The way that TOGAF achieves its intended goal is to divide enterprise architecture into four Architectural Domains: Business, Application, Data, and Technical.
We execute TOGAF framework for developing and managing enterprise architecture.
We deliver the project work packages based on Agile mindset, figuring out which Agile practices will best suit an organization, which spans many approaches to agile such as Scrum, Kanban, Lean, extreme programming (XP), and test-driven development (TDD)
Modern ways of delivering value in a co-creation effort of stakeholders, using an Agile approach in a customer-focused setting. We use modern technologies, including DevOps and cloud computing. Its holistic approach not only supports the management of IT services, but also supports other domains, enabling the integration of IT with the business and with other support domains.
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 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.
Building Internal Developer Platforms
Standardizing and securing key delivery processes
Setting and maintaining internal service level agreements
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:
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
We use various programming languages in our solutions at data processing, real-time data ingestion, developing front-end and back-end applications along with data tools and techs.
We design relational database models for Web and Mobile applications with 3rd NF and BCNF. We also design data warehouse layered structure in 3rd NF, Star or Snowflake schema using Kimball Model ( Dimensional Model ) with Change Data Capture (CDC) and Slowly Change Dimension ( SCD ) types implementation using popular Data Modeller tools. In data warehousing architectural perspective, we may apply Enterprise Data Warehouse ( EDW ) or Data Vault approach in database design depending on the requirements and data sources.
We use different type of databases in our solutions regarding nature of the requirements and decide how to store, process and retrieve of data in effective and efficient way.