What if Steven Covey was a technologist, data professional, or data scientist? Would the best-selling author--best known for promoting his "Seven Habits of Highly Effective People"--have thrived in today's data-driven world?
I believe he could have thrived, if he adapted to the complexity and practical realities of working with data and technology. Like today's best data pros, he would have succeeded by combining technical skills with the principle of "Beginning with the End in Mind" (Habit #2). He would also have needed to take the next step, rolling his sleeves up and figuring out HOW to achieve his most essential goals.
I call this next step "Causal Clarity," by which I mean achieving a clear vision for HOW a goal will be achieved. What will cause the desired result? What are the preconditions? What are the sequence of steps--sometimes thousands of them--that lie between the current state and the desired state?
Causal Clarity and Success: in my experience, data pros thrive when they insist on clear goals, then apply clear, structured thinking to the question of "how will we get there? what will cause the result we want?" Thinking in this way is especially valuable in the complex terrain of data and technology. It supports success in several ways:
- High ROI Projects: at the enterprise level, the goals that matter are those that improve profitability in the long run. When data professionals understand the key drivers ("causes") of profit at the corporate level, they effectively link investments in technology and data to the business outcomes these projects support, which leads to more successful and profitable technology investments. For example, customer retention and loyalty is a key driver of profitability for many corporations. Recognizing this allows tech projects to focus on this key goal: improving customer experiences by delivering the right information at the right time operationally, and predicting customer attrition earlier so that actions can be taken to salvage at-risk relationships.
- Avoiding Low-Value Activities. The inherent complexity of technical work makes it harder to tell the difference between essential tasks and "nice to haves." Asking "how does this task get us closer to our goal?" reduces the risk of wasted effort on low-value tasks. As an example, I worked with a team of data scientists who eliminated months of low-value work by limiting their focus to new analytic projects which could be tied back to key business decisions, essential business processes, and priority business metrics such as customer acquisition and profitability. By focusing on projects they are certain will impact the business, they have unlocked tens of millions of dollars per year in measurable profit impact, avoiding burnout and wasted time on low-value tasks.
- Improving Collaboration: finally, having a clear "Causal Model" improves collaboration both within teams and among them. Technologists understand how their combined efforts support business objectives, and individuals understand how their efforts fit into the bigger picture. All too often, leaders all teammates understand the goals of a project, and the key drivers of business success the project will influence. By clarifying the goals and business logic of technical initiatives, technical pros can avoid wasting time and money, improve the quality of their output, and avoid "failed projects."