Warring Tribes into Winning Teams: Improving Teamwork in Your Data Organization
Christopher Bergh
CEO & Head Chef, DataKitchen: observe & automate every Data Journey so that data teams find problems fast and fix them forever! Author: DataOps Cookbook, DataOps Manifesto. Open Source Data Quality & Observability!
DataOps and Relational Coordination for Chief Data Officers
If the groups in your data-analytics organization don’t work together, it can impact analytics-cycle time, data quality, governance, employee retention and more. A variety of factors contribute to poor teamwork. Sometimes geographical, cultural and language barriers hinder communication and trust. Technology-driven companies face additional barriers related to tools, technology integrations, and workflows which tend to drive people into isolated silos.
The Warring Tribes of the Typical Data Organization
The data organization shares a common objective; to create analytics for the (internal or external) customer. Execution of this mission requires the contribution of several groups shown in Figure 1. These groups might report to different management chains, compete for limited resources or reside in different locations. Sometimes they behave more like warring tribes than members of the same team.
Professor of Information Systems, HEC Lausanne & Competence Center Corporate Data Quality (CC CDQ)
5 年Good idea to clarify the teamwork, but: Data architects are missing - as well as data managers/stewards who ensure that data is fit-for-purpose (data quality).?
A crucial component is missing in today’s methodology – conceptual entity-relationship diagram. It was the map for the other teams. ETL becomes constant re-write without it. Data governance taxonomy becomes long list of items lacking basic definitions. Therefore, it is all reduced to Operations.