What MAkes us Different.
Within our services, we deliver projects in iterative sprints through an agile methodology to help customers focus on delivering benefits and a return on their investment.
We recruit teams of highly skilled data professionals from our carefully curated community. Their wide range of sector and technical experience and expertise means that The Data Analysis Bureau has a unique capability to find exactly the right skills to solve any given challenge for our clients.
We provide an end-to-end service, designed to take you seamlessly from ideation to product.
Click each phase below to find out more about our service and key deliverables.
Explore Opportunities & The Art Of The Possible
Deep-dive potential use cases and opportunities; exploring the data available and resource required to support your business, and design solutions and an analytical roadmap to deliver your solution.
Use case reports or designs based on the focus of your project; playback of selected problems & insights, use case or solution feasibility and approach, as well as indicative timelines for PoC.
Delivery: 1-2 Weeks
Our Aim Is To Help You
move your data from
IDEA TO IMPLEMENTATION.
We have designed a framework to help you easily navigate our services and select what you need, when you need it.
We create a bespoke package for each client to reflect their needs. We aim to transform the way each business works with the growing volume of data and provide an end-to-end data science service designed to accelerate your ability to make data-driven decisions.
BUILD, TEST & RELEASE
We help formulate business questions and explore and audit client data. We help build data products, test and release them into the wild, adopting a lean start-up philosophy to ensure the success of client data projects.
SUPPORT THAT COUNTS
We provide on-going expertise to run, maintain and enhance data services.
we are trusted by
international partners & Clients.
interested in getting started? speak with our
Machine Learning is all about understanding data, and what can be taught under this assumption. This post introduces supervised learning vs unsupervised learning differences by taking the data side, which is often disregarded in favor of modelling considerations.