What are the success factors for data analytics?


Due to their nontraditional nature, data analytics initiatives might face obstacles during development as well as during subsequent deployment.

At this daWos session, we will explore, based on practical experience, what aspects (organisational and infrastuctural as well as technical) that are particularly important to take into account for a data analytics initiative to succeed at a statistical institute.

Questions that could be addressed include

  • What are the experiences and lessons learned from existing analytical projects in terms of the mainstreaming of analytics activities, management and financing?
  • When a successful use case was not deployed in production, what was the reason for this "success story" not to be adopted (or replicated) in production? Is the reason purely technical, methodological or maybe structural/organisational?
  • Are there other examples of actual failed projects? What were the reasons? Can those be considered as systemic issues? Were pitfalls identified? Any way to overcome such barrier in the actual production?
  • What would be the best way to conduct data analytics and promote its adoption by the business/methodological departments (e.g., for instance through supporting ad-hoc collaborative data analytics activities not yet in production)?
  • What are the implication of the use of data analytics tools on the future state of the organisation’s process, its culture and capabilities? What to embrace? Which limitations and risks are foreseen?
  • Regarding "internal" users, what is the necessary adaptation of job profiles and skill sets for an effective use of data analytics services throughout the official statistics community?
  • How to describe the skills/competences needed for data analytics? Should, for instance, specific ESTP trainings be purposely designed for data analytics?