By Chris Frazier and Dr. Michael Zargham
It’s a familiar scene playing out in meeting rooms across the world.
The analysis timeline has slipped twice. The executive has covered for the Data Team with the CEO, but the CEO is losing patience and wants a decision. The Lead Data Scientist and his or her team enter the conference room looking exhausted.
Then comes the detailed presentation with text-heavy slides and multiple charts with a plethora of lines, dots and upper and lower bounds but no solid answers.
Not wanting to look confused in front of the team, the executive moves forward with a decision based on what they believe all of it means, thinking that they just made a data-driven decision! Unfortunately, the Data Team spent too much time researching minutia, only to miss the big picture and the results are a total disaster.
What is an Executive Scientist?
An Executive Scientist is a mindset that can be employed by anyone who is in a position to make impactful decisions that require a balance between data and judgement. This person must be able to leverage data to make timely informed decisions without getting caught in analysis paralysis. This is a tricky problem - a good decision a day late is useless, but a bad decision on time might cause a crisis.
The Executive Scientist strikes the balance, applying both rigor and judgment to drive preferable business outcomes, even (or perhaps especially) in the presence of uncertainty.
This means that the Executive Scientist doesn't go too far in either extreme. They do not lean so heavily on the data that they allow endless machine learning iterations to explore unrelated or irrelevant paths, expecting their data science team to magically divine a perfect unassailable solution -- the Executive Scientist knows that in real life, no such perfect solution exists. They also do not go to the instinct extreme, relying entirely on opinions and gut feelings, causing them to “shoot from the hip” and potentially jump to bad conclusions.
The Executive Scientist takes a hands on approach to analytics by:
- Turning their gut feelings and tribal knowledge into structured hypotheses and assumptions. They then make those assumptions legible by properly formulating and documenting the problem statement.
- Empowering their data teams by communicating the problem statement along with all the applicable context as well as how the results will be used.
- Asking questions early and participating in the analysis. They then review the legible conclusions with the team that include concessions and unavoidable assumptions due to unattainable data.
- They acquire new skills to reduce their own learning curve. They use tools beyond MS Excel, allowing for deeper understanding of the data and how they can be applied to decisions.
- When it is time to execute the decision, the Executive Scientist works with the Data Team to determine the KPIs for measuring observable metrics that are relevant to the implementation.
In future articles, we will expand further upon the hands-on approach employed by the Executive Scientist. In this article, we’ll dive deeper into the decision methods employed by classical Executives and how they can improve with the Executive Scientist mindset.
Executive Decision Methods
The classical Executive wants information readily available so that they can make decisions in a manner with the lowest friction to their other numerous important duties. The Executive understands that their role involves a trade off between risk and reward in the decisions they make.
To achieve this, the classical Executive often delegates “finding the answer” to hired professionals in hope that actionable insights are discovered in a timely manner. This unevolved mindset discounts the value that the Executive can bring to the table, and under weights the friction and loss of context that occurs in the knowledge transfer back to the Executive.
The Executive Scientist plays a more active role in scoping and interpreting the analysis, resulting in a positive influence toward the desired outcome.
They do this by first distinguishing what decisions are merely urgent from those that are truly important. For an urgent decision, they gather the best information at hand and mitigate risk from unknown factors as much as possible. A great way to improve decision making in urgent circumstances is to develop a more complete understanding of the forces at play by evaluating the consequences of past decisions.
For an important decision, the Executive Scientist takes an analytical approach to improvement-oriented research that has clear objectives, is relevant to the question at hand and ensures that the incentives for all stakeholders are aligned. The rigor and procedure applied to important decisions will also provide improved intuition when the next urgent decision comes up.
Below is a matrix of each type of decision-making method employed by Executives, and how they can be improved upon to reduce risk and resource cost while increasing insights and timeliness of the decision at hand.
1. Shoot From The Hip: When a quick decision is necessary, all eyes turn to the Executive to make the right decision. Without the time for an analysis to update or confirm their beliefs, they must rely on the similarity of the current question to decisions that have been made in the past. This is the best that can be done within the time limitations and available knowledge at the time of need. Outcomes, whether they are good or bad, are a learning experience for future quick decisions.
The trap is when the Executive believes that their tribal knowledge is sufficient, even when time is available to test their assumptions. The Executive Scientist always defaults to the analysis rather than the gut decision if time and resources permit.
2. Poor Analysis: This is the worst quadrant to be in. This occurs when you have the resources to perform deep learning of your business question, but the project is poorly planned, context isn’t communicated, information isn’t transparent and incentives are misaligned among stakeholders leading to a research disaster. The results have questionable application to the original question and significant resources are spent while not contributing to the timeliness of the decision needed.
The Executive Scientist does everything possible to avoid this outcome through proper problem statement formulation, context sharing, and promoting a culture of communication, transparency and legibility.
3. Analysis Paralysis: Even with proper planning and communication, you can still miss the mark when the scope of the analysis becomes too large or when perfection outweighs the good. In this case, your project is suffering from analysis paralysis. The team is researching the right question that will produce value, but the timeliness misses the mark and will result in an opportunity loss.
The Executive Scientist mitigates this by encouraging a culture of open communication, and by getting involved early and asking questions throughout the analysis. Each new idea or question that results from the analysis is recorded, and each is questioned as to whether or not it is necessary to achieve the desired outcome. Further research can be done during the time between urgent and important requests.
4. Risk Hedging: This is the best quadrant to find yourself because your organization has made pertinent information highly available. This is done in many ways; the productization of analysis conducted during a past crisis, anticipating large risks and preparing useful KPIs to monitor against them, or by curating data in a way that allows frictionless ad hoc analysis. Data-driven decisions can be made quickly and a post mortem can update existing KPIs with new learnings from the outcome.
To achieve this, the Executive Scientist balances the cost and benefits of procuring preemptive information. Over preparation has a cost in time and money and creating endless dashboards of KPIs - just because you have the data, sometimes can offer limited or even negative value to the Executive.
An Executive Scientist is a business decision making mindset for anyone in a position requiring them to make impactful choices. The mindset balances the risk and rewards associated with the timely needs of a decision against the known value accrued through proper analysis, belief testing, and legibility of conclusions necessary for data-driven decision making.
By adopting this mindset, the quality of decisions will be upgraded, resource waste will reduce and risk will be degraded.
In the next article in this series, we will cover what it really means to be data driven in organizational decision-making.
About the Authors
Chris Frazier has been an executive for over 10 years. He evolved from being a data analyst to becoming a BI and product leader, designing and implementing data driven solutions to a successful media tech firm. Chris is now the CEO and Co-Founder of BlockScience Labs, a data science product company that builds solutions that support both System Engineers and Executive Scientists to make better business decisions through scientific processes.
Dr. Michael Zargham earned his PhD in Electrical and Systems Engineering at the University of Pennsylvania in 2014 where he developed novel methods for decentralized dynamic resource allocation in networked systems. Zargham is the founder of engineering research and design firm BlockScience, which specializes in estimation, decision, and control of complex societal and economic systems. He is affiliated with the Vienna University of Economics and Business in the Interdisciplinary Institute for Cryptoeconomics.
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