measure your progress and determine whether your initiative is on track or falling behind.
A phased approach helps mitigate risks and ensures business continuity throughout the process. Start with a pilot project, such as migrating non-critical data, and progressively scale up once your team becomes more familiar with the new platform.
From there, you can prioritize objectives that have a short time to value and a strong projected return on investment (ROI). By delivering impactful results early on, you can encourage widespread and continuing support for your modernization plan. It will also free up capital to tackle the more ambitious phases of modernization.
Modernizing your data platform means tearing down data silos, thereby making critical business information more visible and easier to monitor and manage.
You’ll want to invest in a modern data governance framework and complementary tools that ensure your data remains accurate and accessible. Promoting data accessibility while also maintaining a strong governance posture will foster more effective team performance without exposing the business to compliance risks.
Modernization should include continuous testing and validation to ensure your new system is both accurate and reliable. Consistently test your architecture and underlying applications to ensure that they are functioning properly. And use automated tools to identify issues in real time and maintain the integrity of your data.
You cannot wait until you are on the precipice of rolling out your new solution to begin testing, either. Your team should be conducting tests throughout the entire project so that they can find and fix issues as they emerge. It is a strategy that has become quite popular among software development teams and provides value during larger-scale modernization projects.
There aren’t any one-size-fits-all solutions to data modernization. You should select a strategy that best aligns with the unique needs of your business while also being mindful of budgetary limitations and long-term goals.
Three of the most common approaches are as follows:
Lift & ShiftRefactoring & Clean-UpRe-Architecting
The Lift and Shift approach involves taking an existing legacy data system and moving it “as is” to a new environment. Typically, businesses go the Lift and Shift route when they are satisfied with their existing data system and simply want to move it from an on-premise environment to a cloud-based alternative.
You will not make any major changes to the system’s architecture or the data itself, a quality that makes Lift and Shift one of the most affordable and quickest data modernization strategies. It’s ideal if you have a tight modernization budget and need better scalability and performance, all while avoiding disruptions to ongoing business operations.
There are a few drawbacks to the technique, however. For one, it is a quick fix, which means it doesn’t position your business to easily adopt emerging technologies. What’s more, you will also be bringing over any technical inefficiencies that are present in your existing legacy technology.
Refactoring involves optimizing your legacy system at code level without completely overhauling its architecture. Your team will focus on cleaning up inefficient code, improving performance, and resolving any long-standing issues while retaining much of the original structure.
Refactoring can be a good option if you need to incrementally modernize your legacy application but lack the time or resources for a full re-architecture. You can improve functionality and continue using existing infrastructure.
Other benefits of refactoring include improved performance, cost-effectiveness, and the preservation of business logic. Your team won’t have to familiarize themselves with a new system or environment. They can continue using operational processes that they are comfortable with.
Be that as it may, refactoring can be time-consuming and burdensome on your IT team. It does not future-proof your system, either, which means many legacy inefficiencies will remain in place. A single misstep on the part of your refactoring team can be catastrophic, compounding existing issues.
The most transformative approach to data modernization is re-architecting, a process that involves redesigning the system’s architecture from the ground up to leverage modern tech and cloud-native capabilities. Your team will focus on building a flexible data platform that scales with your company’s changing needs while promoting future growth.
You’ll need to decommission your legacy system or heavily rework it to align with modern architectural principles. That means it must be able to support the use of event-driven architectures, containers, and microservices.
Re-architecting is best suited for your business if it has complex operational demands or high data processing requirements and is looking for a long-term solution that aligns with its future digital strategy. You can move on from legacy technology that can no longer support the company’s needs and adopt new technologies.
You should consider your company’s goals, current infrastructure, budget, and desired timeframe for modernization when choosing between these approaches. Lift and Shift may be the best approach if speed and minimal disruptions are your top priorities. In contrast, refactoring can be a good option when you need to make incremental improvements without a total overhaul. Consider re-architecting when you need to build a robust, scalable platform for the future.
Any major technology initiative will have downstream impacts. In the case of modernization, it can reshape how your team members use data for reporting, analytics, and machine learning processes, providing them with new opportunities to streamline these processes.
However, it’s important to help your team acclimate to the procedural changes. You must also rationalize legacy reports and identify areas where automation can replace manual processes. Automated reporting reduces errors and provides faster insights.
One of the most important considerations during data modernization involves minimizing downtime. Any major changes to your digital infrastructure can be disrupted. You must carefully plan any technology rollouts and perform thorough testing to reduce the risk of disruptions.
