DATA ENGINEERING & MANAGEMENT

Prior to building any of the exciting data visualisation tools or overlaying any Machine Learning or Data Science we like to get the house in order first. 

By that, we mean ensuring the data infrastructure and pipelines which enable analytics and data science to occur are clean, well integrated, transformed and arranged in such a way that ensures scalability, quick-processing and automation. 

It’s like building a strong foundation for a house you’d like to live in 100 years from now…and in the case of big data – a house 100 elephants would like to live in 100 years from now…!

This is often step 1 for companies who have multiple legacy systems which all collect data and don’t talk to one another. 

It’s a way of arranging data under one common language that the computer understands and can make sense of and organise quickly as it flows through. 

Data engineers also have a strong view to effective architecture of data warehouses; where the data is stored; which lay the groundwork for future pathways of Data Science, AI and Machine Learning projects.

We have seen a great deal of success in implementing the Data Vault Methodology; which blends traditional best practice modelling methods with progressive methods implemented to adapt to the needs of today’s enterprise environments. 

Data Vault solves the challenge inherent with larger and larger data sets and unstructured data inherent with BigData and allows for rapid integration of new business metrics to provide fast ROI on the delivery and organisation of new data.

We are now building scalable, sustainable and resilient environments to adapt and flex to the ever changing size and variance of information which is helping keep our customer ahead of the curve!