Why is Agile Data Warehousing Important?
It is difficult to translate a software development methodology to data warehousing, but it can be done. In this interview with TDWIs David Stodder, Bruce Szalwinski discusses the value found in Cobalts commitment to the iterative style and incremental delivery of agile development for data warehousing.
60 Seconds Smarter: Data Modeling
Tackling tough tech in a minute or less. This episode explores Data Modeling with Steve Hoberman, Data Modeling expert, author and educator.
Lean business intelligence means getting the most from your processes and implementations. Emerging tools have made an agile approach easier, but there is plenty of work to be done in terms of refining enterprise BI methods.
What can psychology and Maslows hierarchy of needs teach us about data infrastructure management? For starters, some of the basic principals of information management problem solving.
At this level, an organization may function, but without any formal processes or systematic management. Data infrastructure management is left to the whims of individuals, who carry relevant knowledge and know-how in their heads. Documentation of individual duties does not exist, and professionals simply go about duties such as backing up data, monitoring networks and administering patches to servers. Informal networks seen at this level lead to inconsistent performances across various systems.
