The Complete Guide to a Modern Data Platform in 2024

Data has become the lifeblood of every modern business. Whether an enterprise SaaS company or a mom-and-pop shop, you need data to support (and drive) business decisions.

However, most organizations don't have an issue collecting data—that's not the problem. It's cleaning, condensing, unifying, and extracting insights from that data—that's where businesses struggle.

Fortunately, there's a solution: modern data platforms.

A modern data platform helps your business capture, analyze, and activate data across global digital ecosystems. It's the technology and tools that turn your data into decisions.

New to data platforms? We've got you covered. Below, we'll walk you through everything you need to know about the modern data platform, including what it is, how it works, why you need it, and how to build it.

What Is a Modern Data Platform?

A modern data platform is a collection of tools and technology that manages your organization's full data lifecycle. It integrates everything from data ingestion and storage to processing and analysis. It's the framework that helps you collect, dissect, and use data consistently to support real-time decision-making and strategic planning.

Traditional data management systems tend to operate in silos. Data is typically isolated in different local and source environments. Customer data lives in your customer relationship management (CRM) tool, website statistics live in your site analytics tool, in-person sales live in your POS system, and paid advertising details live in your paid campaigns program—and this limits holistic cross-platform analysis.

That used to be as good as it got, but now, in 2024, a modern data platform consolidates your data, facilitates real-time processing, and supports advanced analytics.

Your modern data platform does the following:

  • Centralizes Data Management: It consolidates data from multiple sources (including cloud applications, social media, IoT devices, and on-premises systems) to create a single source of truth.
  • Supports Advanced Analytics: The platform provides advanced tools for data scientists and analysts to use complex analyses, predictive modeling, and machine learning to turn raw data into practical insights.
  • Improves Data Accessibility: These data platforms provide user-friendly interfaces, governance models, and visualizations to make data accessible to a broader range of users.
  • Facilitates Real-Time Processing: Modern data platforms handle streaming data to let businesses process and analyze information as it arrives (like for financial trading, online retail, and dynamic pricing).

Benefits of a Modern Data Platform

Data dictates (or should dictate) every business decision you make. From the products you develop to the prices you set to the channels you use, every strategy should be data-backed and data-driven. 

However, data alone doesn’t help you make better business decisions. Conversely, massive amounts of data without proper centralization and activation could do the opposite.

Your modern data platform doesn’t just store information—it transforms it into insights. 

Here are a handful of the benefits of a modern data platform:

  • Data-Driven Decision Making: Empower your teams to make informed decisions quickly based on accurate, real-time data insights.
  • Increased Operational Efficiency: Automate routine data tasks, reduce redundancy, and streamline data flows to improve overall operations.
  • Improved Collaboration: Modern data platforms provide tools to help teams work together. Creating a unified view of your data empowers teams across different departments to collaborate more effectively and work together to drive better business outcomes. 
  • Better Customer Experiences: Use advanced analytics to better understand customer behavior and preferences.
  • Scalability: Easily scale your modern data platform to accommodate growing data sources, volume, and needs.
  • Real-Time Analytics: Access to real-time analytics helps your business react instantly to customer interactions, market shifts, and internal alerts. 
  • Compliance and Security: Keep your data secure and compliant with international data regulations through advanced security features and regular updates.
  • Innovation and Competitive Edge: Leverage the latest in AI and machine learning for predictive analytics that keep you ahead of the curve.

While we’ve labeled these as benefits of a modern data platform, they’re really non-negotiable elements of staying competitive in a fast-evolving digital world. You need these perks to stay on top of your data and turn it into one of your most valuable business assets. 

Importance of Modern Data Platforms

There's a reason for the recent push for modern data platforms. The digital landscape has transformed, and businesses need data they can trust before they risk money and entire business concepts. 

Here are the foundational philosophies that most modern data platforms follow:

