reduction in the time it takes to do data analysis
of work done exclusively with Dataiku visual recipes
Startup Genome supports forward-looking geographies in catalyzing their own startup ecosystems. Their challenge, therefore, is sorting through all of the anecdotal information and dispersed data that surrounds startups in order to develop precise reports from which policy makers can draw insights.
The data and analytics team at Startup Genome performs both primary and secondary data collection surrounding the startup ecosystem, building large collections of datasets and analysis out of that data from which researchers will get insights to produce their annual Global Report plus many specific deep dive reports for their clients.
Given the nature of their work, Startup Genome faces several unique challenges:
Startup Genome uses Dataiku as their centralized system for all database and analytics needs (data governance, data blending, manipulation & feature engineering, predictive model creation, and data governance).
If I wanted to do everything that we do manually in some other way, the chances of error, the time involved – that is a pain which Dataiku has taken away completely. I don’t have a data warehouse in one technology, ETL happening in several places, analysis happening in five different tools.Munish Malhotra Director of Analytics & Data Science at Startup Genome
Dataiku ensures that everyone works all in one place, without data floating on local machines — this also ensures consistency and quality of analysis by keeping everything in the same tool. Thanks to data preparation features in Dataiku, the team at Startup Genome is able to leverage visual analysis for about 70 percent of their work, keeping the need for coding to only about 30 percent of work, ultimately speeding up analysis. Ultimately, with Dataiku, Startup Genome follows a standard data pipeline and can quickly iterate, reduced the amount of time iterations on data analysis take by an estimated 40-50%.
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