Choosing between cloud vs. on-premises architecture represents the biggest decision you’ll have to make during your planning process. Most businesses will find cloud-native platforms to be the most pragmatic option, as they provide virtually limitless scalability and the capability to reduce capital expenses via software-as-a-service (SaaS) and pay-as-you-go billing models.
You should also consider which approach will better support your company’s core business applications. A cloud-based model can facilitate legacy app modernization and enable you to upgrade your technology suite to align with changing business needs.
That doesn’t mean that on-premise solutions are never a good fit, though. In fact, businesses that require more control over their industry or operate in industries with strict compliance requirements may consider them.
You could also opt for a hybrid model that combines the scalability of the cloud with the control of on-premises infrastructure. The trade-off is that these models can be very complex to implement, but they offer the best balance of benefits.
You’ll need to leverage a top cloud data platform as the foundation for your modernization efforts, such as:
Each of these solutions offers unique features designed to support data modernization and advanced analytics. Explore each option and consider which platform best aligns with your short and long-term technology needs.
Modern on-premises solutions are also evolving, offering more flexibility and better performance. While they still cannot match the scalability of cloud-based alternatives, the latest generation of on-premise data platforms is far more versatile than legacy solutions.
Before you begin making changes to your existing architecture, you must document key elements, such as:
A digital document management solution will help you create and maintain accurate, relevant documentation throughout the modernization journey.
The major players in your modernization journey are as follows:
While it’s important for each person or team to stay in their lane, they must also collaborate and support one another through the duration of the project. A synergistic approach will make the process less stressful on your team while also decreasing the risk of major delays.
A consulting firm like UDig can facilitate data strategy acceleration by providing scalable support and guidance to your team. Our collaborative approach provides an objective perspective on modernization challenges while ensuring you have access to the expertise necessary to achieve long-term business goals. We can also provide access to specialized tools and methodologies that provide clarity to your project.
Below are a few missteps that you must be aware of, as they could derail your entire modernization effort:
Underestimating the Complexity of ModernizationFailing to Address Data Quality IssuesInsufficient Testing in an Evolving SystemLack of Stakeholder EngagementIgnoring Change Management & Training Needs
Data modernization is nothing if not complex. There aren’t any shortcuts to exploit, and you cannot resort to half-measures. Accept the fact that restructuring your data architecture will be time-consuming and resource-intensive to ensure you give the project the focus it deserves.
Data modernization projects are meant to provide your business with a clean slate. Don’t tarnish your fresh start by migrating a bunch of junk data into your new environment. Address quality and accuracy issues before bringing the information over into your modernized architecture.
Regularly test your data platform to ensure proper functionality and compatibility with other critical systems. If flaws in the architecture go unnoticed, they could lead to costly disruptions and spur change resistance among your workforce.
You need top-down buy-in to successfully complete a data modernization project. The C-suite must support your efforts, and line-level staff must believe that the changes will make a positive impact on their day-to-day work life. Getting everyone on board early in the process ensures they feel engaged throughout the process.
Be proactive about change management. Put your workforce in a position to thrive by providing them with adequate training and educational resources. If employees are confident using the new technologies you are providing them with, they will embrace them.
UDig has helped several business clients overcome their data challenges with our holistic approach to modernization. One such client, a rapidly growing insurance brokerage company, found itself facing data fragmentation issues. Their existing data platform lacked scalability and flexibility, which could impede the company’s long-term growth.
UDig developed a data strategy and roadmap that will enable the client to better understand profitability and leverage its downstream data to support its big-picture growth goals. Once it implements our plan, the client will be able to provide faster and more accurate pricing, thereby accelerating customer experiences.
You are going to pour countless hours and significant capital into your data modernization journey, so you must make sure that your efforts will provide lasting benefits to your organization by focusing on future-proofing the platform.
Prioritize scalability, flexibility, and cloud-native capabilities so you can modify the platform as your company’s needs evolve. Integrate technologies that will give you a competitive advantage, including AI and ML.
Now that you’re ready to move from ideas to tangible impacts, UDig can map out your modernization journey by providing bespoke consultative services. Reach out to our team, and let’s accelerate your path to modernization with data modernization services.
Reid, SVP of Data and Analytics at UDig, is a long-time data professional with experience at multiple Fortune 500 companies. Most recently, he was the Chief Data and Analytics Officer at Markel. Prior to that he held multiple roles at Capital One including VP of Data Engineering.
