The complexity and interconnectedness of world economies have led to an exponential increase in the volume of data generated, stored, and used worldwide. It reached a staggering 59 zettabytes by 2023. Companies today must adopt a modern data architecture to handle the increasing data volumes.
The evolution of data architecture in most companies has been heavily influenced by changes in business and operational requirements. Certain key elements of data modernization can be shared by all businesses. The need to enhance traditional use cases and make them cost-effective.
This new generation of data architecture relies on a few common data management tools. These tools include the data lake, real-time streaming, and data warehouse.
No matter what industry you work in, modernizing the data architecture requires a revolution in data-centric technology. To ensure agility, efficiency, speed of ingestion, siloed information must be migrated from legacy databases to modern cloud-based storage lakes.
1. Modernized Data Architecture is Critical
Before we get into the reasons why businesses should modernize their data systems, let’s first look at how legacy data management tools work. To achieve desired outcomes, the traditional data stack consists of a variety of tools that must be perfectly aligned with one another. These systems were designed to handle static and predictable data that is derived from a fixed source.
The increase in data complexity and predictability has resulted from the diversification and variety of data sources, both structured and unstructured. The traditional data management tools were deemed expensive, complex, and ineffective for solving modern data problems. The pace of change has been rapid.
Businesses can use existing data and analytics platforms to create a flexible, scalable data stack. Data modernization improves productivity by revolutionizing the way enterprises gather, process, analyze and use data to make better business decisions
They offer a single point for access to data assets from multiple sources and a coherent view of them all at once:
- Data governance can be improved by complying with privacy and data protection regulations.
- Access the correct data at the right moment
- By bringing together data points from different sources, you can bridge organizational silos
- Offer valuable and actionable insights that will meet the demands of a dynamic business environment
- Reduce costs, data latency, speed time to market, and better decision making
- Unstructured data can be accommodated and open-source technologies accepted
Data modernization can also have important benefits from the standpoint of data strategies. Modernizing the data architecture will allow for enterprise-wide, real-time democratization of data. This can lead to lower OpEx costs, better data security, and governance, as well as improved data quality.
Hybrid infrastructure: As enterprise data continues its rapid growth, companies must consciously create robust data retention strategies. To ensure the best analytics delivery, companies must devise a feasible data storage structure.
Data modernization is therefore even more important. Modern enterprises must plan, update, build, and maintain the correct data storage architecture to modernize their data core.
Cross-country data: A key driver of data modernization is the frequent modification in cross-country regulations surrounding data practices. The majority of companies today do not restrict their operations to one country or location.
Companies have had to expand their data boundaries beyond the borders due to the rise of nearshoring and offshoring business models. This has placed enterprise data under the purview of various, but strict data regulations.
For example, data retention regulations in the US vary from state to federal. It depends on the data type and the nature of the regulation. Companies that rely on inaccessible data formats may face significant difficulties in querying data, which can lead to non-compliance and loss of revenue.
Monitoring data use: Cross-border operations require multiple teams to evaluate the same data from different locations. Companies must thoroughly audit data usage and monitor the utilization of resources to ensure seamless data access. Data modernization can make a significant impact in this area by creating data lineage. This allows teams to maintain transparency and a clear audit trail.
2. Impact of Cloud on Data Modernization
Two major changes are evident in the current IT landscape. Data modernization is one of them. The rise of the cloud is the other. The convergence of these two factors has been the basis for recent digital transformation developments.
Data modernization is possible through cloud data warehouses. A Deloitte survey found that 55% of organizations consider data modernization to be a key component in cloud migration.
Cloud provides better performance at low costs and high scalability:
- Increased capacity: A serverless infrastructure has many benefits. Users can scale easily with a fully managed cloud platform without needing to manage standard database operations.
- More flexibility Cloud platforms allow for greater automation of resource allocations based on demand. This allows for greater flexibility.
- More tools at your fingertips: Cloud platforms remove data silos and help you see all of your business functions from one place.
- Deeper insights: The cloud computing system automates data processing and unites data sources under one cloud repository. This allows for deeper, faster, and more valuable insights.
- Improved security: Cloud is a great platform for data modernization projects. Its state-of-the-art security management feature is another important aspect. This is designed to enable organizations to harness cloud apps and networks to their full potential while minimizing potential threat vectors. Cloud platforms often include encryption key management, which helps businesses protect data from loss and maintain data integrity. It encrypts mission-critical data and secures connections.
- Enabling transient and stateless application architecture Lastly, cloud computing has made modern technology stacks more modular. It allows for both transient and stateless architectures to flourish and is flexible and agile. Stateless architectures enable seamless deployment of smaller and simpler microservices and assist companies in moving away from the clunky monolithic apps of the past.
3. How to Modernize a Legacy Data System
Modernizing your legacy data systems does not have to be all or nothing. Companies can still be competitive and relevant even if they don’t build their data stacks entirely from scratch. Many of the problems that legacy systems present can be solved by a well-designed hybrid system.
These are steps to upgrade your legacy system. They are based on the following principle:
- Assessment: Start by identifying your goals, challenges, and needs. Don’t get too excited about the total overhaul of your IT systems. Instead, evaluate the new IT stack in terms of its features, business value, as well as the willingness of your customers to accept the change. You will eventually be better equipped to determine if you require a complete or hybrid transition.
- Migration: Even though you don’t want to replace your IT infrastructure, cloud adoption is worth considering. Moving data to a cloud platform can help you free yourself from technology limitations and be a step towards legacy modernization. It is possible to research the cloud tools that are most relevant and popular among the market competitors and then adopt them.
- Adoption Adopt containerized apps that are independent of applications and infrastructures. This will allow for portability and flexibility.
- Prepare for future growth and changes. This will allow you to remain relevant in a changing market. To ensure that you stay relevant, you can track KPIs and benchmarks.
- Partnership: Select the right partner for your budget and needs. Your legacy systems should be integrated with a cloud infrastructure, or they can be completely replaced if necessary.
Modern-age businesses are based on data. Business leaders cannot afford to ignore the potential for data value enhancement. Data modernization is an evolutionary aspect of data management solutions that can help businesses accelerate their digital transformation.
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