Why 82% of Businesses Invest More in Data Modernization

Imagine this: your sales team needs last quarter’s numbers right now to close a deal. Marketing is waiting for updated customer insights to start a new campaign. But the data is stored in outdated systems and locked in department silos, making it time-consuming and difficult to access. Instead of moving forward, projects are delayed, and your team is left frustrated.

Sound familiar? You’re not alone. Many businesses still operate on legacy data systems that slow decisions, stifle innovation, and limit growth opportunities. Without strong data governance, these problems are getting worse.

Fortunately, with data modernization, you can stop these issues. By migrating data from outdated or siloed systems into secure, scalable environments, you break down existing barriers. As a result, you speed up access and open the door to advanced analytics and AI-driven insights.

According to Forbes, data platform modernization was named the top data and analytics investment priority by 40, 7% of organizations. Plus, 82% of businesses plan to increase their investments in it. This is a great opportunity for you to follow their example and transform your big data strategy.

In this article, we’ll explain what automated data modernization is, explore its use cases, and show what it can bring to your company. Keep reading!

What Is Data Platform Modernization?

Data modernization is the process of upgrading a company’s data systems, infrastructure, and practices to modern, often cloud-based, platforms.

With automated data modernization, you shift from outdated, siloed databases to advanced software where your data is connected and ready to work for you.

This includes everything from structured and unstructured data in spreadsheets, emails, or social media content. You make your data more accessible, secure, and valuable. So, you can turn insights into action faster. Plus, good data governance is one of the benefits you gain when you choose data transformation.

Modern architectures like cloud data warehouses, data lakes, and scalable data centers don’t just store information. With the help of analytics and AI that power these advanced systems, you can spot trends, predict outcomes, and make data-driven decisions.

Along the way, data platform modernization involves integrating, cleansing, consolidating, and transforming your data as part of effective data management. As a result, it’s consistent and ready to use across your business.

In short, data modernization isn’t just a tech upgrade; it’s a business transformation. Rebuild your data, and your decisions will be smarter, your insights more actionable, and your operations far more efficient.

Why You Need Data Modernization

Now that you understand what data platform modernization means, it’s time to see what it can bring to your table. We know your company is unique, so we’ve prepared a list of benefits of data modernization that truly matter to you.

Improved data quality and access

Legacy systems often make it hard to find and trust the right data. The data modernization initiative ensures your information is organized, accurate, and available. So, your teams spend less time searching and more time using large volumes of data to create value.

According to TechCrunch, data quality has become a key differentiator for AI success. That’s the main reason why Databricks acquired Neon and Salesforce acquired Informatica.

Enterprise VCs surveyed by TechCrunch agree: clean, accessible data isn’t just nice to have, it’s essential for AI adoption and the agility businesses need to stay ahead. Data modernization is how organizations achieve this, replacing outdated systems with a unified, reliable data infrastructure.

Better and faster decisions

When your data is clean, connected, and available in real-time, you can make informed choices without guesswork. High-quality data is vital for cornering the market.

According to Forbes, an AI-powered data modernization process enables swift solutions, helping organizations shift from reactive to proactive strategies. As a result, they can enhance the speed and precision of their decisions by incorporating AI into their data strategy.

New opportunities with advanced analytics, AI, and ML

Modern data platforms work well with predictive analytics, AI-driven recommendations, and machine learning techniques. When it comes to data processing, they can help you spot trends sooner, personalize customer experiences, and launch new ideas faster.

It’s no surprise that 72% of businesses have already adopted AI for at least one function, and 64% expect it to boost productivity. With the right data foundation in place, these AI-powered advantages become easier to achieve and scale.

Shorter time-to-market

With streamlined data systems, your business can respond quickly to market changes with new products. You can also adapt to customer needs faster as you’ll have all the relevant information about your audience.

