How to Build a Data Stack That Ensures Data Quality

How does your organization manage the volumes of data ingested each day? The most effective way to ingest and manage large amounts of data is with a modern data stack. To build a data stack that meets your business needs, follow the five essential steps to ensuring the highest quality, most useable data possible outlined in this article.

Key Takeaways

• A data stack is a set of tools and technologies used to collect and transform large volumes of data.
• There are five primary components of a modern data stack: data sources, data ingestion, data warehousing, data transformation, and data analysis.
• To build an effective data stack, you need to identify your data sources, establish a data warehouse, choose data ingestion tools, determine a data modeling process, and provide robust reporting and analytics.

What is a Data Stack?

A data stack is a collection of technologies that collect, transform, and manage data for use within an organization. As businesses generate vast amounts of data daily, managing this information efficiently becomes critical. Experts at the University of Tennessee estimate that 2.5 quintillion bytes of data are created every day, a figure expected to reach 44 zettabytes per day by 2025. Unfortunately, much of this data remains underutilized — Seagate reports that 68% of enterprise data goes unused.

A modern data stack is designed to ingest data from various internal and external sources, transform it into a usable format, and equip users with the tools needed for extraction and analysis. An effective cloud-based data stack enables organizations to make data-driven decisions, from daily operations to long-term strategic planning.

When properly implemented, a data stack not only makes large volumes of data manageable but also democratizes its access. By enabling more users to interact with data, a fully functional data stack enhances both operational efficiency and analytical capabilities within an organization.

Understanding the Difference: Data Platform vs. Data Stack

The terms “data platform” and “data stack” might seem interchangeable, but they represent distinct concepts in the realm of data management and analysis.

• Data Platform: This is a holistic ecosystem designed to manage all aspects of data within an organization. It provides comprehensive capabilities for storing, processing, and analyzing data. A data platform typically includes features such as data warehousing, data lakes, integration tools, and advanced analytics. It functions as a unified environment where businesses can seamlessly handle all stages of data usage, from collection to actionable insights.
• Data Stack: On the other hand, a data stack is more like a toolkit comprised of various individual components. It involves the selection and integration of multiple specialized tools to address specific needs of data processing. Starting from data ingestion tools, such as Apache Kafka, moving to transformation tools like dbt, and ending with visualization software like Tableau or Power BI—each component plays a crucial role. The data stack is customized to fit the specific workflows and requirements of the organization, allowing for flexibility and targeted functionality.

In essence, while a data platform is an all-encompassing solution providing a seamless environment, a data stack is a collection of modular tools, each serving a particular purpose within the data lifecycle. Understanding these differences can help organizations choose the set-up that best meets their data needs and business goals.

Core Components of a Modern Data Stack

A modern data stack has five main components: 

• Data sources. Most data stacks incorporate data from multiple sources. These sources can be both internal and external, including customer databases, acquired data, social media, and various event streams.
• Data ingestion. This part of the data stack ingests the data from the multiple sources. 
• Data warehousing. The data warehouse stores the ingested data. Most modern data stacks use cloud-based data warehouses, although they can also be based on-premises on a network server. 
• Data transformation. This part of the stack transforms the raw data into a more usable format. Data transformation typically includes data quality monitoring and cleaning. 
• Data analytics. The final component of the data stack enables users to analyze the data, run reports, and obtain actionable insights. 

Why Start with Fewer Components in Your Data Stack?

When businesses embark on building a data stack, it’s tempting to incorporate multiple layers right from the start. After all, more components can mean more capabilities. However, starting with a simpler setup can prove to be far more beneficial in the long run.

Simplified Data Management:

Starting with a streamlined setup focusing on core components like data source, integration, storage, and analytics helps prevent unnecessary complexity. By initially limiting the number of layers, organizations can effectively manage and understand their data without overwhelming their resources.

Easier Troubleshooting and Maintenance:

Fewer components mean fewer points of failure. This simplicity translates to easier troubleshooting and maintenance, ensuring that any issues can be swiftly identified and resolved. A complex setup, in contrast, can make it difficult to pinpoint problems, leading to longer downtimes.

Cost Effectiveness:

Building and maintaining an intricate data stack requires significant investment not only in technology but also in skilled personnel. By starting small, businesses can allocate their budgets more efficiently, gradually expanding as they reap the benefits of their current setup.

Scalability and Flexibility:

A basic data stack provides a robust foundation that can be expanded gradually. This approach allows businesses to scale their data operations in line with growth and evolving needs, ensuring the system remains flexible and adaptable over time.

Focused Learning and Improvement:

A simpler data stack enables teams to gain a deeper understanding of each component. This focused learning paves the way for continuous improvement and optimization, creating a strong data-driven culture within the organization.

Ultimately, initiating your data stack journey with fewer components affords businesses the opportunity to build a scalable, cost-effective, and manageable system. By gradually layering on additional components, companies can avoid unnecessary complexities while gaining actionable insights that drive growth.

How to Build a Data Stack in 5 Easy Steps?

How can your company build a data stack that works for your specific needs? While every situation is unique, there are five basic steps any company, regardless of type or size, can follow to build an effective and efficient data stack. 

Step #1: Identify Your Data Sources

The first step in building a data stack is to identify your data sources. You need to know where your data comes from before you can determine how to store and manage it. 

Depending on your organization, you may source data from:

• Office applications, such as Microsoft Word and Excel
• Customer relationship management (CRM) applications, such as Salesforce
• Internal company databases
• External databases
• Social media
• Internet of things (IoT) devices

For each of these sources, you need to consider various characteristics of the data, such as type of data, freshness, accuracy, and format. These criteria determine how the data needs to be ingested, stored, and transformed. 

