With the need for digitization consistently accelerating data creation, businesses create and manage enormous quantities of data every day. Modern data analytics and management solutions are quickly becoming essential to make sense of all this information for business purposes.

Read on to learn what a modern data platform is and how you can modernize your company’s technology stack.

What Is the Modern Data Stack?

Simply put, the modern data stack is a reimagining of the traditional information flow. This process comprises three steps:

Creation: New information is created or captured — for example, an author writes a book or a photographer snaps a picture.
Collection and organization: Unstructured information is essentially unusable. Data stack technologies provide a system for structuring and organizing your data to make it suitable for analytics. Some highly regulated industries, such as health care and finance, are in the process of standardizing their data storage and organization processes.
Analysis: Once your data is in a format you can use, you can examine it and apply that information to create real value for your business. New technologies like artificial intelligence (AI) and machine learning (ML) create innovative new ways to turn data into revenue.

Many companies lack the resources and expertise to process and organize data themselves. However, the modern data stack makes data accessible to anyone within your organization. By quickly transforming and analyzing their data to uncover new opportunities, the modern data stack enables companies to be proactive and get ahead of the competition.

What Makes Up the Data Stack?

The modern data stack includes tools for every aspect of data management, from creation to storage and transformation. These tools are modular in design, enabling you to create a tailored system that meets your business’s unique needs.

1. Common Data Sources

Business data comes from a wide variety of sources, including:

Sensors: As high-quality sensors have become more affordable, more manufacturers are including them in their devices, from cell phones to construction equipment. These sensors are constantly collecting data on their surroundings and sending them to your system.
Event streams: An event is a user interaction you can use for analytical purposes. For example, your website could record any time a user clicks on a specific product page and you could analyze this data to determine which product is most popular.
SaaS programs: Many companies leverage software as a service (SaaS) offerings to streamline their business processes. These programs create and store enormous quantities of data. For example, you might use an SaaS customer relationship management (CRM) system to onboard and interact with customers.

Your system ingests data through a data pipeline — a complex transport structure of various hardware, software and networking components. A well-built data pipeline is critical for efficient data processes regardless of the other components you have in your technology stack.

2. Data Integration Platforms

Data integration is the process of organizing all your data in one centralized location, such as a data warehouse, a database or a data lake. Many companies utilize a combination of the three. Here’s a quick explanation of each one:

Data warehouse: A data warehouse is a centralized storage location used to house structured, curated data for fast analytics.
Data lake: A data lake is another centralized data repository, but unlike a warehouse, it stores all types of data, including raw and unstructured data.
Database: A database is where companies store up-to-date transactional and operational data. In other words, databases house the data that powers business operations.

Most companies handle the integration process through ELT, which involves extracting and loading data into one of the repositories above for transformation. This process usually requires an SaaS data integration platform that facilitates easy extraction and loading.

3. Data Storage and Querying

Before you can use your data, you need a place to store it, making a cloud-based repository a crucial part of the modern analytics stack. Newer data products utilize AI and ML technologies to automate important data management functions, such as security, governance and organization.

A query is a request for information from a data repository. Modern data stack programs enable anyone within your organization to query your databases directly, increasing your organization’s efficiency and agility.

4. Data Transformation

Data transformation is also known as extract-transform-load (ETL), which is the process of preparing data for business use. Here’s a quick breakdown of how each stage changes data:

Extract: This process involves identifying and pulling relevant data from your sources and moving it through the pipeline to a data repository.
Transform: The transformation stage involves cleaning, structuring and reformatting data as necessary to match its destination.
Load: Data loads into its final destination for use in business intelligence (BI) or analytics operations. This final destination may be a data warehouse, a data lake or an analytics platform, depending on what you plan to do with it.

A modern data analytics platform accelerates this process by allowing you to transform your data while it’s still in the pipeline, enabling more efficient analysis and action.

5. Data Governance and Monitoring

Data governance involves managing the availability, security, integrity and usability of all the data in your system. Good data governance practices ensure your data is accurate, making informed business decisions possible.

Some examples of data governance functions include:

Pipeline testing and monitoring: This step evaluates data during the ingestion process, preventing bad data from ending up in your system.
Metrics layers: By defining key performance metrics, such as churn rate or active users per month, you create a system for understanding your data.
Data cataloging: When your data is properly documented, you enable people within your organization to quickly find the tables they need for analysis.
Privacy and access controls: Data security is critical for every business, so it’s important to choose a platform that lets you easily control who has access to what data.

What Makes a Modern Data Stack Modern?

The modern data stack is made up of cloud-native applications, which is its primary defining factor. SaaS offerings such as modern data analytics platforms have become popular because they are hosted in the cloud, meaning your data is hosted on secure offsite servers.

Cloud-based data stack components are both scalable and elastic, making them advantageous for growing businesses in fast-paced industries. Here’s the difference between the terms:

Scalable: You can upgrade your data analytics platform to meet your changing needs as your organization grows.
Elastic: You can quickly downsize or upgrade to adapt to dynamic changes in short-term organizational demands.

Additionally, integrations and modular design mean each component within the stack is like a piece of a puzzle. The modern data stack is highly customizable, as you can add whatever tools make the most sense for your business. You can configure your stack to fit seamlessly together with little technical knowledge.

The Modern Data Stack vs. the Legacy Data Stack

The legacy data stack is built on traditional IT infrastructure, such as the devices you’d find in a traditional data center. On the other hand, modern data platforms are cloud-based, which removes some of the limitations of conventional systems.

Here’s how the modern data stack transforms the traditional data flow:

Accessibility: Since modern data stack components are cloud-based, your people can access your data anywhere, from any device, as long as they have an internet connection. Centralized cloud repositories provide a single source of truth for all your data, eliminating data silos and improving data quality. Unlike traditional infrastructure, these systems require minimal technical configuration on your end so that you can set them up quickly without a team of IT professionals.
Scalability: Most modern data platforms are cloud-based SaaS applications that allow you to scale your coverage as your organization grows. In contrast, scaling with a legacy system requires you to purchase new hardware and expand your IT department, which is costly and often results in wasted server space.
Efficiency: Legacy systems are highly complex and challenging for anyone with limited IT experience, making analytics an inefficient, overly complicated process. Because modern data systems are so accessible and user-friendly, your business teams can analyze your data quickly without needing to rely on the IT department. This efficiency benefits more than just your business teams — your IT department also gains more time to focus on urgent tasks.
Agility: The ability to quickly analyze critical business data is more than just a competitive advantage these days — for many companies, it’s the difference between survival and failure. While hardware restrictions limit legacy data technologies, cloud-based modern data systems support business agility and innovation by making iterative processes more affordable and efficient.

Benefits of a Modern Data Stack

The following are some of the most notable modern data stack benefits for companies across various industries:

A business-focused model: The modern data stack enables you to refocus your organization’s time and maximize the value of your data by dramatically reducing the time it takes to extract and load that data. This restructuring enables you to rapidly uncover key insights and turn them into action.
Enhance analytics through AI and BI: Modern data analytics platforms use AI and BI technologies to rapidly process and draw insights from various data sources.
Streamline data governance: Data governance ensures your data is always accurate and up to date. The modern data stack makes data governance easier through automated pipeline testing and monitoring that can catch and eliminate data errors before they leave your pipeline.
Increase flexibility: A truly modern data solution embeds the analytics process in your data pipeline, making your business more flexible by enabling you to pull insights from your data before it even hits your warehouse. Getting the information you need to make business decisions faster gives you a competitive edge.
Try before you buy: Most cloud-based data stack tools are SaaS products that offer free trials or demos that let you test out a solution you’re interested in before you make the investment.