What is the BizCubed Data Engineering Methodology?

One of the big challenges of the modern data ecosystem is that it is in a constant state of change – from one paradigm to another, one platform to the next, tool after tool, day in and day out. Despite these fluctuations, the business users of data must be able to have trust and confidence in data accessibility and in the data sets themselves. Building the necessary reliable, consistent delivery patterns to achieve that can be difficult.

BizCubed has delivered thousands of data projects over the course of its 17-year history. One of the most valuable lessons we have learned in that time is that organisations don’t need another data project; they need consistent, reliable, and ongoing *operational capability*. This is easier said than done.

Getting executive buy-in (for what they may see as an expensive strategy) and driving convergence between data teams and business teams requires more than just an aspirational roadmap. Data teams must identify a clear contribution of value that matters to the business, and then actually deliver on the promise of data excellence in a robust and repeatable way.

Delivering a reliably consistent data operation in the context of an ever-changing environment requires a strong conceptual anchor point – something that doesn’t change, regardless of the team or the tools used to deploy it. That’s why we developed our data engineering methodology.

The BizCubed Data Engineering Methodology represents the individual components that any mature data operation must include to remain robust, resilient, and reliable on a daily basis.

Our approach to data delivery is:

• Disciplined
• Rigorous
• Methodical
• Repeatable
• Proven

By applying our methodology we are able to create uncommon efficiencies, free up capacity for innovation and growth, and drive alignment between business teams and tech teams. Taken together, these serve to accelerate our customers on their journey into the future state – whatever they might design that to be.

Is your organisation ready to adopt a scalable, repeatable approach to data engineering?
Contact us now