Tips for Building a Data Science Capability

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ANALYTICS CAPABILITY ADOPTION CURVE DEGREE OF SUPPORT I am aware of the analytics capability stand up AWARENESS I understand how the change contributes to the analytics vision and how it impacts me UNDERSTANDING I am open-minded to the change and tell others how it contributes to the analytics vision ACCEPTANCE I am accountable for participating in the analytics capability ADOPTION OWNERSHIP I confidently share the successes related to the analytics capability stand up. I am committed to sustaining the change and can clearly explain how it relates to the analytics vision A good place to begin when defining the vision is a stakeholder analysis. Whether you are planning to stand up an enterprise-wide analytics capability or provide analytics for a single business unit or division, you should understand the cross-organizational concerns such as improved access to data and data security. A key to realizing the vision is communicating it to stakeholders. This is most often achieved through a strong leader at the top of an organization. But the vision can also be realized from the ground up. For example, Booz Allen’s visioning process helped an intelligence agency—overwhelmed by the volume of data to be analyzed—create a vision for their analytics capability. That vision was communicated primarily through well-respected domain experts at the agency who led a grass-roots effort to build an analytics capability and successfully influenced leadership, staff, and peers. Whether the vision is top-down or bottom-up, it is essential to identify leaders and sponsors who can reinforce the ideas and keep the effort moving. Booz Allen gives you, as leaders and sponsors, the messaging and other change management tools to define your vision for analytics and present it to the organization. Booz Allen’s collaborative visioning process helps define a future state that reflects the unique realities of your organization and cultivates a willingness to participate. A key role of the vision is to help overcome the roadblocks to buy-in. Among the ways to accomplish this is by demonstrating leadership’s commitment and sponsorship, and by helping analytics teams and end users establish shared values and goals for the analytics. As these efforts take shape through the vision, they must be reinforced through participation in the analytics capability and through prototypes. PARTICIPATING IN THE ANALYTICS CAPABILITY The next step to overcoming resistance and building buy-in is to encourage stakeholder participation in the analytics capability. One way to accomplish this is to have stakeholders from across multiple business units contribute to decisions on analytics tool development. Through this engagement, stakeholders become actively involved in making the vision a reality. Participation calls for true collaboration— leaders do not simply push their ideas down, but bring all the stakeholders into the effort. Through regular feedback and other methods, stakeholders 18 | KEYS TO BUILDING BUY-IN
can help shape and take ownership of the organization’s analytics capability. The Analytics Capability Adoption Curve shows the five basic stages of change adoption, from initial awareness to ownership. Organizations can move stakeholders along this path by establishing a network of change agents at different levels and domain areas. For example, change agents might include mid-level managers from each business unit or division, or representatives from analytics teams. They are essentially embedded advocates that facilitate communication among stakeholders. Booz Allen helps develop this network of change agents and gives them tools to target their communications and feedback collection. By fostering collaboration among the stakeholders, the change agents—along with leadership—help break down the data silos and encourage information sharing so that the results of the analytics will be more complete and accurate. The stakeholders work out their differences, offer suggestions, and understand how they will all benefit. This same process can help all those involved in the analytics capability—from the owners of the data to the end users of the analytics—develop a set of shared goals. And it can help make the data more transparent so that users are more likely to trust the results. GAINING BUY-IN THROUGH PROTOTYPES As organizations develop greater analytics capabilities to drive decision making, prototypes are essential in solidifying the support that is built with the vision and participation. Prototypes should focus on areas where quick buy-in is needed most. For example, if there is strong resistance to sharing information, the prototype should be strategically designed to show how the various stakeholders will benefit. It is often helpful to start with smaller projects or less complex analytics where quick wins are more assured. As illustrated in the chart below, prototypes that are most likely to generate buy-in have four qualities: TO BUILD BUY-IN, AN ANALYTICS PROTOTYPE SHOULD: FEATURE A HARD PROBLEM TO SOLVE YIELD A HIGH RETURN If the problem is too easy to solve, people may not have confidence in the strength of the analytics capability. But make sure the problem is not insurmountable. Demonstration of business value is the holy grail—the analytics outputs must be actionable, as people want to see results before they buy in. PROVIDE AN ITERATIVE SOLUTION REACH ACROSS TARGETED GROUPS Quick success, even if preliminary, is important—the solution does not have to be perfect to gain buy-in and inspire action. Be wary if the problem you are trying to solve requires extensive data collection or highly complex analytics to even begin to show results. If the analytics capability is intended to serve multiple business units/divisions, the problem you are trying to solve should resonate with all of them. Positive results for only a small portion of stakeholders will limit organizational buy-in. Analytics and Change | 19
Page 1 and 2: Tips for Building a Data Science Ca
Page 3 and 4: CONTENTS INTRODUCTION..............
Page 5 and 6: INTRODUCTION Many organizations bel
Page 7 and 8: BOOZ ALLEN’S DATA SCIENCE DELIVER
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
Page 15 and 16: locked in rigid data silos that are
Page 17: ANALYTICS & CHANGE Keys to Building
Page 21 and 22: strategic and tactical gaps and pot
Page 23 and 24: usiness problems, and they may see
Page 25 and 26: THE DEPLOYED MODEL As with the diff
Page 27 and 28: THE LEADERSHIP ANGLE Harnessing the
Page 29 and 30: goals. No matter the alignment, the
Page 31 and 32: EVERYTHING YOU NEED TO KNOW ABOUT M
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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