Solutions

Solutions that focus on accelerating time to value

ANALYTICS PATHWAY

Vision | Solution | Capabilities | Plan

PROOF OF VALUE

Prove a scenario | Assess readiness & feasibility | Identify value

DATA SCIENCE @ SCALE

Model development | Model evaluation | Governance | Training

PERFORMANCE INTELLIGENCE @ SCALE

Gather requirements | Metric design | Data integration | Data health







ANALYTICS PATHWAY

The Analytics Pathway is about developing a common understanding and clear goals across your organisation around the capabilities that will be used to collect, optimise and leverage data to efficiently run and grow the business

  • We approach the creation of a strategy through a few key streams:
  • Setting a vision based on analytics led outcomes
  • Defining the end-to-end solution capabilities required
  • Positioning the organisational capabilities needed to leverage the solution
  • Setting the right sequence for the longer term as well as next steps to get started



PROOF OF VALUE

The application of data science is generally the investigation and testing of a hypothesis. The reality with any such testing is that the identification of value is not guaranteed, with the risk that if left open-ended it may result in resource consuming, costly, and never ending analysis.

To reduce this risk, we recommend time- boxed ‘proof of value’ (POV) scenarios. With clearly defined timeframes and success criteria we give sponsors confidence that time and money are not being wasted “just experimenting” whilst allowing for the exploratory nature of data science.

  • Time-box effort in proving a scenario (typically 6 – 8 weeks depending on complexity)
  • Assess data readiness and machine learning feasibility
  • Identify value and provide recommendations on implementation of repeatable models














DATA SCIENCE @ SCALE

To avoid the risk of “production prototypes” our data science at scale methodology looks to deliver repeatable insights in a sustainable manner. The focus is to ensure that data science ideas are incorporated into business processes and are being used by the personas they were created for.

We bring the full experience and accelerators of our organisation to every engagement. Incorporating best practise and learned practises for data science engineering.

  • Data Science model development
  • Champion vs Challenger methodology
  • Model evaluation – business and technical performance
  • Data Science governance models
  • Training



PERFORMANCE INTELLIGENCE @ SCALE

The ideal performance model will allow day-to-day metrics that measure the organisation’s value chain to seamlessly roll up and align with indicators of strategic success and corporate risk.

Our delivery leadership can help guide the guide the setup of fit-for-purpose delivery models, be that Agile or milestone driven.. Where needed, we kick start engagements with templates and accelerators.

  • Requirements gathering accelerators 
  • Metric design framework
  • Visualisation best practise
  • Data integration
  • Data lifecycle management 
  • Data health checks
  • Governance models
  • Information portals








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