Defining the Modern Data Stack and Its Key Benefits
Data holds the key to solving many of today’s business challenges. However, there’s a big gap between what businesses can do with data, and what they actually are doing. Up to 73% of data goes unused, with data silos, complexity, and quality issues being major hindrances to adopting data-driven practices. To address these challenges, businesses are turning to modern data stacks.
Modern data stacks are made up of an ecosystem of best-in-breed data tools that help businesses deal with the growing volume, velocity and variety of data, as well as the increasing demand for insights, scalability, and flexibility. These data stacks, working in alignment with people and processes, enable companies to collect and store data, analyze it, automate workflows, and scale to support the growing number of analytics use cases over time.
Here’s what businesses need to know about the components and benefits of the modern data stack.
What Is The Difference Between Traditional and Modern Data Stacks?
Traditional data stacks were built for an outmoded approach to data. They were highly customized and required extensive in-house resources to maintain. Because of these limitations, traditional data stacks could not handle the complexity or volume of data needed to support modern data and analytics.
Enter the modern data stack. Modern data stacks set themselves apart by being built for today’s data challenges and future data demands. There are a number of factors that separate traditional and modern data stacks, starting with their architecture. Traditional data stacks generally used on-premise technologies or leveraged cloud platforms inefficiently rather than utilizing modern, cloud-based platforms which offers scalability and cost-efficiency.
Within the traditional approach to data, centralized teams often controlled all aspects of the data lifecycle from sourcing, transformation, and distribution. This approach often led to bottlenecks, preventing users from obtaining their core data assets efficiently.
Modern data teams, however, are focused on curating high-quality, standardized data outputs which are readily available to business users. Business users will work in conjunction with the data team to produce high-value data products, which allows for an increased focus on the highest ROI generating activities.
Modern data stacks are also built to accommodate the reliability, flexibility, and scalability that companies need to improve their data capabilities.
- Streamlined Integration: Modern data stacks work well with sophisticated ETL or ELT tools. These tools come with pre-built connectors that streamline data ingestion without the need for costly, time-consuming development.
- Storage: Modern data stacks leverage data lakes and/or lake houses, which provide greater flexibility in data storage and analysis. Data lakes allow you to store raw data, and are built to provide flexible, scalable storage as your data volumes grow.
- Data Governance: Modern data stacks aim to shift left on governance. Instead of retroactively cataloging and inventorying metadata, modern data stacks incorporate governance and the definition of data from the start.
- Advanced Analytics: To help businesses unlock deeper insights and predictions, modern data stacks are built to accommodate the specialized needs of advanced tech like machine learning and artificial intelligence.
- Security and Compliance: Modern stacks often include advanced, integrated tools for data access, security, and compliance.
What Constitutes a Modern Data Stack?
A modern data stack is an ecosystem of tools that supports the speed and accuracy companies need to make data a competitive advantage. The components of a modern data stack can vary based on the needs of the organization, the types of data being used, and the specific use cases that the data stack is meant to support. Here are some elements that typically make up a modern data stack:
Data sources are the various databases, files, APIs, or applications where the original data is stored and generated. This can include a wide swath of technologies, such as relational databases, NoSQL databases, IoT sensors, customer relationship management(CRM) platforms, and data lakes.
Data Pipeline and Ingestion Tools
Pipelines manage the flow of data from its original source to its final destination. They handle processes such as extraction, transformation, and loading (ETL) or extraction, loading, and transformation (ELT). These typically include plug-and play connectors for common data formats and sources, which ease the ingestion process. Modern approaches favor reusable code and components when it comes to pipelining – much like an object oriented programming approach.
Data storage within a modern data platform usually favors a file based approach and aims to keep as few copies of the data as possible. Data is stored in flexible semi structured formats to allow easy modeling on demand as needed.
Data Governance and Security Tools
Data governance and security features help organizations ensure the availability, integrity, and confidentiality of their data. Modern data stacks integrate upfront definitions, policy enforcement, data lineage, and access management so that governance is incorporated in data processing mechanisms from the start.
Downstream Enablement Tools
Modern data stacks support a wide range of use cases, such as business intelligence and machine learning, that enable businesses to derive value from their data. The architecture of these platforms supports tasks such as feature engineering, model training, model validation, and model deployment.
Benefits of a Modern Data Stack
The modern data stack enables businesses to keep up with the velocity and scale of modern data analytics while also reducing costs and the burden on internal resources.
Improve Data Capabilities
Modern data stacks allow you to consolidate data from disparate sources into a single, accessible location. This allows users across the enterprise to explore, analyze, and report on data more easily.
Because data volumes are growing rapidly every year, modern data stacks are designed to handle massive amounts of data and can easily scale up or down depending on the needs of the organization. They also allow companies to easily adapt to changes in data variety or analytics needs. Typically, resources can be provisioned or de-provisioned as needed, which makes scaling more cost effective than it would be with a traditional approach.