Step #2: Establish a Data Warehouse

The next step in building a data stack is figuring out where and how you’ll store the ingested data. For most larger organizations, the logical choice is a data warehouse that can handle large volumes of data. 

You also need to determine what type of data warehouse to establish. You can build a data warehouse on-premises, utilizing your existing network servers, or you can base it in the cloud. Cloud data warehouses have several inherent advantages over on-premises warehouses:

• Reliability: Most cloud-based data warehouses are easier to maintain and have higher uptime than traditional server solutions. 
• Accessibility: Any authorized user with an Internet connection can access data stored in the cloud, making cloud-based solutions ideal for today’s growing remote workforce. 
• Scalability: Expanding an on-premises data warehouse means investing in new servers and physical infrastructure. Conversely, expanding a cloud-based solution requires only the purchase of more storage space in the cloud. This means a cloud-based data warehouse can easily scale as your business and data processing needs grow.
• Security: Cloud-based data warehouse solutions have robust security measures to protect against data breaches, ransomware, and other cyberattacks. 
• Affordability: Because you don’t have to invest in expensive physical infrastructure, cloud-based data warehouses have lower up-front costs as well as lower ongoing maintenance expenses. 

Whichever type of data warehouse you choose, make sure it can handle your current and expected data volume, is easily manageable, and is secure against cyber threats.  

Step #3: Choose Your Data Ingestion Tools

Next, you need to determine what tool(s) you’ll use to ingest data from your chosen sources into your data warehouse. Your choice of data ingestion tools should reflect the types of data you’ll be ingesting, as well as how those tools integrate with your chosen data warehouse. Look for tools that:

• Can ingest both batched and streamed data.
• Can handle various types of data, including unstructured data.
• Prioritize important data.
• Eliminate duplicates from redundant data streams.
• Offer fast and efficient ingestion. 
• Have low maintenance needs and costs.
• Can work automatically with minimal human intervention.

Step #4: Determine a Data Modeling Process

Data modeling is part of the data transformation process. Data modeling tools transform raw data into usable formats while monitoring data quality and managing poor-quality data. To choose the right data modeling process, you need to consider how you plan to store your data, what data formats are acceptable, and how that data will be accessed and used.

Step #5: Provide Robust Data Analysis

The final step in building a modern data stack is designing a robust data analysis process. This step should be driven by the data’s users – data analysts as well as everyday business users. You need to know what insights users expect from the data and how they can best extract that information. This typically means creating a mix of reports and real-time dashboards to help users visualize and dig deeper into the data you manage. Look for analytics tools that are:

• Easy to use
• Customizable
• Adaptable for both casual and advanced users
• Scalable 
• Capable of real-time reporting and analysis
• Produce easily sharable results

By following these steps, you can create a data stack that meets your organization’s current needs and can evolve as your needs change in the future.

Future Trends in Data Stacks

As the digital landscape evolves, data stacks must adapt to meet new demands and technologies. Here are key trends shaping the future of modern data stacks:

1. Emerging Technologies:
   • Serverless Architecture: Reduces infrastructure management, enabling teams to focus on development.
   • Data Mesh: Decentralizes data ownership for better scalability and autonomy.
   • Edge Computing: Processes data closer to the source, enhancing real-time analytics.
2. Integration with AI/ML:
   • Automated Data Quality: AI tools identify anomalies, ensuring real-time data accuracy.
   • Predictive Analytics: Machine learning forecasts trends to inform decisions.
   • Natural Language Processing (NLP): Simplifies data queries through conversational interfaces.
3. Scalability and Flexibility:
   • Cloud-Native Solutions: Enable dynamic scaling based on demand.
   • Modular Architecture: Supports easy integration of new tools as needs evolve.
   • Multi-Cloud Strategies: Optimize costs and mitigate risks by distributing data across cloud providers.

Why Regularly Reviewing and Updating Your Data Stack is Crucial

In the ever-evolving digital landscape, maintaining a competitive edge requires not just collecting data but also innovatively managing it. Here’s why revisiting your data stack should be a regular practice:

1. Adapt to Dynamic Data Needs: As businesses grow, their data needs shift. New types of data, such as unstructured data, become relevant, demanding more sophisticated tools and analytics capabilities.
2. Optimize Performance: Through regular stack analysis, you can spot performance bottlenecks and address them promptly. This ensures your data processes run smoothly and efficiently, reducing downtime and increasing productivity.
3. Incorporate Cutting-edge Technologies: Constant technological advancements mean that holding on to outdated tools can put you at a disadvantage. Modernizing your data stack with the latest AI and machine learning tools can enable better insights and decision-making.
4. Ensure Data Quality and Compliance: Regular reviews allow you to maintain high data quality and ensure that your practices remain compliant with evolving data protection regulations.
5. Scalability for Growth: As your business expands, so does your data volume. Upgrading tools like your data warehouse or business intelligence software is essential to handle increased loads effectively, ensuring your infrastructure supports growth rather than hindering it.

Staying proactive in managing your data stack not only keeps your operations efficient but also ensures you leverage data-driven insights, keeping your business ahead in the competitive marketplace.

Improve the Quality of Your Data Stack with First Eigen’s DataBuck

First Eigen’s DataBuck uses the latest technologies, including artificial intelligence and machine learning, to ensure that the data flowing into and stored within your data stack is the highest possible quality. DataBuck not only automates data quality monitoring, but it also helps reduce errors, increase processing speed, and reduce costs. 

Contact FirstEigen today to learn more about data quality and data stacks.

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