A Leader’s Guide to Modern Data Platforms
This guide breaks down the different potential components of a modern data platform in clear terms, helping you evaluate whether investing in one makes sense for your organization’s goals. It is the first in a series designed to help business leaders understand the value and cost of a modern data platform, without needing a technical background.
What is a Data Platform?
The concept of a data platform is nothing new. Companies have been consolidating data into data warehouses for decades, in an attempt to create centralized data. As computing has advanced, data lakes have become increasingly popular.
The foundation of a data platform is a centralized area to store data from multiple sources. This is then fed into business intelligence and reporting. However, a modern data platform is much more than just this technology. The key focus is to create golden data. Golden data is centralized data that is trusted, validated, and AI-ready. This means faster insights with a centralized, single source of truth, ensuring that your business decisions are based on accurate and reliable information. Without this, a company cannot take full advatange of their data or deploy effective AI.
To achieve this, in addition to centralized data storage, a modern data platform incorporates multiple components. Each component is a separate piece of the overall system that focuses on a specific function, such as AI, Master Data Management, or Reporting.
A well-designed data platform is more than just data storage. It’s a strategic tool that compiles golden data, democratizes that data, and leverages it in multiple ways across the business to improve decision-making and drive business growth.
This is the first guide in a series for business leaders who want to understand how to leverage modern data platforms and AI, evaluate their benefit and cost, and calculate the return on investment.
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What are the components of a data platform?
Data platforms are far more than just moving and storing data. A modern data platform is a combination of multiple components - separate pieces of the overall system that each focus on a specific function. While data platforms can take many shapes, this guide will explore the components of a typical platform. Each component is split into layers, and each layer has several assets within it. A layer represents a functional part of the component, and and asset is a specific tool or technology that builds that layer. While there are many tools or technologies that can be used, we’ve given an example for erch asset.
Component: a separate piece of the overall system that focuses on a specific function, such as AI, Master Data Management, or Reporting.
Layer: a functional part of that component, such as the Storage Layer or the Ingest and Transform Layer.
Asset: a specific tool or technology that builds that layer.
What is an example of a modern data platform?
Now we will look through a well archirected, modern data platform and review each component in detail.
A typical architecture will look like this:
Let us review each component in more detail.
Data Infrastructure
The data infrastructure component is the foundation of any data platform. It is commonly described as data engineering and it involves moving data from all source systems into a single location. That data is cleaned and transformed into usable formats for reporting and analytics. These are known as pipelines – piping data from the multiple source systems into the cleaned and transformed central storage.
Deploy Layer: Keeps data systems up to date automatically and ensures any changes made by the team are released in a safe, consistent, and efficient way. This uses proven IT best practices called Continuous Integration/Continuous Deployment (CI/CD).
Ingest & Transform Layer: Extracts data from multiple systems and applies business rules and transformations.
Store Layer: Persists data in structured or unstructured formats such as data lakes and warehouses. This will often take the approach of a data lakehouse, set in a ‘medallion architecture’ - known as Bronze, Silver, and Gold layers.
Reporting and Business Intelligence (BI)
Modern BI tools like Power BI and Tableau turn data into dashboards, KPIs, and reports. While traditional reporting is structured, and pre-designed, users also need access for ad-hoc or self-service needs.
Exploration Layer: For pro-code teams, this is a tool that enables users to freely query and analyze raw and curated data for insights. Users will often use the SQL language.
Semantic Layer: For business teams, a semantic layer provides a consistent business-friendly view of data and standardized metrics in a self-service manner. Users can connect using tools like Microsoft Excel or Power BI.
Reporting Layer: Structured, defined dashboards and visual reports tailored to business users and leadership. GenAI can also be used here, allowing users to ‘talk’ to their data by asking questions and getting accurate, data-driven responses.
Master Data Management (MDM)
MDM is a crucial and often overlooked component of a data platform. Its job is to ensure clean, reliable data with enterprise-wide consistency.
MDM Layer: Data quality is often limited accross different sources. This layer builds a golden data list, such as a master list of customer names. It can then write back and update each source to and ensure that key entities such as customers, products, vendors, and accounts are standardized and accurate across all business systems, enabling unified reporting.
