Data science is often – rightly or wrongly – associated with high levels of data maturity. Data scientists are tricky to recruit and difficult to retain in an increasingly competitive marketplace, so even organisations who recognise the value of data science may struggle to implement it.

Due to the exploratory nature of data science, spending precious budget on what might seem like uncertain outcomes can seem daunting, which is where our Proof of Concept comes in.

Discover the feasibility of solving a business problem with data science, by developing a use case without committing excessive budget, time, effort and resource, and with guaranteed insights for action.

Data science solutions have a wide variety of applications, and with the Proof of Concept, you can see where data science can add value across your business functions and operations, without the hassle and stress of new recruits or training expenses.

Prove how valuable data science could be to your organisation

Generate insight without the excess costs associated with data science

Explore a use case to assess the feasibility of using a data science approach

Gain an actionable roadmap on how to productionise the model for ongoing benefit

Benefits & Deliverables

The first aspect of the Data Science Proof of Concept is a Solution Scoping Workshop, during which we will collaboratively decide what immediate business problem is a viable candidate for the Proof of Concept. This will include a deep dive into the prospective value of a data science solution, and ultimately how feasible it is to put in place, based on various factors like the data itself, technologies in place, people and skills, and processes. The outcomes of this workshop will inform a solution design document.

We will then work through the solution over a number of weeks, conducting exploratory analysis, preparing the data, iteratively modelling the data to achieve the greatest possible predictive power. You’ll receive the raw results of the predictive model (typically in a single Excel document), and a written report, detailing the methodology of the solution. This includes details of the modelling techniques, challenges faced and how they were resolved, transformations or feature engineering, as well as statistics of the model performance and an informed estimate of how successful the model could be if productionised.

After this, a Solution Evaluation Workshop is held with key stakeholders to discuss the results and report content – essentially to evaluate whether the solution is feasible in practice, and therefore what specific benefits this would provide to your organisation.

In terms of benefits, these will naturally be specific to the problem chosen for the Proof of Concept, but regardless of what has been selected, the Proof of Concept will provide you with an honest and reliable assessment of how data science methodologies can add value to your organisation.

Benefits include:

• Immediate and actionable insight based on the problem statement and model performance
• A costed plan for enablement and productionisation activity (eg. Data platform, integrations, data quality)
• Validation that the problem is solvable using data science methodologies, and how best to go about doing so
• Quantification and evidence of the viability of a data science solution based on your current data maturity
• Provision of a ‘PoC sandbox’ on our own Azure tenancy where you don’t have one
• Input and knowledge transfer from our vast data science experience

Why Simpson Associates

Simpson Associates are a leading Data Analytics Consultancy and Managed Services provider based in the UK, specialising in data management and data performance for over 30 years. We are a Microsoft Solutions Partner with four competencies in Data and BI, and are also an IBM Gold partner. We pride ourselves on enabling organisations to make better data-driven decisions by developing a data solution to suit their needs.

Next Steps

To learn more about our Data Science Proof of Concept, book a free consultation with us today.