Michael McKinley
Data. Made actionable.
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Michael McKinley is the founder of McKinley Consulting. Michael has managed projects and workstreams in consulting for the last six years, in areas including data analytics, change management, operations excellence, and supply chain transformation.
Mr. McKinley is currently leading a BI team at AllianceBernstein, a mid-level global investment bank. The reports his team generates are viewed by senior leadership, including the CEO, and are reported to Wall Street.
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Application Lifecycle Management for Business Intelligence...a.k.a. ALM for BI
If you have been developing Power BI reports in a team setting for any length of time, you have wondered how to manage that process and avoiding code conflicts.
In this session, I will borrow from my experience in application development and share with you the state of the art in solving this problem. You can have multiple developers working on the same dataset at the same time, and you can have your model exist as code in your source repository.
Automatic versioning and code diffs will tell you who made what changes when, and you can connect this work to a ticketing system if you would like. You can even set up deployment pipelines for continuous integration / continuous development.
If you manage a team of BI developers, or if you work on such a team, then this session will help you solve several of your biggest challenges.
Data Modeling for Business Intelligence
We all start using business intelligence tools by using a single dataset, like a flat file. Then, we hit a plateau in our learning curve, or we add a second flat file into our BI product, and we get incorrect results. One reason is that we haven’t modeled our solution correctly.
In order to move to the next level of our journey along the BI learning curve, we need to understand how a tool like Power BI works. Only then will we understand why we design our data models the way we do.
In this session, we will review how tools like Power BI work under the covers and how that impacts the way we construct our data models. This is an introductory level course, so we will not get too technical, just enough to get us to the next level and moving back up that learning curve!
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