Data Delivery and Data Pipelines
- Determine as-is and target architectures.
- Establish the volume and types of data in scope, including delivery timeline requirements.
- Identify critical data services and map system dependencies.
- Recommend platform technologies, including a roadmap and migration approach.
- Provide robust and highly resilient data engineering services.
- Enable machine learning and deep learning models to have robust feature pipelines that just work.
Tooling and Automation
- Determine as-is and target tooling & automation.
- Identify target release and deployment models, putting the ops into DataOps.
- Recommend a roadmap and migration approach from your existing data estate to a target data platform.
- Enable a high degree of ML and DL model promotion and integration ensuring that model piplines are given the care that the data pipelines are.
Governance and Operations
- Determine as-is and target operational environment, including security, tracing and logging requirements.
- Determine current governance mechanisms and how these can be reaching the right balance between control and innovation.
- Establish data retention requirements.
- Provide a model governance layer to ensure models are performing as tested and drift is monitored that can be actioned.
Technical Skills and Training
- Identify technical skills gaps.
- Detail approach to resolving missing skills and technical knowledge.
- Define job roles and key skills.
- Build targeted training modules.
- Transform Product Managers to Data Product Managers.
Platform and Data Security
- Identify data security levels and restrictions.
- Identify services handling sensitive and personal data.
- Understand any geographical constraints around handling data.
- Determine appropriate security model and environment.
Insights from our experts
October 23, 2019
Accelerate Data Science: Why a Graph should be at the heart of your Data Platform
A majority of Data Science takes data about previous customer interactions and uses it to interrogate the current state, predict future activity and confirm hypotheses on changes to implement.
September 25, 2019
Artificial Intelligence: Hype vs Reality Recap
On 11th September we unveiled our Artificial Intelligence: Hype vs Reality report. The event, took a deep dive into the findings of the report and the learnings that can be applied across enterprise when it comes to scaling AI adoption. For those who were unable to join us, here is a quick recap and we hope to see you at future events.
September 11, 2019
Artificial Intelligence: Hype vs Reality
In partnership with YouGov, we commissioned a short survey of over 1,000 senior decision makers, including owners, partners, chairpersons and non-executive directors, working across the public and private sectors. The focus of the study is to lift the veil on boards’ attitudes towards Artificial Intelligence and understand the reality of adoption and awareness at executive level.
May 23, 2019
Scaling AI in the Enterprise Recap
Thanks to those who were able to join us last week for our first AI event with InstaDeep. This, the first of four events, looked at Scaling AI in the Enterprise and helping business leaders to realise the potential of introducing and scaling AI in their businesses. For those who were unable to join us, here is a quick recap and we hope to see you at future events.
May 21, 2019
Busting data lake myths so you can get the most value from yours
Having a data lake reaffirms an organisation’s need to invest more effort in data governance and data quality, helping you to understand the content that resides within it. However, both in our experience, and as revealed by Gartner in their report on data lake failure, if data lakes are misunderstood in the context of a wider data strategy, this can lead many initiatives to flounder.
April 1, 2019
Kickstarting your ModelOps journey
It’s vital to ensure that your AI is kept fit and effective. The first step in doing so is understanding the need to consistently curate your AI models. To do this, you need an effective ModelOps team. AI is evolving quickly and businesses need to ensure that their teams evolve to deliver against business and regulatory needs, whilst empowering their people too.