Running a full stack data shop

Show Notes

• 01:54
  Running a full stack data shop

  Running a full-stack data shop involves centralizing various data functions like data quality, master data, data services, data and analytics, and data science teams. This integrated approach facilitates knowledge sharing, and synergies, and accelerates learning and innovation.
• 02:56
  Organizational setup for data functions

  The setup of having all data functions under one organization, as in a full-stack data shop, is less common. More frequently in organizations, data teams tend to be siloed from each other, lacking the tight grouping and connection found in a full-stack approach.
• 03:41
  Transitioning to a full stack data shop

  J.R. Simplot unintentionally integrated its master data, data conversion, and data quality teams with the enterprise data and analytics organization during a large SAP initiative. Eliminating the silos between data functions enabled a seamless and more efficient data conversion process.
• 06:02
  Overcoming the challenges of integrating different data teams

  Divergent missions among master data, data quality, data analytics, and data science teams often result in communication challenges. Fostering trust, informal relationships, and a collaborative culture ensures teams know how and when to communicate with each other.
• 07:08
  The value of unified data teams in legacy data transfers

  By integrating the data teams into a data quality team, J.R. Simplot streamlined the process of preparing legacy data for SAP transfer. This unified approach significantly reduced the time between production and reporting by avoiding duplicated efforts in data transformation.
• 08:21
  The benefits of a centralized data and analytics team

  These teams offer speed to market and consistent delivery of results. In addition, having in-house system and data expertise reduces friction within the IT organization, enabling quicker responses to business unit requests.
• 09:20
  Advocating for the centralization of data functions

  The rapid evolution of the data space demands consistency and efficiency. Highlight centralizations ability to facilitate people working in the same way, enable fluid movement of personnel across teams, and enhance organizational flexibility.
• 12:44
  Onboarding in a complex data environment

  Onboarding new team members in a complex data environment with multiple ERPs is a significant challenge. Centralizing data operations can streamline onboarding processes, making the team more efficient and effective in innovating and providing insights.
• 13:34
  Change management for data centralization

  In transitioning to a centralized data system, communicate the value of the change and manage people's expectations within the organization. It's crucial to focus on what can be done now and learn from mistakes rather than waiting for all the answers before taking action.