File upload Layer: Even with good practices in place, businesses often have data in formats such as Excel they need to ingest into the Data Lake. By nature, files like these are difficult to ensure data quality. This layer must ensure that validations are run on these files, such as that a phone number contains the correct amount of digits.
Monitoring Layer: A good data platform will use AI and automate as much data quality rules as possible, but manual intervention is often necessary. A key part of any MDM solution is to provide insight into where this is needed. This is in the form of exception reporting.
Intelligent Collaboration and Business Apps
Data becomes more valuable when it's embedded in daily workflows. Integrating with tools like Microsoft Teams, Excel, and low-code business apps ensures teams can access and act on insights collaboratively and efficiently.
Productivity Apps Layer: Builds tailored low-code/no-code apps that address specific operational needs, embedding data into business applications.
Agentic Workflows Layer: Agentic workflows use GenAI, mimicking analysts. Agents can react to data driven events, and even be given power to take appropriate actions.
Workflow Automation Layer: Connects systems to use data to trigger automated workflows, alerts, and approvals.
AI and Machine Learning
Artificial Intelligence and Machine Learning extend traditional reporting with predictive analytics, financial forecasting, and anomaly detection. These tools help CFOs move from reactive to proactive decision-making.
Machine Learning Layer: Trained AI models that can perform jobs such as forecasting, to improve business needs. Other examples include anomaly detection, such as using AI to automatically find unusual transactions. This can enable advanced analytics integrated into business processes.
Explore Layer: Allows data scientists and analysts to investigate data patterns, make predictions, and to run advanced analyses that take advantage of powerful AI algorithms.
Data Governance and Strategy
Transferring data via services based in certain regions, such as the U.S., may violate data protection laws like GDPR. It’s essential to understand legal implications when building a global data platform. It can be easy to overlook detailed requirements and adherence to regulations like GDPR and SOX. Data governance ensures your data platform is secure, compliant, and trustworthy. However, it goes far beyond this. A good data governance strategy will help foster a data culture and actually enable data to be used effectively across your organization.
Plan, Deploy, and Monitor: This describes planning for policies, roles, and responsibilities for data ownership and quality. Tools are deployed to track data usage, quality, and compliance over time. It includes access controls, audit logs, data lineage.
Governance: Map and create data catalogs to improve data understanding, tracking, and compliance. Implements controls, classifications, and rules within the data environment.
Secure: Use state of the art protection against outside attacks. Minimize potential for data leaks, mitigate risks, and comply with regulation.
FAQs:
Do I have to implement all components?
No. Most companies will start with Data Infrastructure and Reporting, then build out over time. While some form of data governance and master data management is highly recommended, many companies lack the resources or need for some of the more advanced components.
Do I need a data platform if I already have dashboards and reports?
Yes. Dashboards are only the surface. A data platform ensures the underlying data is clean, consistent, governed, and scalable, so reports remain accurate and can adapt to business changes.
How long does it take to implement a data platform?
This varies depending on your company size, data complexity, and existing systems. A typical phased implementation might take 3 to 12 months, starting small and scaling gradually.
Is this only for large enterprises?
No. Smaller and mid-sized companies can also benefit from modern data platforms. Cloud technologies have made these platforms more accessible and cost-effective for all business sizes.
What is the difference between a data warehouse and a data lake?
A data warehouse stores structured data that’s cleaned and organized for reporting. A data lake stores raw or semi-structured data, often in larger volumes, and is more flexible for advanced analytics or AI use cases. Either can be used within a data platform.
Is AI necessary, or can we just focus on reporting?
You can start with reporting, but AI adds value over time by automating insights, identifying trends, and improving forecasting accuracy. It’s often part of a future-ready strategy.
Will this replace my finance team’s work?
No. A data platform enhances your team’s capabilities. It frees them from repetitive manual tasks, allowing them to focus on higher-value analysis and strategic decision-making.
What about data privacy and regulations like GDPR?
Modern data platforms support governance frameworks that help ensure compliance with regulations and industry-specific rules. Good governance is built in.
Will we need to hire data scientists or technical staff?
Not necessarily. Many modern platforms are built to be user-friendly for business teams and analysts. That said, having technical support or partnering with a consultancy can accelerate success.