Data — and data analytics — is the lifeblood of a modern business. Customers volunteer data, sales teams gather data, and research departments purchase data. Data from customer and operational touch-points is piling up everywhere and this information has to be securely collected, managed efficiently, and analyzed quickly for companies to prosper.

Data analytics architecture is the process of standardizing the systems, tools, and protocols organizations use to collect, store, transform, analyze, distribute, and use data. According to Talend, a leading big data, cloud storage, data integration, data preparation, and enterprise application provider, the goal of data architecture is to deliver relevant data to people who need it, when they need it, and help them make sense of it.

As companies seek to expand their analytics capabilities and strategy-making abilities, it’s important for them to realize that the key to a successful analytic strategy is a strong analytics architecture. An organization’s data analytics strategy determines the tools and underlying assets or resources they need to monitor and maintain those tools.

Why is an analytics stack so important? A well-designed and carefully implemented business analytics stack offers near real-time reporting; automation of clean, accurate data that can be queried by various users; maintenance and accessibility of historical data for reporting purposes; business agility; improved decision-making capabilities, and more.

Data Architecture Solutions: an Overview

Although many companies are moving to the cloud, a number of different options exist for companies looking to modernize their reporting and analytics. They can use open source tools, an integrated selection of tools, a unified analytics platform, or cloud-only architecture. Here’s a quick look at the pros and cons of each solution.

Open Source Tools: Pros and Cons

Open source tools are free and can be quite sophisticated, but they require highly technical resources to construct them. Plus, tech teams will invest a huge amount of time and effort in standardizing open source tools and preparing them for scalability. They can also open up a business to vulnerabilities, they’re not very user-friendly, and there’s minimal personalized support. Open source architecture tools can be a very difficult path to follow.

Integrated Selection of Tools: Pros and Cons

An integrated selection of tools is costly and requires careful expert integration. However, they can provide flexibility in customizing solutions to your specific business needs and offer strong ecosystems for support and knowledge.

Unified Analytics Platform: Pros and Cons

A unified analytics platform is a less expensive option than an integrated tools solution, but this path requires a knowledgeable architect to design the solution. Customization is limited to what the platform offers but off-the-shelf options are often extremely robust.

Cloud-only Architecture: Pros and Cons

Increasingly, businesses — small, mid-size, and enterprise — are moving to cloud-only architecture that offers no-code functionality for users and the flexibility to fit business needs through customization options. Some tools that are popular right now include Snowflake, DOMO, Fivetran, Boomi, and Alteryx.

Best Practices in Data Architecture

Today, most companies select their own tools within the analytics stack and optimize each solution to fit unique business needs and create a foundation for scalability. For the majority of companies — those who select cloud-only architecture solutions, modularity is the key to success.

In the world of data architecture, modularity means that each selected tool performs a specific function exceptionally well. There’s no overlapping functionality when modularity is the focus. This ensures that one set of tools can be replaced by another without interrupting analytics and reporting services. Modularity also helps to make sure that the tools actually meet the business’s requirements and aren’t merely repurposed to fit the solution!

Which Tools Work Best for Your Stack?

Here are some questions business leaders and stakeholders can ask to determine which set of architectural tools and which analytics tools would work best in their stack:

  • What is the current technical knowledge of business intelligence (BI) users and analysts?
  • Are they able to perform complex SQL queries?
  • Are there any data teams present or engineers who handle data-related tasks or any data-system in use?
  • What is the volume of data?
  • What are the various sources and formats for the data?
  • What is the frequency for data ingestion?
  • Is there already a BI tool already in-use?
  • If so, what’s working and what’s not?
  • Are BI users expected to know how to create calculated fields for precise reporting?
  • Would access to historical data inform future decisions?

How MorganFranklin Can Help

MorganFranklin’s business and technology transformation experts can assess your current analytics stack and create remediation strategy for pain points; create an analytics roadmap to help inform analytics strategy and decisions; assist with vendor and tool selection; implement an entire analytics stack or support the integration of individual components like data warehousing, BI tools, or ETL tools. We can also help businesses redesign reporting cycles to reduce inefficiencies; explore data, data architecture and provide recommendations; build a data-wiki, and automate ETL/ELT jobs through the implementation of a scheduler.

Contact us today to start building a data architecture that supports your analytic strategy goals. Let’s get the conversation started.

Authored by: Pierre Stricker

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