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Everything you need to know about data analytics stacks with real examples.
Building a modern analytics stack is one of the most critical things for a business. It provides the tools to collect, process, and analyze data to make smarter data-driven decisions than ever before.
In this blog, you'll explore the importance of building a modern analytics stack. We will answer the questions, such as—what is an analytics pipeline, what is an analytics stack, and how it all works. You will also go through its significance and the stages of building it. You will understand how to capture, store, and process your data, including how to analyze and visualize it.
Let's start with the definition. Data analytics is analyzing data to gain insight into business processes and operations. It can be used by businesses to improve their efficiency and effectiveness.
Data analytics is essential for every business and is the key to obtaining a competitive advantage. It helps you understand your business, your product, and your customers to ultimately provide a better service/experience. As data workloads move to the cloud and modern organizations deploy a suite of apps to perform various duties across their business functions, it's becoming increasingly hard and complicated to process data to generate valuable business insights. An average organisation has more than 200 SaaS applications and many hundreds internal data sources they need to keep track of.
The marketing team may utilize a combination of HubSpot and Marketo for marketing automation; the sales team, Salesforce, and Apollo to manage prospects; and the customer support team, Intercom, to manage customer requests and troubleshoot issues. This results in data fragmentation across multiple data sources with no single source of truth—resulting in inaccurate insights and reporting.
Data silos can make even fundamental business insights hard to obtain. An analytics stack can help you consolidate and refine data from various data sources and then transform to create meaningful insights. This typically leads to increased data access, better governance, and fast analysis path to your business insights. It also helps ensure consistent data quality and provide standardization to your reporting layer, which is crucial for reliable business insights.
Now that you know what an analytics stack is, let's find out how it works. Though the specifics of the stack may differ across different businesses, the basic workings are similar and composed of the following stages:
But just putting the stages one after the other does not guarantee success. In the following sections, we will explain to you the ways in which you can ensure you are building a data analytics stack and building a good one.
This is the first stage of successful analytics. In reality, capturing data is much more difficult than it sounds. There is just so much data to capture, it is so easy to get drowned or, worse, capture the data that give incomplete or incorrect insights.
If you're reading this article, you've probably already decided that data analytics is important to your organization. Now, it's time to start thinking about where all this data will be housed. The first step in building a strong data analytics stack is choosing a data warehouse system that can handle your unique needs, fit your cost profile and allow for easy access to all the information being collected.
There are many factors to consider when choosing a modern cloud data warehouse. Some of which are:
A modern data analytics stack has at its core cloud-based solutions that allow it to easily scale storage and compute to support analytics use cases. The market today offers a variety of solutions when choosing a cloud data warehouse to store and transform data with different functionalities and more flexibility. The most popular solutions in the market include Snowflake, Google BigQuery, Amazon Redshift, and Firebolt.
Data processing involves extracting data from the source and transforming it into a format that is more suitable for analysis. The industry has now fully adopted an ELT (Extract, Load, Transform) workflow, where data is first extracted and then loaded into a data warehouse to be transformed. The process consists of two well-defined steps:
Once data has been processed, the next step is to present it in the form of a report or visualization to draw insights from. This visualization is done using BI (Business Intelligence) dashboards, such as Looker, Tableau, Preset, and Mode. These dashboards can help you make informed decisions, predict outcomes, and make recommendations. This stage of your data stack is one of the most critical as it is the point of contact between data analyst teams and business functions across the organization.
Reverse ETL solutions, such as Census and Hightouch, offer out-of-the-box connectors to various systems, such as Salesforce, Hubspot, and Intercom. Adding this component to your data analytics pipeline allows you to push and sync transformed data back into those SaaS (Software as a Service) applications' standard and custom fields. By pushing data back into these third-party systems, it operationalizes or activates data back into the organization. This gives sales, marketing, and operations teams direct access to enriched data daily. Some common use cases include:
By implementing reverse ETL tools in your data stack, you can push data directly into the SaaS tools used by line of business users while also streamlining automation and eliminating manual export and import using CSV (Comma-Separated Values) in your third-party applications.
Building your data analytics stack requires an integrated approach. The steps in the stack include capturing, storing, and processing data, visualizing it, and then taking action based on what you learn.
To get you started on this journey, here are some basic steps:
These three vital processes form the foundation upon which everything else rests!
Transitioning from dependence on siloed and automated applications to building your own modern data analytics stack can definitely be an uphill struggle. However, because we heavily rely on data to make business decisions, building a modern analytics stack is now a necessity rather than good to have. Furthermore, a well-built stack can evolve with your business—providing you with meaningful insights that you can act upon. However, it's also no secret that a data analytics stack is made of different components, with many competing tools to choose from. It's important to embrace good engineering practices to build your data stack upon a set of tools that can provide scalability, reduce switching cost while maintaining control over the challenges of gluing many of these tools together.
In the next blog of this series, you will learn how to build an analytics stack by looking at the architecture of a modern data stack and its components. We will also evaluate some tools based on their ease of use, integration capabilities, community, documentation, and pricing.