Big data has become so commonplace that hardly anyone bothers to call it “big data” anymore.
The need to wrangle a constant stream of information from multiple sources, in multiple formats, and of varying degrees of quality and accuracy is now a daily challenge for most organizations.
Sure, all that data holds the key to better business decisions and AI/machine learning innovations… but first it has to be ready for the task.
Data engineering fills the gap between raw data and the sophisticated analytics that can help your business achieve its goals.
When data is properly engineered, data scientists, decision makers and others in the organization can access the data they need quickly and utilize it with total confidence—and AI/machine learning platforms have a reliable source of information from which they can continuously learn and improve.
At Logic20/20, data engineering isn't just about cleaning and moving data—it's about making a positive impact on your business.
We make sure that we thoroughly understand your needs over the short and long term to prevent over-engineering.
Based on your specific needs, we’ll make sure we pick the right type of database (SQL, AzureSQL, Cosmos, etc.) and approach (metadata ETL framework, config-based, etc).
The engineering team checks in with the business regularly to ensure the design continues to meet your needs.