From our experiences and research, we have identified four typical challenges or pitfalls that organizations encounter when working with the discipline we refer to as: Operationalizing data science. These pitfalls are crucial to address directly to succeed with your journey towards becoming data-driven.
001 | Prioritizing projects where the stable and reliable stream of source data can both be secured for long-term usage
002 | Building cross-functional project teams combining data science and domain expertise to run agile development projects and actionable insights
003 | Ensuring buy-in, involvement and long-term commitment of business owners before beginning development
004 | Investing time to turn the model’s findings into decision-making tools and to train end users on the tools
005 | Using phased launch plans that allow for an iterative approach to building model and manages business risks during any changes/transitions
We have a team of data scientists and data engineers who can work with clients to build, test and deploy new analytical models through three key stages:
001 | Clarifying user requirements and securing clean, reliable data sets to build a clear phased action plan with a stable long-term foundation.
002 | Working with domain experts and end users to engineer features and develop actionable algorithms.
003 | Rigorously testing new models in operational settings and putting them into production.
And as demonstrated through our other services, we can also help clients with building the decision tools, processes and training to put these models in the hands of the right end users.