How can you better communicate Big Data Project Management tradeoff’s with Leadership? You need a simple approach for positioning information. I remember when I was working at Tandem Computers in the Manufacturing New Product Introduction (NPI) Program Office. I added to the standard project management template the prioritization of the Iron Triangle factors, e.g., cost, time, schedule and quality. It was exciting to move from mastering the craft of the complex project charter components and global team contributions to now having a leadership discussion on what is important to the organization.
Chris Nott does the same thing for Analytics project team decision. In an IBM blog post he builds on the foundation of big data’s varying number of Vs: volume, variety, velocity and veracity to key analytics project factors to consider. These guiding principles can be used to inform the leadership team and stakeholders on features, training and timing. I have added the project management implications for his approach.
Confidence: What is the data architecture and data science behind the features? Do we need to educate business stakeholders on the tradeoffs made and possible suggest a staged approach to data-driven projects?
Context: Per Chris – Understanding context requires understanding who is asking the question and why. I consistently see that outsourced or offshore teams can make assumptions about the requirements. Projects need to have facilitated sessions and liteweight documentation about what this question is being asked, what is the value of answering it and not to over-constrain the solution.
Choice: Opting for a particular technology platform and analytics tools represents the third C of big data—choice. Infrastructure architecture and buildout needs to be completed quickly at the beginning of a project to reduce the time to outcome perception by the business user. Yet, continuous review of architecture needs to be completed by a cross-functional team during all iterations as technology is changing fast and proof of concepts are hardening into production ready systems.
Great insight on how to use Confidence, Contest and Choice to easily introduce tough and complex analytics project tradeoffs during big data project execution.