Many IT veterans would hesitate to consider "agile" and "data warehousing" in the same sentence, given the track record of the latter to follow a long-worn process of requirements gathering, large data models, complex data integrations, and even more data sanitizing and tweaking before a polished report or visualization is ready. Tired of the rigmarole, some business groups are bypassing their IT organizations entirely and jumping to products like Tableau and Qlik to generate insights faster with ad-hoc data sets. However, these often lack guardrails to ensure the data's accuracy and consistency. Could there be a third way? Providing insights and examples from proven approaches, this talk presents a model for managing data analytics projects that produces high-value results rapidly using agile practices yet plays well with the demands of enterprise data management stewards, systems and standards.
We will discuss:
- What types of data analytics projects work best with agile?
- How do you set appropriate expectations with project sponsors and end-users?
- Who should be on your team?
- What skills and tools can help you get started?
- What rhythms or practices are invaluable along the way?
- How to Drive Agile Data Analytics for the Enterprise
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Eric Spaulding, MA, CBIP, is an information technology professional specializing in business intelligence and data management (quality, integration, analytics, and performance). His in-depth experience in BI practices and tools spans the entire project life cycle, including project leadership, business/systems analysis, solution architecture, data modeling, ETL/ELT development, reporting, and data visualization.
PMP's: This event counts for one PDU
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