Flashcards on Accelerating Data-Driven Transformation
Accelerating Data-Driven Transformation: BBVA's Success
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Data Analytics
12 cards
Card 1
Question: What was one performance metric focus for D&A when converting new work practices into reusable tools and techniques?
Answer: Both financial targets and long-term capability-building goals.
Card 2
Question: How were D&A data scientists funded in a way that influenced their focus?
Answer: They were only partially funded by business units, which justified focusing on strategic, long-term endeavors.
Card 3
Question: How many new tools and analytic solutions did D&A introduce into business units in 2016?
Answer: Eleven new tools and thirty-four new analytic solutions.
Card 4
Question: What development platform did D&A build to store and document code and serve as a platform for customer analytics?
Answer: A platform called Clarity.
Card 5
Question: Name two functions Clarity provided for data scientists.
Answer: Create, document, and publish models/algorithms that produced customer attributes and metrics; allow users to explore data models, find existing solut
Card 6
Question: What platform did D&A create to support asynchronous knowledge sharing among graduates of its data science training programs?
Answer: A community of practice (CoP) platform.
Card 7
Question: What initiative moved BBVA data from business unit silos to enterprise data platforms, and what was its outcome by 2017?
Answer: The 'data rainmaking' effort; by 2017 it had released thirty-four new data subject areas for enterprise usage.
Card 8
Question: By the end of 2017, how many data science projects had D&A launched and for how many business units?
Answer: More than forty data science projects for twenty-seven business units.
Card 9
Question: What commercial data product resulted from D&A work and what impact did it have on merchant margins in Spain?
Answer: Commerce360; users in Spain generated 25% higher margins than merchants not using the application.
Card 10
Question: What enterprise capabilities did D&A data scientists develop?
Answer: Technical platforms, shared data sets, and a collection of machine learning algorithms.