Uncover the most impactful use cases for your business and how they can be applied to your data stack in a 1-day workshop.
Data + AI. Combined to solve your business challenges, fast.
Migration to the Cloud
Companies seeking to modernise their IT capabilities and access the benefits of the cloud are strategically optimising their data and processes. Migrating to the cloud leads to capital and operational cost savings. Cloud technology also unlocks further advantages and key business enablers such as speed, scalability, and security.
Enterprise Analytics in the Cloud
Enterprise analytics enables you to access self-service analytics at scale, harnessing both internal and external data to drive and increase the impact of, decision-making. Improve operational efficiency, achieve higher profitability and enable faster decision-making.
MLOps is a set of best practices, guidelines and technologies designed to deploy and maintain ML in production. It enables models to achieve faster time to value, increased impact, and reduced maintenance costs. MLOps also unlocks new revenue streams, such as data monetisation, advanced ML use cases, and edge-ML.
FinOps is a key practice that promotes shared responsibility for cloud computing infrastructure and costs. Cloud FinOps enables you to report & optimise cloud spending, reduce costs by achieving the insights you need. Machine Learning can further simplify the process of FinOps, and maximise savings through AI-powered use cases.
Data Mesh has gained momentum as an alternative way to structure business data, taking a decentralised approach to data management, and enabling businesses to harness and share data to become self-sufficient in addressing challenges around scalability and reliance.
Data monetisation is using data to obtain quantifiable economic benefits. This includes using data to make measurable business performance improvements and inform decisions, as well as data sharing in negotiations, information bartering, selling data outright, or offering information products and services.
Complex approaches to data science often require even more complex approaches to data, platform & ML engineering. Through our iterative ML solution, we focus on scalably deploying a simple use case to add value as quickly as possible. Each stage of the entire ML cycle is visited repeatedly to ensure you receive a highly performant, accurate model that solves your use case.
Responsible AI refers to the ethical, transparent and responsible development, deployment, and use of AI technologies. AI has the potential to transform industries and improve lives, but only if understood, developed and used responsibly.
Generative AI is a type of Artificial Intelligence (AI) that can create new content, such as text, images, and code, from scratch. It does this by learning from a massive dataset of existing content and then using that knowledge to generate new examples.
We also offer industry-specific solutions for telco + media companies, including
AI for networks
Our industry-specific solutions for retail companies include
Supply chain optimisation
How can we help?
Our solutions meet you wherever you are on your data + AI journey.
Evaluate the strategic and technical fit of a solution to your business and get advice on its suitability to your needs.
Understand the value that cloud data, BI, or AI use cases can bring to your business, get help with prioritising them, and kickstart your journey with a PoC or a series of workshops.
Validate your chosen solutions and tools by proving the value they can bring to your business based on a set of pre-agreed use cases.
Rapidly deploy solutions and build required capabilities within your business.
Build your in-house capabilities and upskilling to turbocharge your talent with instructor-led training courses customised to your business needs – provided by Datatonic Academy.