Running Data Science Projects and integration within the Organizational Ecosystem
Running Data Science Projects and integration within the Organizational Ecosystem


This talk explores the application of formal methodologies in running data science projects and presents tried and tested approaches to their integration and adoption within modern organizations.

Specifically discussed are the merits and concessions of introducing approaches such as CRISP-DM, KDD and SEMMA into traditional project management and Agile-driven ecosystems, where they can be integrated successfully and where they might remain segregated. Also explored are the areas of variance between approaches and where some organizations are evolving their own approaches to data project delivery as they become more capable.

Also included is an examination at how organizations should look to integrate data science teams into established project delivery lifecycles with a view to attaining greater levels of traceability and accountability within a project environment. Cultural pressures introduced from new skills and technology also affect team collaboration and effectiveness and this talk explores approaches to maintaining high-levels of productivity and collaboration throughout that transition.

Within similar consideration, this discussion also examines what analytics and key indicators are important for measuring the effectiveness and efficiency of data science delivery within an organization.

Other focus points in this discussion include presenting the challenges that organizations typically face when first scaling up data science teams such as technology integration and tooling that can exist autonomously within a technology stack and the areas of compliance and security that should be considered. Within this areas there is also an examination of the trends of data science platforms and how they’re evolving to meet organizational requirements for policy and compliance.

Lastly included is a summary of the benefits and concessions that organizations will explore as they increase and further integrate data science practices within their business.


Cameron Sim is the CTO and CoFounder at CrewSpark, a Collaborative Data Science platform for businesses aiming to achieve advanced capabilities is data science tooling, workflow, efficiency and operational transparency.

Cameron is a career technologist with a passion for creating operational efficiency, innovation and value at the convergence of data science, cloud and core engineering.

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