Implementation
We want to help your organisation extract more value from your data by applying data science and analytical techniques to gain insight to benefit you and your customers.
We tailor our offering to align to your business needs and culture. We acknowledge that not all organisations want or need to implement bleeding edge data science techniques. Some organisations we work with simply want to get away from the limitations of working exclusively in spreadsheets. Others do need to use some more advanced analytics to gain more value-added insight from their data. Whatever the need, the principles of the data science process are valid and provide a structured framework for us to work with your teams.
We are specialists in all aspects of the data science process: from the derivation of the requirements and subsequent solution specification; through to delivery of models, proof-of-concept tools and complete software solutions; and providing insight communicated through the most effective means.
We predominantly work alongside your existing teams, to make the most of your existing data, models and corporate knowledge. We can also provide complementary skilled resources, that are particularly valuable at the early stage of capability development or if specific skills are required outside of your organisation’s specialisms.
Our background as consultant engineers and mathematicians enables us to gain a rapid understanding of your business, processes and data; apply the most appropriate techniques; interpret the results in the context of the original business problem; and communicate through a range of media to deliver value to you and your stakeholders.
We provide static and interactive solutions using tools such as Python, R, Matlab and PowerBI; with full version control and data management. We are also able to host tools using external resources such as AWS, or provide support to host within your enterprise architecture.
Our requirements approach to data science is outlined in the figure below. Generation and use of the requirements are at the core of the process: we make use of data, models and knowledge in the context of the challenge and the desired outcomes. The approach is entirely scalable - we can support all stages through the process or provide a targeted subset if required. It can be applied to a broad spectrum of challenges, from strategic decision making, through to real-time operational dashboards and evidence-based interventions.
We tailor our offering to align to your business needs and culture. We acknowledge that not all organisations want or need to implement bleeding edge data science techniques. Some organisations we work with simply want to get away from the limitations of working exclusively in spreadsheets. Others do need to use some more advanced analytics to gain more value-added insight from their data. Whatever the need, the principles of the data science process are valid and provide a structured framework for us to work with your teams.
We are specialists in all aspects of the data science process: from the derivation of the requirements and subsequent solution specification; through to delivery of models, proof-of-concept tools and complete software solutions; and providing insight communicated through the most effective means.
We predominantly work alongside your existing teams, to make the most of your existing data, models and corporate knowledge. We can also provide complementary skilled resources, that are particularly valuable at the early stage of capability development or if specific skills are required outside of your organisation’s specialisms.
Our background as consultant engineers and mathematicians enables us to gain a rapid understanding of your business, processes and data; apply the most appropriate techniques; interpret the results in the context of the original business problem; and communicate through a range of media to deliver value to you and your stakeholders.
We provide static and interactive solutions using tools such as Python, R, Matlab and PowerBI; with full version control and data management. We are also able to host tools using external resources such as AWS, or provide support to host within your enterprise architecture.
Our requirements approach to data science is outlined in the figure below. Generation and use of the requirements are at the core of the process: we make use of data, models and knowledge in the context of the challenge and the desired outcomes. The approach is entirely scalable - we can support all stages through the process or provide a targeted subset if required. It can be applied to a broad spectrum of challenges, from strategic decision making, through to real-time operational dashboards and evidence-based interventions.
