You might think about a technological upgrade when you hear the word ‘modernization‘ in the context of your company data. That’s right, but not completely. Modernizing data infrastructure is a strategic necessity we cannot avoid to be competitive. It empowers businesses to:
- Process and analyze data more efficiently.
- Gain actionable insights.
- Maintain compliance with regulatory standards.
- Drive innovation and competitive edge.
In other words, you’ll forget about the data silos and be able to create a unified working ecosystem that offers advanced analytics possibilities and increased efficiency in business operations.
In this article, we’ll discover how data infrastructure modernization improves businesses and what advantages it may bring.
Table of Contents
- What is Data Infrastructure Modernization?
- Advantages of Data Modernization for Businesses
- Key Elements of Data Modernization
- How to Implement Modern Data Infrastructure
- The Role of Skyvia in Modernizing Data Infrastructure
- Real-life Examples of Data Infrastructure Modernization
- Summary
What is Data Infrastructure Modernization?
Data infrastructure modernization is updating and enhancing an organization’s data management systems, tools, and methods to better handle the volume, velocity, and variety of data in today’s digital environment. This process clarifies data and makes it more accessible, valuable, usable, and ready for further analytics and decision-making, helping avoid the cons of legacy systems.
The data legacy management, solutions, and practices are challenging. To solve these problems, you’ll need new strategies and actions, e.g.:
- Transition from siloed and inefficient legacy systems to more agile, scalable, and integrated data platforms.
- Adoption of cloud-based services, data lakes, advanced analytics tools, real-time data processing capabilities, and more robust data governance and security measures.
Let’s see how data integration, cleansing, consolidation, transformation, etc., improve data quality and benefit companies in real life.
Advantages of Data Modernization for Businesses
Organizations must understand the advantages of data modernization to make such infrastructure improvements. It’s like a house repairement. You know it’s necessary and will make your environment convenient and cozy, but just the thought of some innovations scares you. It was working, so why do I need to change things? The answer is that you’ll feel the results immediately.
Here are at least six benefits your company obtains with data infrastructure modernization.
Optimized Data Management
We live in the data analytics times, where streamlined data management is not just essential or beneficial. It’s critical for any company aiming to be competitive in a data-driven world. The first player in this magic game is cloud data warehousing. It helps to consolidate data from various sources into a single repository, enhancing data consistency across the organization. On the other hand, the integration processes (cleaning, deduplicating, and transforming data, etc.) provide higher data quality and reliability for analysis.
Better Decision Processes
Fast and perfect decisions allow businesses to grow and succeed. Advanced analytics and BI based on real-time data insights are beneficial in this case. Modern data solutions allow quick data analysis and help businesses be top trendy thanks to unmistakable decisions.
Increased Efficiency in Business Operations
Outdated systems always have inconsistent data. That’s a rule. Businesses need cloud computing and big data technologies to bypass this inconsistency. Cloud platforms allow users access from anywhere, creating a more flexible work environment. They also enable disaster recovery solutions, ensuring data backup to restore quickly. So companies can plan their business future and reduce downtime during unexpected events. The abilities of big data for predictive analytics are helpful for forecasting trends, demand, and potential issues before they occur. This approach optimizes resource allocation, prevents downtime, and improves efficiency.
Stable Data Storage Solutions
If your business seeks data quality and accuracy, it needs data modernization. Using advanced data storage solutions means catering to modern enterprises’ efficiency and scalability needs and enhancing the stability and reliability of data management systems. Such technologies ensure companies’ data storage infrastructures are robust, secure, and capable of supporting current and future data demands.
Modifiable and Scalable Solutions
Data volumes grow each second in the modern world, and legacy systems aren’t designed to meet this challenge. Modular data architecture allows businesses to quickly build data infrastructures that are more adaptable to changing technologies, data volumes, and business needs. It provides a sustainable framework that not only supports current data management requirements but even prepares companies for future growth and innovation. Adopting a modular approach to data architecture is increasingly becoming a strategic imperative in a data-driven world where flexibility and scalability mean success.
Advanced Compliance and Data Security Measure
In the modern business world, you’re out of the competition without data governance and security that ensures that data assets are managed responsibly, securely, and in a manner that complies with legal and regulatory standards. While your data is growing in volume, variety, and strategic importance, these practices protect your company and enhance its competitive edge by making data a safe, reliable, and accessible resource for all stakeholders.
Key Elements of Data Modernization
The next step of the data modernization journey includes considering its key elements. Here is the list of the most important ones.
