Services

Analytics

We support our customers across a full range of internal and external analytics use cases. Leveraging over 25 years of experience and cross-industry knowledge to cover key components throughout the end-to-end Business Intelligence implementation journey.

Our passion and goal is to deliver a robust, scalable and innovative Business Intelligence environment that delivers strategic outcomes, meaningful insights and accelerates business performance.

Our Services Include:

Data Platforms

We help you define your entire data platform architecture – from business discovery through to implementation and ongoing support:

• Business discovery – understanding your current architecture and business needs
• Technical architecture – designing the architecture based on your needs around data sources, tool sets and desired outcomes
• Implementation:
  
  • Real-time replication/streaming or batch processing of data sources
  • Integration services to create data warehouses or data/delta lakes
    
  • Master Data Management and Data Quality – making sure  your data is cleaned, transformed and catalogued to be consistent, reliable and analytics-ready
• Enhancements and continuous improvement process through our Managed Service offering

Managed Services

Our Managed Service framework is designed to manage, support and maintain our customers’ Cloud Data Platform, Business Intelligence Environment and AI/ML solutions.

Our tailored approach and partnership allows our customers of varying maturity to deploy internal resources more effectively, freeing up teams and individuals to deliver value-add elsewhere for their businesses, with peace of mind over the security, governance and optimisation of their data environments.

Auto ML, AI and Prediction as a Service

We support businesses across the whole lifecycle of their AI and ML deployment. While the process is cyclical, there are three key areas we support:

Feasibility Study Phase
Can it be done? This is the first question that needs to be answered before any client should have to commit to spending large sums of money for a production model, or a large ML platform.

The Feasibility Phase answers this overarching question by carrying out two separate studies, each trying to answer the following questions:

• Can your data power the desired use case with good accuracy? – Predictive Feasibility
• Can this data be viably used in a production deployment? – Production Feasibility

Production Development Phase
This phase covers the development and management of the production model. With the work from the feasibility studies creating the groundwork for this stage, here we are taking this forward to build the production model to your specification.

Model Lifecycle Management Phase
Once deployed, this production model will be ready for use, with the maintenance and monitoring of the model carried out by Data Technology. As you grow your data function and make new data available, which may enrich the model’s performance, further feasibility micro studies can be done assessing the potential benefit of such enhancements.