  • Democratization of Data: Data should be accessible to everyone. Technical boundaries shouldn't prevent decision-makers from participating. Data platforms need user-friendly tools and interfaces that empower everyone to get involved.
  • Agility and Flexibility: Modern data platforms need to adapt quickly to changing business needs and technologies. This means they can integrate new data sources, scale in response to fluctuating demands, and update with minimal disruption. It's all about staying flexible—you never know what's around the corner, and you should be ready to pivot.
  • Real-Time Data Processing: Data platforms need to be able to ingest, process, and analyze data as it's captured. This lets you gain immediate insights and responses, and that's non-negotiable for industries like finance, retail, and telecommunications.
  • Security and Compliance: With increasing concerns over data breaches and privacy regulations, modern data platforms need strong security measures and compliance protocols. Data should be secure, and it also needs to be transported and stored properly.
  • Sustainability and Efficiency: Data centers aren't cheap on business bank accounts or the environment. Modern data platforms recognize environmental impacts and focus on optimizing energy use and resource efficiency. It's all about reducing the carbon footprint by making data work smarter (not harder).
  • Continuous Innovation: This is just the beginning. Modern data platforms don't stay modern without non-stop continuous development. Platforms need to incorporate leading technologies, and today, that's solutions such as machine learning, AI, and blockchain. Tomorrow, who knows what it'll be.

Must-Have Components of a Modern Data Platform

Modern data platforms need to have a variety of components to provide a holistic solution to businesses. These components are the backbone of your platform—they handle the specific, special functions that capture, aggregate, and activate your data:

  • Data Ingestion
  • Data Storage and Processing
  • Data Transformation and Modeling
  • Data Intelligence and Analytics
  • Data Governance and Compliance

Data Ingestion

Data ingestion describes when data enters your platform, and its the first time in the data management process. Modern platforms need to be able to handle batch ingestion and real-time ingestion:

  • Batch Ingestion: This method involves processing accumulated data at scheduled intervals—daily, weekly, or even monthly. Batch ingestion is ideal for large volumes of data that doesn’t require immediate action (such as historical data analysis or monthly reporting). It lets you process data efficiently on a large scale to handle extensive datasets without impacting performance.
  • Real-Time Ingestion: Real-time ingestion deals with data that is captured and used immediately (as it is generated). This type of ingestion works for applications that rely on timely data to make quick decisions (such as fraud detection in baking or real-time personalization in e-commerce). 

Being able to ingest data in both ways lets you do everything from analyzing historical trends to making split-second decisions.

Your data ingestion tool will also tidy up and organize your data to make it consistent, and that's easier said than done, especially when you're collecting data from dozens of different sources. This involves validating and correcting data as well as standardizing formats.

Data Storage and Processing

Modern data platforms facilitate processing, storing, and fetching your data. Some solutions store your raw data until it's ready to be used, while others prepare it immediately in a ready-to-use format.

Here are a few common storage and processing solutions:

  • Data Warehouses: These are specialized databases optimized for analyzing and retrieving data quickly. Data warehouses support complex queries and are essential for business intelligence and analytics. They store data in a structured format, organized into tables and schemas, which makes it easier to perform fast and effective data analysis.
  • Data Lakes: Data lakes allow for the storage of massive volumes of raw data in its native format. This approach is flexible because it doesn't require initial data structuring or processing. You can store unstructured data—from logs and XML files to social media posts—and process it only when needed, which is cost-effective for managing big data.
  • Data Mesh: A data mesh adopts a decentralized approach, advocating for a domain-oriented and self-serve data infrastructure. In a data mesh, each business unit manages its own data but follows agreed-upon standards and protocols to maintain interoperability across the organization. This setup improves data accessibility and autonomy across different parts of your organization.

Data Transformation and Modeling

Data transformation and modeling refine and reshape data to make it more useful and accessible for analysis and decision-making.

Data Transformation:

  • Cleaning: This initial step removes inaccuracies and inconsistencies from data, such as duplicates, errors, or incomplete information.
  • Normalizing: Normalization involves organizing data to reduce redundancy and dependency by dividing large tables into smaller, related tables.
  • Enriching: Data enrichment improves raw data with additional context or insights from other sources. For example, appending demographic information to customer records can provide a fuller understanding of customer segments.

Data Modeling:

  • Defining Relationships: Data modeling includes establishing the relationships among data types. For example, defining foreign keys that link one table to another based on common data points.
  • Schema Development: Whether it's a star schema for simpler querying in a data warehouse or a more normalized form for transactional databases, the schema dictates how data is stored, connected, and accessed.
  • Indexing: Creating indexes on data attributes that are frequently searched speeds up query performance. Indexing improves read operations, but it must be balanced with write performance as it can slow down data insertion processes.