Modern data analytics requires real-time insights. With modern data stacks, users can make timely, on-the-spot decisions with real-time data. In situations where speed and accuracy are critical, such as customer-facing interactions, these capabilities can be a key competitive differentiator.
As companies incorporate advanced analytics tools like machine learning and artificial intelligence, modern data analytics enables them to implement and use those tools effectively. This supports more accurate predictions, better understanding of customer behaviors, and enhanced operational efficiency.
Refine Data Processes
The most successful data-driven organizations are built on a foundation of practices that support data quality. Modern data stacks incorporate data governance and support compliance measures to ensure that data is accessible, accurate, consistent, and reliable. These practices also reduce risk by supporting processes to ensure compliance with regulations like HIPAA and General Data Protection Regulations (GDPR).
Cybersecurity is another source of risk, and hackers are as enterprising as ever. Modern data stacks prioritize data security, including features like encryption and access control mechanisms. This allows companies to protect sensitive data and control who can access networks and systems.
Empower Data-Driven Processes
To become a data-driven organization, businesses need to make accurate, up-to-date data available to users across the company. A modern data analytics stack can support self-service tools so users can self-serve without needing to rely heavily on IT teams or waste valuable time collecting and normalizing data.
Having a centralized and accessible data analytics stack also facilitates improved collaboration between departments. Each department can make use of the same, consistent data, leading to better cross-functional insights and decision-making. Promoting digital collaboration is especially vital now, since roughly two-thirds of employees who can work remotely are doing so at least some of the time.
Data-driven collaboration is also increasingly important for compliance. With the Securities Exchange Commission (SEC) and the European Union making progress to enact standards for Environmental, Sustainability, and Governance (ESG) compliance, companies need to ensure that they have the right data to plan, execute, and report on a wide variety of activities. Modern data analytics tools incorporate governance practices that allow companies to make data-driven decisions and share their results with stakeholders and regulators with confidence.
Modern Data Stack FAQs
What is the difference between a legacy data stack and modern data stack?
A legacy data stack is usually made up of a highly customized set of tools, which are typically housed on-premise. A modern data stack is made up of an ecosystem of cloud-based tools. They require less customization and allow companies to easily scale and adapt as their data needs change.
What are the key fundamentals of the modern data stack?
The key fundamentals of the modern data stack are:
- Leveraging cloud-based technologies that require minimal hands-on provisioning and maintenance
- Promoting scalability and agility, so companies can easily adapt as data needs change
- Incorporating data governance up front, as well as security and compliance practices to ensure data integrity, safety, and availability
What is the value of a modern data stack?
A modern data stack offers numerous benefits:
- Supports improved data capabilities and advanced analytics tools like machine learning and artificial intelligence
- Enables companies to scale and adapt their data infrastructure with ease
- Optimize costs by allowing companies to purchase only the resources they need, and making it easy to remove any tools they don’t need
What are some of the challenges in building a modern data stack?
Building a modern data stack can be challenging due to the number of resources available. Understanding what you need and implementing it correctly are essential to reaping the benefits of a modern data stack.
Also, it’s necessary to have the right people and processes in place to ensure that you’re properly leveraging every aspect of the data analytics stack.
How can machine learning and AI be integrated into a modern data stack?
AI and machine learning can generate automated analytics and insights and route them to users throughout the company. This enables you to make faster, more accurate decisions and collaborate more effectively.
How does a modern data stack contribute to data security?
A modern data stack can incorporate security protocols that protect sensitive data. This includes features like encryption and managing data access so users only see what they need to.
Explore the Capabilities of a Modern Data Stack
The demand for timely, accurate analytics has never been higher. As the amount of data businesses manage increases, the pressure to modernize will continue to grow. Adopting a modern data stack allows you to be more competitive and innovative. With the right ecosystem of data sources, advanced processing tools, robust storage options, and cutting-edge analytics capabilities, you can unlock the full power of your data.
Kenway can help you fuel data-driven decision-making and foster agility and adaptability with a modern data stack. We work with enterprises, including those in highly regulated industries, to ensure people, processes, and technology work in alignment to reap the full benefits of data.
Our data experts helped one asset management firm address the data silos, inefficient processes, and communication gaps that prevented it from gaining a 360-degree view of its customers. We assessed their current state and developed a modern data platform architecture that supports the firm’s current requirements and is flexible enough to adapt to future demands. Today, the firm’s employees can easily access all the information they need on prospective and current clients from a single source.
We can help you realize benefits like these for your business. To learn how Kenway can help you extract meaningful insights from your data, predict future trends, and create a competitive advantage, reach out to us for a consultation.