Each tiny analytical discovery can be reviewed and used by business decision makers and is a step toward achieving the bigger original goal. Each tiny business action or decision based on analysis is rigorously measured so that we can learn whether the analytical model has the intended consequence.
This build-measure-learn cycle is key to ensuring that we are converging on the right outcome even if it diverges from the original goal. Agile analytics is continuously focused on actionable, insightful, and disruptive knowledge discovery.
It is very easy to find yourself doing interesting data analysis that is not necessarily impactful analysis.
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Agile Analytics is a blend of sophisticated analytics techniques, agile delivery methods and lean learning principles, but doing it well involves a bit more than that.
I recently gave a talk in Singapore where I outlined the essential elements of effective agile analytics.
Rather they are the complex heart of powerful solutions to challenging business problems.
Examples like using machine learning on traffic flow data in real-time to reduce gridlock and traffic jams or using dynamic, real-time bioinformatics sensors embedded in a sports bra to detect breast cancer in women are at the pinnacle of such solutions thinking. High ethical standards are needed to endure the inevitable scrutiny that analytics undergo.
Standards include ensuring personal privacy and enabling individuals to control their own data.
I summarize these seven dimensions here, which accompanies this slide deck from my original talk. While I’ve grown cautious of this buzzword, it is important to acknowledge that today’s data is more diverse and messy than ever before.
Agile analytics involves the use of both SQL (relational) and No SQL technologies to create a polyglot persistence infrastructure for data management.
The ability to align your data storage technology to the nature of your data is key to flexibility and speed. Creating true value from data requires going well beyond conventional BI reporting, which is important but rearward facing.
Competitive disruption comes from predictive and prescriptive analytics (Data Science) and visual storytelling. Agile analytics starts with a high value business goal and then chunks up the data science into tiny incremental goals that can be presented to stakeholders every few days.