Data Consolidation
Data consolidation helps companies perform holistic analysis, gaining insights that might not be apparent from isolated datasets.
Consolidating data reduces redundancy and simplifies the data infrastructure, making data management processes more efficient.
High-Quality Data Standards
Cleaning and standardizing data enhances its overall quality and usability. They ensure data is a valuable, trustworthy asset supporting advanced analytics, AI, and data-driven decision-making.
Data Storage Solutions
Modern data storage technology offers solutions that prioritize scalability, performance, cost-efficiency, and accessibility. Let’s go through the list of them:
- Cloud Storage: Provides scalable, flexible, and cost-effective remote data access.
- Solid State Drives (SSDs): Offer faster data access speeds and improved reliability over traditional HDDs.
- Object Storage: Ideal for unstructured data, it enhances scalability and data manageability with metadata tagging.
- Network Attached Storage (NAS): Enables efficient data sharing and collaboration through centralized network storage.
- Storage Area Network (SAN): Delivers high-speed, dedicated network storage access for data-intensive applications.
- Hyper Converged Infrastructure (HCI): Simplifies data center management by integrating computing, storage, and networking.
- Data Lakes: Support big data analytics by storing vast raw data in its native format.
- Hybrid Storage Solutions: Combine storage types to balance performance, cost, and accessibility.
Cloud-Based Technology
Cloud computing in modern data infrastructure fundamentally transforms how data is stored, processed, and analyzed. It offers scalable, flexible, cost-effective solutions supporting data-driven companies’ growing demands.
Business Data Insights
BI tools transform raw data into meaningful information, like reports, dashboards, and visualizations, to make them easy to understand. This process allows companies to identify trends, patterns, and anomalies, facilitating data-driven decision-making.
Key benefits of this approach are:
- Improved operational efficiency
- Enhanced customer understanding
- Increased competitive advantage
- Ability to predict market trends.
Such solutions also support real-time analytics, helping businesses to respond quickly to changing conditions.
Graphical Data Presentation
Modern data analytics needs the transformation of complex datasets into intuitive graphical representations, graphs, and heat maps. It helps to communicate insights, foster an organization’s data-driven culture, and make understanding patterns, trends, and outliers within large volumes of data easier. In today’s data-rich environment, effective data visualization accelerates the analysis, enhances comprehension, and supports the identification of actionable insights for better business strategies and outcomes.
Data Management Policies
It’s critical to have data governance and security measures to ensure your company’s data integrity, availability, and confidentiality. Effective data governance frameworks define policies, standards, and procedures for managing data across its lifecycle, enhancing data quality and making it a reliable asset for decision-making. Strong security measures protect sensitive information from unauthorized access, breaches, and cyber threats, maintaining trust among stakeholders and customers. In other words, data governance and security are critical to regulatory compliance and risk management and save an organization’s reputation.
How to Implement Modern Data Infrastructure
If you wish your data management systems to be more scalable, flexible, and efficient, you must implement a strategic approach to redesign and upgrade them.
You may start with assessing current data infrastructure, identifying areas for improvement, and defining specific business needs and objectives.
The table below displays each step in detail.
