Now that you understand how to assemble your data-driven organization and what the respective roles and responsibilities are in the hub-and-spoke model, the next step is to understand the technology infrastructure necessary to make data self-service.
It used to be that big data initiatives involving large datasets, Hadoop, and open source technologies were mysterious and complex processes best left to PhDs. But this has changed. In the last few years, the cloud coupled with powerful yet easy-to-use querying and reporting tools has made self-service possible. This includes enabling self-service data for data analysts as well as business users who lack a technical background.
Gartner defines self-service analytics as follows:
[a] form of business intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support.
Putting the appropriate infrastructure in place is an essential part of achieving self-service. The mindset has to be thus: as a data-driven organization, we will publish all data without thinking of how it will be used. Then, the infrastructure platforms and analysis tools all need to be self-service and data universally available.
Chapter 4 looked at all of the different personas within the data-driven organization. ...