The Digital Trust Cyber Security Practice is responsible for ensuring consumer, partner and employee confidence by reducing and managing digital risk through considerations such as cybersecurity, dataprivacy, resilience and transparency underpinned by the following areas:

Companies that successfully deliver secure, seamless data experiences will win in the data marketplace through higher customer loyalty. Cyber Security should protect the business while preserving great user experiences.  The goal of Digital Trust is to create a secure, frictionless experience for users through Cyber Security with simplicity

What We Do

The Digital Trust Cyber Security Practice is made up of two key Functions 

• Security Governance/Compliance 
• Security Operations. 

The ultimate objective is Risk Mitigation through Visibility, Awareness Education and Transformation.

Solution Engineering

Solution Engineering practice is a core capability, responsible for taking Machine Learning (ML) and data driven projects and embedding them in businesses processes systems to deliver value. Once embedded, we maintain these to ensure the value continues long term and drives on-going business value.

What We Deliver

The practice is responsible for architecting serverless solutions, building applications and integration with enterprise architectures. The projects the practice undertakes cover the full solution life cycle: from prototype, using Cloud ML services to full production deployments, using DevOps and following Cloud best practices.

How We Deliver

DevOps is essential to the full life cycle capability that we offer to our customers; it is a process and culture that allows us to quickly deliver value by taking ML and delivering it into the heart of a customer’s business. In addition, we also use DevOps to build prototypes to showcase the future potential that ML could have as a differentiator or disruptor in their market or industry

Principles

• Secure (Security built-in from Day 0, Digital Trust enactment)
• Accessible (doc, comments, traceable)
• Controlled (Version and change management)
• Re-usable (modularity, boundaries and encapsulation)
• Efficient (performant and low costs)
• Durable (fault tolerant and recoverable)

Skills

Cloud Migration Operations

We plan and implement migration from on-prem to any cloud platform such as AWS, Azure or GCP. These operations are generally planned and performed in 2 phases by our Solutions Architects and Engineers.

• Lift & Shift: As-is basis on-prem system and data migration operations.
• Modernisation: Re-develop the migrated systems by applying best practices in Security, Architecture and Engineering perspectives using the most efficient and effective cloud services.

Platform Engineering

Platform Engineering automates infrastructure management and enable developers to self-serve reliable tools and workflows from a centrally managed technology platform, enhancing developer productivity by reducing the complexity and uncertainty of modern software delivery.

1. Building Internal Developer Platforms
2. Standardizing and securing key delivery processes
3. Setting and maintaining internal service level agreements
4. Monitoring team performance metrics

We help customers identify data science opportunities within their organisation, develop Machine Learning and Deep Learning solutions to validate our customers business case and support them to bring these solutions to life. We are inquisitive by nature, and we love exploring solutions using state of the art technology and algorithms.

Ultimately our goal is to deliver great Data Science solutions that meet the customer needs by keeping true to our scientific and analytical methodology.

What We Deliver

Typically, we deliver 4 key project types:

For each use case, Goaltech proposes a four-stage approach

How We Deliver

We follow an iterative approach in developing solutions – according to the CRISP-DM framework

Our discoveries and productionisation work are underpinned by data driven decisions. Thinking analytically and following Data Science best practices is in our DNA. When it comes to answering our customers’ questions, we never take things for granted. We are the first ones to admit “we don’t know what we don’t know” and allow the data to guide us towards the right answers.

Our ACTIVE principles underpin our work:

Skills

This team is responsible for the Machine Learning model development to align with business requirements
and is underpinned by three expertise pillars:

Our data scientist team’s general capabilities:

• We all write code – good Python dev skills
• We all have advanced DS / AI skills
• We are all AWS and Azure certified

There are areas of expertise across the team:

• Computer Vision
• Natural Language Processing ( NLP )
• Time series and forecasting
• Anomaly detection

There are three core areas of focus within Data Management with members possessing a blend of Data Architecture, Engineering and Platform knowledge, with DevOps Culture underpinning all that we deliver for our customers.

The work that the Practice performs can be sub-divided into 4 main “Data Processing” capabilities: 

Data Ingestion – Extraction of data from varied sources to a common destination platform

Data Modelling – Articulating the relationships between entities at the conceptual, logical or physical level and choosing the best data model to suit the requirement

Data Transformation – Conversion of data from original format into a structure, quality and format optimal for analysis

Data Exploitation – Extracting business information from the data, normally via visualisation tools