Question 1

What is data architecture?

Accepted Answer

It’s kind of like building a house: you need your blueprint to know what you need to build, and how all the pieces will fit together. Many times when software is acquired, it's a hammer looking for a nail, when you need to approach data architecture like a general contractor.

Data architecture is the structure and standardization around how organizations collect, store, transform, and use data across their business. A strong data architecture helps organizations make better use of their data by understanding where it is, how it moves, and when it needs to be delivered to users.

Question 2

What is data engineering?

Accepted Answer

If data architecture is your blueprint, data engineering is the building of the house itself: from studs to plumbing. Data engineering is the building out of technical infrastructure within your data architecture. Whether in batch or real-time processing, data engineering is often bespoke to each organization's data needs, so having the right partner is critical for project success.

Often included in data engineering is API integration, pipeline development, big data applications and platforms, data integration solutions, and data lakes or warehouses. Software engineers, data architects, and data scientists are involved in building out these components.

Question 3

Why would I choose RevUnit for my data project?

Accepted Answer

We’ve worked on data projects at some of the world’s largest companies: Walmart, Unigroup, Tyson, J.B. Hunt, and Hyatt (just to name a few).
We take an agile approach to data architecture and engineering.
Our team of experts know how to tie data to real business value.
We use a teach-and-do model that will upskill your internal team in best practices while we work alongside you.

Question 4

What challenges can better data architecture and engineering solve for me?

Accepted Answer

Your business can’t afford to not take full advantage of your owned data. We can solve for:

Data taking wayyy too long to get
Systems not “talking” and data not shared
Reporting dashboards that are confusing and don’t affect change
Technology that users dread, or create workarounds to avoid
Integrating / embedding insights into existing tools
Getting you up to speed so you can start predictive analytics

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