Agility in Analytics

Of the Conventional Analytics Mind

Using a conventional mindset, data ponds are identified and analyzed to build predictive models:

1. Data ponds are identified for evaluation
2. Data scientist performs detailed descriptive analysis
3. Based upon the descriptive analysis a “outcome of interest” is established
4. Using established guidelines a modeling technique is carefully chosen
5. A predictive model is constructed
6. The model is exercised and its performance is evaluated
7. If the model performance is not acceptable further analysis of data ponds is required
8. If the model meets or exceeds performance expectations, the model is deployed

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Adding Agility to Analytics

Organizations more mature in leveraging predictive models will recognize that establish models never stop evolving. An agile mindset will continue to score the model against “live” production data and determine what might be done to the model to improve or sustain its performance levels.

Conclusion

Organizations will need to not only invest in predictive technologies but also evolve is business processes to embrace the demands of the existing and future predictive analytics landscape.

“So long, Earth. Catch you on the flip side” – Jack Swigert