Data Intelligence and Analytics

Data intelligence and analytics turn your raw data into actionable insights. This component includes advanced analytics, business intelligence tools, and machine learning models that help your business:

  • Predict Trends: Data analytics tools can forecast future trends by analyzing historical data and identifying patterns. This helps you anticipate market changes, customer behaviors, and potential industry shifts.
  • Optimize Operations: Analytics tools scrutinize performance across various processes, identifying inefficiencies and bottlenecks. These insights help you streamline operations, reduce costs, and boost productivity.
  • Personalize Customer Experiences: Analytics platforms use customer data to tailor experiences, offers, and communications to individual preferences and behaviors.
  • Drive Innovation: Data-driven insights foster innovation by highlighting new opportunities and underexplored markets. Businesses can use data analytics to develop new products, services, or business models that meet evolving customer needs.
  • Improve Resource Allocation: Analytics provide a detailed view of resource usage and returns to help you allocate assets more efficiently.
  • Mitigate Risks and Business Threats: Data analytics improve risk assessment by predicting potential risks based on trends and historical data. They help identify fraud patterns and security threats ato help you develop more robust preventive measures.

Data Governance and Compliance

Modern data platforms provide automated tools to keep your data compliant. It helps you follow internal policies and external regulations to better manage user access, secure data transfers, and maintain audit trails for accountability.

Here’s how:

  • Policy Management: Modern data platforms let your business define and enforce data policies that govern data access, usage, and security.
  • User Access Control: Modern platforms provide authentication and authorization features that regulate who can access data based on their roles and responsibilities. This granularity improves security and minimizes the risk of data leaks or unauthorized access.
  • Data Encryption and Secure Transfers: Modern data platforms safeguard data at rest and in transit, protecting sensitive information during data exchanges across networks.
  • Audit Trails and Monitoring: Modern platforms automate creating audit trails for forensic analysis if security breaches occur and for regular compliance audits. These logs provide a transparent overview of data movements and modifications.
  • Compliance Automation: Many modern data platforms offer compliance automation tools that help your organization stay up-to-date with the latest regulatory changes. These tools can automatically apply necessary updates to data handling and processing protocols.
  • Data Quality Management: Good governance also involves maintaining high data quality. Modern platforms provide tools that continuously clean, deduplicate, and validate data to guarantee it remains accurate and useful for decision-making purposes.

How to Build Your Business's Modern Data Platform

Every modern data platform looks different based on the unique needs of businesses, users, and industry regulations. While your data platform and collection of tools won't look exactly the same as another, there's a process for building it out to make sure you don't forget anything.

Here's a simple approach to creating a comprehensive data infrastructure that's tailored to your data needs:

  1. Evaluate Your Data Needs: Start by considering the types of data your business generates and the insights you need. Understand the data flows within your organization and identify key areas where data can add value.
  2. Define Your Data Architecture: Decide on the architecture that best suits your needs—whether it's a centralized data warehouse, a flexible data lake, or a decentralized data mesh. Consider how these structures will support your data types and usage scenarios.
  3. Choose a Comprehensive Data Pipeline Platform: Use a platform like Matillion to access tools that efficiently build and manage data pipelines. Matillion's integrations let you connect various data sources and automate workflows to improve data availability and quality.
  4. Implement Data Governance: Establish data governance policies to maintain data quality, compliance, and security. Set standards for data access, usage, and privacy to keep control over your data assets.
  5. Deploy and Integrate Analytics Tools: Select and integrate advanced analytics and business intelligence tools that can turn your raw data into actionable insights. Find tools compatible with your data platform.
  6. Train Your Team: Give your team the necessary skills to use the data platform—the last thing you want is high-quality data that's not powering your decisions. Consider ongoing training and support to keep up with new technologies and methods.
  7. Evaluate and Scale: Regularly review your data platform's performance and scalability. Adapt and scale your system as your business grows and as new data technologies emerge.

Take Control of Your Data with Matillion

Building and activating a modern data platform might feel daunting, but it doesn't have to be. With the right tools and partners, you can build a sustainable, scalable solution that lets you do more with your data. And we can help.

Matillion helps your business take control of its data with powerful, easy-to-use solutions that streamline data workflow creation and management. Whether you're looking to integrate complex data sources, accelerate data transformations, or enable real-time analytics, we provide the agility, scalability, and efficiency you need to make it happen:

  • Streamline Data Processes: Automate and manage data pipelines to reduce time-to-insight and let you focus on strategic decisions.
  • Enable Scalability: Adapt and scale your data solutions as your business grows and data volumes increase.
  • Improve Data Governance: Keep your data secure, compliant, and ready for use at all levels of your organization.

Don't let the complexities of data management hold your business back. Try Matillion for free today to unlock the full potential of your data. 

Ready to see how Matillion can transform your data strategy? Book a demo and discover the power of a modern data platform tailored to your business needs.

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