According to Forbes, automated insights and real-time data affect how products are developed. They help businesses launch swiftly and respond quickly to the market. In fact, 73% of global data professionals use data analytics to automate processes and shorten the time between insight and action.

See, data platform modernization matters because clean, high-quality data lets your team act fast and stay ahead of the competition. With advanced analytics and artificial intelligence, you can spot trends early, make smarter decisions, and innovate with ease. Plus, clean, secure, and scalable systems give your business the foundation it needs to grow and adapt for the future.

Ready to transform your data and build a scalable, advanced system? Contact Forbytes today to start your data modernization journey.

What Are the Key Components of Data Modernization?

Now you know that data platform modernization is a valuable strategy for your business, one that is worth implementing. Next, let’s explore the key components of data transformation, why they matter, and why it’s important to make sure all these elements are in place.

• Data integration: B2B companies gather data from many sources: internal systems, partners, suppliers, and customers. Integrating it from CRMs, ERPs, supply chains, and APIs gives a unified view, making analysis easier and decisions smarter.
• Data cleansing: Modernization keeps your data accurate, consistent, and ready to use. Clean data, free of duplicates and errors, leads to trustworthy insights and smarter decisions.
• Data warehousing: A data warehouse stores organized data for analysis. Modern B2B companies use it to handle large volumes and different types of data, making access and analysis faster and easier.
• Analytics and Business Intelligence (BI): Modern data platforms use analytics and BI tools to reveal patterns, trends, and anomalies. This helps you make smarter decisions, optimize operations, and drive growth.
• Cloud & Big Data: Cloud platforms offer scalability, flexibility, and cost efficiency. Big data tools like Hadoop, Spark, and NoSQL help process all types of data, from structured databases to social media and customer interactions.
• Data governance & security: Modernizing data keeps it safe and compliant. Define ownership, control access, monitor usage, and set policies to ensure data is accurate, secure, and meets regulations.
• Data visualization: Visualization tools turn complex data into charts and dashboards. They help teams spot trends, uncover insights, and make faster, smarter decisions with smart data management.

By understanding and implementing these key components, your business can fully unlock the power of its data. Each element plays a crucial role in making data accurate, accessible, and actionable. Together, they help your teams make smarter decisions, operate more efficiently, and drive growth.

Real-World Use Cases for Data Modernization

Data modernization plays a key role in today’s push for smarter, more agile businesses. It replaces outdated systems and transforms scattered, siloed data into accessible, actionable information.

The examples below show how different industries modernize their data and turn it into insights that drive business transformation.

Customer experience

How Modernized Data Is Used:

• Personalized marketing: Combine customer data from multiple sources for targeted campaigns and recommendations.
• Better service: Use AI and analytics to predict customer needs and improve support interactions.

How Businesses Apply Data Modernization:

• Amazon Business: Extended its B2C model to B2B, integrating analytics tools like Guided Buying to personalize procurement and improve visibility.
• Vodafone: Partnered with Google Cloud and Quantexa to unify customer data, enabling personalized offers and proactive churn prevention.

Financial services

How Modernized Data Is Used:

• Fraud detection: AI spots unusual patterns in transactions to prevent fraud.
• Risk management: Real-time insights help assess market and financial risks.

How Businesses Apply Data Modernization:

• JPMorgan Chase: Migrated most of its data to cloud platforms and deployed AI tools like Coach AI, boosting wealth management sales.
• Klarna: Uses machine learning for instant credit approval, streamlining customer experience, and enhancing fraud detection.

Manufacturing

How Modernized Data Is Used:

• Smart manufacturing: IoT and modern systems optimize production and reduce downtime.
• Predictive maintenance: Analyze equipment data to prevent failures and lower costs.

How Businesses Apply Data Modernization:

• Tesla: Uses IoT sensors and predictive analytics to optimize production and reduce downtime.
• PowerArena: Its HOP system tracks operator actions in real time, enhancing safety and efficiency.

