Tips for Building a Data Science Capability

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ANALYTICS CULTURE Why Hasn’t Your Data Science Investment Delivered on its Promise? The promise of game-changing results from data science has been touted for years, and the allure of that promise has driven organizations across all commercial markets and the public sectors to invest huge sums of money, time, and resources to make it happen. Encouraged by the outcomes of analytical pilots, organizations are now looking to scale data science across their enterprise. As they continue their pursuit, they have found the benefits are elusive and they are often humbled by organizational inertia. The hard truth is that a key enabler to delivering on the biggest promise of data science is transforming organizational culture. And, contrary to popular belief, it’s not about transforming to just any culture. Organizations must start to pursue an analytics-driven culture. An analytics-driven culture uses analytics to generate insights that can be used by organizations to inform strategic decisions and propel the organization to the next level of performance. One of the key benefits of an analytics-driven culture is the difference in timescales in findings solutions. With an analytics-driven culture, the organization becomes particularly adept at asking the right questions and rapidly (within minutes sometimes) getting answers. That shifts the time available to thoughtful discussion on what analytical outputs mean and how they inform decisions to derive desired outcomes.
BOOZ ALLEN’S DATA SCIENCE DELIVERY MODEL Booz Allen’s Data Science Delivery Model describes the ongoing activities that collectively evolve an organization to an analytics-driven culture. We work with organizations to approach analytics in a holistic matter through three phases: + Imagine Outcomes focuses on deconstructing business problems to define objectives that will be met through analytics. We work with our clients to create analytics strategies, prioritize initiatives, and establish a collaborative environment around analytics. + Generate Insights is where our experts apply data science and advanced analytics methods, tools, and techniques to support our clients in unveiling analytic insights. + Realize Value turns the analytic insights into actionable results that enhance an organization’s effectiveness, efficiency, and/or bottom line. We support our clients in putting analytics into production, developing feed-back ecosystems, establishing an environment of continuous learning, and building and managing their talent. DATA SCIENCE DELIVERY MODEL IMAGINE OUTCOMES What do I want to achieve and how will I get there? •Identify business problems and desired outcomes •Create analytic strategy and approach Opportunity Prioritization, Idea Generation, Relationship Building ACCELERATORS IMAGINE O U T C O M E S DOMAIN KNOWLEDGE including industry expertise, familiarity with data sets, and operational understanding, underpins an organization’s ability to maximize the value from its data GENERATE INSIGHTS ANALYTICS- DRIVEN CULTURE ACCELERATORS ollaborative GENERATE INSIGHTS How do I unleash the value from my data? •Apply data science to generate insights •Develop, test and evaluate hypotheses nalytics, ata aluation, echnology Infusion REALIZE VALUE ACCELERATORS REALIZE VALUE For my operations, organization, bottom line, and culture •Implement analytics in the enterprise •Identify and apply insights to generate value ctivated nalytics, daptive nterprise, ituational ensing Analytics Culture | 7
Page 1 and 2: Tips for Building a Data Science Ca
Page 3 and 4: CONTENTS INTRODUCTION..............
Page 5: INTRODUCTION Many organizations bel
Page 9 and 10: We found that discovery is self-ful
Page 11 and 12: eviewing various articles, press, a
Page 13 and 14: ORGANIZATIONAL PROFILES Getting Pas
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Page 17 and 18: ANALYTICS & CHANGE Keys to Building
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Page 25 and 26: THE DEPLOYED MODEL As with the diff
Page 27 and 28: THE LEADERSHIP ANGLE Harnessing the
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Page 33 and 34: number of data scientists supportin
Page 35 and 36: across the talent life cycle, inclu
Page 37 and 38: CASE IN POINT Data scientists who a
Page 39 and 40: (i.e., context) and builds the inst
Page 41 and 42: WE’RE HERE TO HELP Booz Allen is
Page 43 and 44: Erin Senter LEAD ASSOCIATE @erinsen

Tips for Building a Data Science Capability

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