How to Build the Perfect Analytics Stack?

How effective is your organization’s analytics stack? 

You need the right stack of technologies to maximize data ingestion, transformation, storage, and analysis. A less-effective analytics stack can result in poor data quality and often unusable data. 

When you build the perfect analytics stack, you’ll be able to more effectively manage your day-to-day operations and make better-informed strategic decisions.

Here are the components you’ll need to build a modern analytics stack.

Quick Takeaways

• An analytics stack integrates a variety of tools to transform raw data into actionable intelligence
• An analytics stack consists of three components: data ingestion and transformation, data warehouse, and data analytics
• The two approaches to data transformation are ETL (extract, transform, load) and ELT (extract, load, transform)
• An effective analytics stack ingests data from multiple sources, eliminates data silos, and enables real-time analytics

What’s an Analytics Stack?

An analytics stack is a set of tools that work together to transform raw data into usable intelligence. Like the similarly named technology stack, an analytics stack consists of multiple layers of technology, each performing its own role in the process. 

There are many types of technology stacks used in businesses today. For example, a tech stack for developing web applications includes front-end technologies such as HTML and CSS, along with back-end technologies such as databases, frameworks, and web servers. 

An analytics stack consists of numerous tools for data ingestion, data management, data monitoring, and data analysis. These tools come together in an integrated system with the goal of creating high-quality data that can be used to help run a business. 

Image Source

Why Your Business Needs an Analytics Stack?

An analytics stack provides valuable insights for businesses and organizations of all types and sizes. The information derived from a well-implemented data analytics stack helps managers make better day-to-day operational decisions and more informed long-term strategic decisions.

A data tech stack takes data from several different sources, converts it into a standardized format, cleans it of errors and inconsistencies, and provides in-depth analysis of that data. This enables businesses to trust the quality and accuracy of their data.

Without an efficient modern analytics stack, a business might have mountains of data but no effective way to use it. According to Gartner, bad or unusable data costs businesses $15 million per year. A properly built data analytics stack can turn raw data into actionable insights, making it far more valuable and usable.

Components of a Modern Analytics Stack

A modern analytics stack consists of a variety of different services employed across the entire data pipeline. Each services has its own function and cleanly integrates with the other technologies in the stack. 

The three main processes in a data stack include:

• Ingestion and transformation
• Storage
• Analytics

We’ll discuss how each of these processes work within the analytics stack.

Data Ingestion and Transformation

The goal of data ingestion and transformation is to take data from multiple sources and in various states and transform it into something usable. 

Ingesting data from multiple data silos is challenging and can involve various tools and services. Data can come from internal databases, including:

• Enterprise applications
• SaaS tools
• CRM data applications

Data can also come from external sources, including partner organizations and data purchased from third parties. Data can be either static or come into the system via real-time live streams.

All of this disparate data must be ingested and transformed into a usable format. In many cases, this involves fitting unstructured data into a standardized data structure. In addition, not all data will be of the same quality. Data quality and data monitoring tools are necessary to identify inaccurate or incomplete data and clean it so it can be reliably used. 

This process of ingestion and transformation can take two different forms:

Extract, Transform, Load (ETL) 

In the ETL model, data is extracted from its original source, transformed into a standardized format, and then loaded into centralized database. That is, data is transformed before it’s loaded into storage.

Image Source

Extract, Load, Transform (ELT) 

In the ELT model, data is extracted, loaded into storage, and then transformed before it’s ready for analysis. This approach allows for more free-form storage and saves the transformation until the data is ready to be accessed. Many data experts believe that ELT is the more flexible approach in that it allows unstructured or semi-structured data to be directly queried. 

Data Storage

The second part of analytics stack is storage, typically in a data warehouse. The most common approach to data storage is to combine data from all sources into a centralized repository. In the ETL model, data from all sources are transformed into a standardized format for storage. In the ELT model, raw data is stored in its original formats. 

It’s important for all the data in an organization to be centralized, rather than stored in departmental or location-based silos. When data is siloed, it’s harder to know where a particular piece of data is located, which makes it harder to access specific data or combine multiple pieces of data for more detailed analytics. For example, 47% of marketers say that difficult-to-access siloed data is their biggest challenge to gaining insights from their data. 

Eliminating data silos speeds up business processes, makes it easier to access and monitor data, and improves data quality. Centralizing data puts all data in the same place, equally accessible to all employees. A centralized data warehouse can be located either onsite or in the cloud, where it’s more accessible to remote workers.

Data Analytics

Analyzing the data collected and stored is at the top of the stack hierarchy because it’s the part of the stack that provides demonstrable value to the entire organization. 

Raw data alone is next to worthless until actionable information can be extracted from it. Modern analytics tools not only pull discrete data from data warehouse but also use machine learning and artificial intelligence technologies to identify patterns and trends within and across that data. 

Business intelligence tools analyze data for historical trends, provide real-time operational information, and provide predictive views of likely future developments. Data can be filtered for specific purposes and combined to provide more visibility into operations. The information gleaned from this analysis is valuable for maintaining day-to-day operations and informing longer-term strategic planning. 

How a Modern Analytics Stack Drives Better Decision-Making?

A modern analytics stack empowers businesses by providing real-time analytics capabilities, enabling them to make faster and more informed decisions. By integrating data from multiple sources and transforming it into actionable intelligence, organizations can gain real-time insights into operations, customer behavior, and market trends.

This agility allows businesses to respond quickly to emerging opportunities and challenges, reducing time spent on manual data processing. Furthermore, the ability to perform predictive analytics helps in strategic planning, enabling leaders to make data-driven decisions about the future, optimize resources, and reduce operational risks. With a modern analytics stack, companies can enhance operational efficiency and improve long-term business performance by leveraging the power of real-time data.

Challenges in Building a Data Analytics Stack (and Effective Solutions)

Implementing a data analytics stack comes with its own set of challenges, especially when it involves integrating multiple tools and systems. Some common issues businesses face include:

1. Data Silos: Data is often stored in separate systems or departments, making it difficult to access and analyze across the organization. To solve this, businesses can adopt tools that consolidate data into a centralized data warehouse, eliminating silos and providing a single source of truth.
2. Integration Problems: Different tools in the analytics stack may not always work together seamlessly. Ensuring compatibility between your data ingestion, transformation, storage, and analysis tools is crucial. Businesses can use integration platforms and adopt data tech stack solutions that are designed to work well together to reduce friction and improve data flow.
3. Data Quality Issues: Inaccurate or incomplete data can lead to faulty analysis. Implementing data quality management tools, like FirstEigen’s DataBuck, can automatically monitor and validate data, ensuring only high-quality data enters your analytics stack.

Building a Better Analytics Stack with DataBuck

Every organization has unique data needs so no two analytics stacks will be the same. You must assemble the specific data tools necessary to ingest, transform, store, and analyze your organization’s collection of data. 

FirstEigen’s DataBuck should be an essential component of your organization’s analytics stack. DataBuck is an autonomous data quality management solution that uses machine language and other advanced technologies to monitor data quality across the entire data pipeline. It can automatically validate thousands of data sets in just a few clicks — ensuring high quality data for your analytics stack. 

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

Check out these articles on Data Trustability, Observability & Data Quality Management-

• 6 Key Data Quality Metrics You Should Be Tracking
• How to Scale Your Data Quality Operations with AI and ML
• 12 Things You Can Do to Improve Data Quality
• How to Ensure Data Integrity During Cloud Migrations