| Step | Definition |
|---|---|
| Set Objectives and Goals | Clarify objectives to see what to modernize, like data silos reducing and security. |
| Analyze Current Situation | Revise the things you’re staying on, like: – Identify existing data sources, types, and storage. – Evaluate data quality, accuracy, and completeness. – Think about data governance practices and compliance with data regulations. – Analyze data integration, warehousing, and analytics solutions you already use. |
| Determine Main Business Influencers | Identify key stakeholders and factors that drive, impact, or are significantly affected by modernization initiatives. |
| Create a Data Strategy | When you know your objectives, assessment, and business drivers, you can create a working data strategy, like: – Data integration and management methods. – New data architecture scenario. – KPIs for measurement success. – Security and compliance checking. |
| Order Initiatives by Priority | Prioritization is usually about paying attention to cost, complexity, and expected return on investment. So, you have to identify and prioritize specific data modernization initiatives based on their potential impact and alignment with your data strategy. |
| Choosing Appropriate Tools and Technologies | Select the convenient data infrastructure modernization tools according to your organization’s goals, existing infrastructure, and plans to work with, like data integration platforms, data lakes, data warehouses, data quality tools, analytics tools, etc. But ensure that the chosen ones can scale with your requirements. The links below might be helpful in the selection. – Top 10 Data Analysis Tools in 2024 – Top 10 Data Warehouse Tools in 2024 – Selecting the best Data Movement Tool in 2024 – Best CRM Data Enrichment Tools [2024] – Top 6 Data Orchestration Tolls in 2024 – Core Factors That Influence the Cost of Data Analytics – What Are the Benefits of Business Investment in Data Consolidation? – Most Popular Salesforce Reporting Tools in 2024 |
| Data Management Oversight | Implementing data governance policies and standards is the best way to ensure your data’s accountability, quality, security, and compliance. Create a working data governance framework and clarify data ownership, user roles, and abilities for data management oversight. |
| Integrating and Migrating Data Systems | Consider migrating data from legacy systems and implementing it into innovative scenarios and data storage to raise data quality and consistency. |
| Securing Data and Ensuring Compliance | All the data must be protected and comply with data privacy and industry-specific standards like GDPR, CCPA, HIPAA, ISO 27001, SOC 2, etc. |
| Analyzing Data for Insights | Clarify objectives to see what to modernize, like data silos reduction and security. |
The Role of Skyvia in Modernizing Data Infrastructure
Businesses often hesitate while choosing the correct data infrastructure modernization tool. The app must be cloud-based, no-code, simple in usage, supporting a variety of connectors and business scenarios. And, of course, it has to keep a balance between functionality and price. That’s right because if we’re starting the big repair, we need to see what we receive at the endpoint.
In this case, Skyvia is a good choice. The platform is cloud-based, pretty simple in usage, and supports 180+ sources like cloud apps, databases, and storage services. It provides ETL, ELT, reverse ETL, data migration, one-way and bidirectional data sync, workflow automation, data sharing via REST API, backups for cloud apps.
The pricing is pay-as-you-go and includes the free plan to start with.
- Basic ($15/mo)
- Standard ($79/mo)
- Professional ($399/mo).
And, of course, Skyvvia’s Enterprise option tailors companies’ unique scenarios to match its growth.
Real-life Examples of Data Infrastructure Modernization
The real-life use cases below show how modernizing data infrastructure with Skyvia solution simplifies businesses’ lives and improves decision-making.
Teesing Improved Data Orchestrating with Skyvia Data Integration
Background: Teesing is the worldwide supplier for the high-tech OEM market, developing and assembling turnkey sub-systems to control the gas and liquids market. They needed to export data from the website to CSV files and import it back. Creating a custom Python script for each data export-import was necessary but also non-productive, tedious, and time-consuming.
Another point was that the CRM system could not automatically import data on a scheduled basis, making data transfers from the website to CRM impossible without manual imports.
Solution: The company introduces automated data management in its quality control processes, using sensors and machine learning algorithms to detect defects in real-time during manufacturing. The system automatically adjusts machinery or halts production if quality issues are detected.
Result: Data-related performance enhanced, data integration automated, human impact decreased, manual work avoided, and time and costs saved.
NISO Automated Data Flows for Financial Operations with Skyvia
Background: NISO is an outsourced CFO company that provides services to the MCA industry (merchant cash advance), offering its customers outsourced finance and analytics departments. They searched for ways to connect different operational data sources within the cloud infrastructure. Precisely, the company needed to load data from MySQL to QuickBooks Online.
Solution: Skyvia has a connection to QuickBooks, so the NISO team would be able to connect services in the cloud directly. Skyvia also has a lot of necessities regarding SQL and MySQL servers and integrating with other products with their specific developing features. For that, it’s the perfect choice.
Result: With Skyvia’s integration features, NISO can get a broader reach for customer information and confidently scale.
Skyvia & Cirrus Insight: Salesforce-QuickBooks Integration
Background: Cirrus Insight is a CRM platform integrating Salesforce with third-party services, like Gmail and Microsoft’s Office 365. They adopted the strategic approach to integrate Salesforce with QuickBooks using Skyvia, aiming to enhance productivity and reduce costs.
Solution: Skyvia enables Cirrus Insight Salesforce-to-Salesforce and Stripe-to-Salesforce integration and connecting with QuickBooks,
Result: Operational efficiencies are improved, financial reporting is streamlined, and significant costs are saved.
Summary
To stay competitive in today’s fast-paced, data-driven business environment, companies have to update legacy data systems and processes to more contemporary, efficient, and scalable solutions. The tools they’ll use depend on the needs and scenarios, but the key balance is the solution’s usability, simplicity, and appropriate pricing.
