There has been a lot of news coverage lately around the topic of creating a data-driven culture within an organization. The fact of the matter is a data-driven culture is crippling. We tried to create a data-driven culture too, but ultimately found that our real transformation came by using data as inputs into a real Analytics driven-culture. A culture that values true experimentation, understands failure is the price of discovery, and actually makes use of analytic outputs for decision-making.
While there is common agreement about the power and potential of data science and advanced analytics, many organizations struggle with how to incubate and sustain a curious and innovative Analytics-driven culture. It is a concept that continues to perplex organizational leaders and prevents organizations from reaching the next level of performance. While often viewed as an organizational problem to be addressed by senior leaders, we have seen first-hand how every data scientist in an organization has an important role to play. Leadership from the top is critical, but the support of all data scientists is needed to create a cultural change.
As Booz Allen Hamilton formed its 500+ data science team, we faced some challenges in creating our own Analytics-driven culture. In the spirit of sharing, I wanted to share a few key practices (and some lessons learned) from our own experiences. These practices helped us sprout and grow analytics as a central cultural tenet.
1) Failure is the Price of Discovery. One of the first things we had to do on our journey to an Analytics-driven culture was recalibrate our tolerance for failure. To realize the full power of what data science and advanced analytics can provide, we needed to create conditions that foster curiosity and experimentation and embrace failure for the unexpected insights and learning that it can provide. At Booz Allen, we created these conditions in a few ways. First, we empowered our data science team to investigate and discover; to design and run their own experiments that blend inductive pattern recognition with deductive hypothesis generation; and to explore “rabbit holes” of interest. Recognizing, like many others, that data scientists are rarely motivated by money and prefer the time and space to “geek out”, we established a series of reward and recognition mechanisms focused on investing in passion projects. What we learned in our journey is that true curiosity and experimentation requires an enormous tent of skills, abilities, and perspectives to achieve meaningful outcomes for data science. It is important to take an inclusive approach to advanced analytics that extends beyond the traditional cadre of world-class data scientists. For example, our data science teams often include organizational design and strategy practitioners, design thinkers, and human capital planners. Finally, discovery has an added organizational benefit. We found that discovery is self-fulfilling and that once you start on the road to discovery, you gain momentum and energy that permeates throughout the organization, which provides powerful lift to your data science team.
2) Story Tellers Learn by Doing. Data science and advanced analytics can be intimidating for some and exciting for others. What’s important is to provide a variety of learning opportunities that can cater to the different skill sets and attitudes that exist within the organization. We tackled this challenge in a variety of ways: 1) We sponsored analytical challenges and hackathons to both test and satisfy the desire of advanced practitioners while at the same time providing a safe learning environment for those still developing their skills; 2) We created a self-paced online training course for data education, Explore Data Science, to teach both foundational and advanced data science concepts such as data visualization; 3) Additionally, we created a data science training program called TechTank to cultivate skills through apprenticeship and structured training. These are just a few examples. The bottom line is that we learned it is important to design opportunities and learning programs at all levels to help people get familiar with data science concepts and become more comfortable in practicing it. Don’t cast data scientists as mythical rock stars and then not offer a way into the “club”. Remember, all data scientists have a role in providing mentorship and learning opportunities. Attend hackathons, teach classes, and mentor junior staff whenever possible.
3) Adjust Your Time Scale. One of the key benefits that we have found from our 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 (we’re talking minutes here) getting answers. That shifts the time available to thoughtful discussion on what analytical outputs mean and how they inform decisions to derive desired outcomes. For example, at one government agency, our Analytics-driven perspective led us to abandon a traditional data-driven approach, such as clustering models, in favor of developing models of ‘normal’ behavior and then filtering the data set to decrease the signal-to-noise ratio in order to catch fraud in a significantly shorter amount of time. This pioneering technique was a direct result of data scientists thriving in a culture that values discovery and ingenuity.
While these are just a few examples of how we have approached our journey to an Analytics-driven culture and the benefits we have received, we hope that the ideas presented here inspire you and offer some practical ways to help you get started on your journey to an analytics-driven culture. Remember that everyone in the organization has a role to play from organizational leaders to data scientists. It’s through the collective effort of these groups that you can achieve an Analytics-driven culture.
Are we making sense? Check out more of our perspective at: www.boozallen.com/consulting/strategic-innovation/nextgen-analytics-data-science
—Written by Ezmeralda Khalil