Operational efficiency

How Modernized Data Is Used:

• Supply chain optimization: Improve inventory, logistics, and reduce costs.
• Process automation: Use AI and cloud tools to automate tasks and boost productivity.

How Businesses Apply Data Modernization:

• Uber: Built a real-time analytics platform to predict demand surges and optimize driver availability, reducing wait times and improving pricing.
• Airbnb: Developed Airflow to streamline data workflows, enabling faster insights and contributing to open-source innovation.

Retail

How Modernized Data Is Used:

• Inventory management: Track stock accurately and forecast demand.
• Sales analytics: Understand customers, optimize pricing, and improve products.

How Businesses Apply Data Modernization:

• Walmart: Uses real-time inventory analytics to reduce stockouts and optimize supply chain logistics.
• Target: Applies predictive analytics to personalize promotions and improve customer loyalty.

What you can learn from these examples

Here are a few quick tips pulled from use cases:

1. Unify your data sources: Combine information from different systems to get a full picture of customers, operations, or markets.
2. Rely upon AI and analytics: Use them to spot trends, predict needs, and make proactive decisions.
3. Adopt cloud platforms: They make data more accessible, scalable, and easier to analyze in real time.
4. Automate where possible: Streamline processes to boost efficiency and reduce manual work.
5. Use predictive insights: Forecast demand, detect risks, and prevent problems before they happen.

If you’re ready to transform your data, our team is here to help. With our expertise in data engineering, we can turn your data into a powerful driver for growth and efficiency.

How Data Modernization Works

When you’re modernizing your data, it’s not just about moving it from one place to another. You need a clear plan so the process is smooth, accurate, and future-proof. Here’s a breakdown of the main stages:

Stage 1: Data assessment

Before you start anything, make sure you understand exactly what you have. This is also the moment to protect yourself from data loss.

What to do:

• Back up your data so nothing gets lost if something goes wrong.
• Map your data sources to see where everything lives and what’s connected.
• Classify your data so you know what’s standardized and what might need extra work later.

Stage 2: Pre-migration

Think of this as cleaning up before the big move. You’re making sure your data is organized and in the right format for migration.

What to do:

• Cleanse your data by fixing typos, removing outdated info, and correcting errors.
• Profile your data to spot inconsistencies and unusual patterns.

Stage 3: Data transformation

Here’s where you turn messy, scattered data into a format that’s consistent, accurate, and ready to be useful.

What to do:

• Analyze your data to understand its current state.
• Improve and enrich it so it’s more valuable for decision-making.
• Integrate your data from multiple sources into one system.
• Load it into the new environment so it’s ready for use.

Stage 4: Ongoing migration

Migration isn’t a “set it and forget it” process. You’ll need to monitor and manage it as it happens to maximize data modernization efforts.

What to do:

• Manage your resources: budget, tools, and team capacity.
• Monitor data quality to fix issues in real-time.
• Keep stakeholders informed so everyone understands the progress and challenges.

Stage 5: Post-migration

Once data is moved, it’s time to monitor whether it’s complete, accurate, and ready for everyday use. This step is a vital part of successful data platform modernization, bringing benefits to your business.

What to do:

• Reconcile your data by comparing it with the original to ensure nothing is missing.
• Audit your systems to make sure everything works as expected.
• Train your team so they can confidently use the new tools and processes.

How Forbytes Supports Data Modernization

When outdated data systems slow your business, we build modern BI platforms. So, your data is organized, structured, and turned into actionable insights.

Forbytes organizes and migrates data from a data lake to a data warehouse. In the warehouse, your data is sorted and filtered. That’s why only the most relevant and valuable information remains. We can also create a tailored data mart to pull data from the warehouse for analytics, whether you need reports, invoices, or statistics.

Our approach to data modernization is straightforward and reliable, and ensures quality data management. We help you structure, handle, and optimize data to streamline operations and move your business forward.

If you’re ready to modernize your data platforms, talk with our team!