Our prior post on data-driven cultures discussed how adopting the right mindsets can improve your decisions by combating the cognitive biases that can cloud your judgment. In this post, we examine why you need to systematize your decision-making processes to reinforce the behaviors found in data-driven cultures.
Examining your Decision Process
As we noted, data-driven cultures are obsessed with an open-minded pursuit of the truth, supported by rigorous analysis. The combination of these two factors determines the quality of decisions. To accomplish this, you need to channel your obsession into creating a systematic process that can be learned, practiced, and improved daily. In other words, your daily behaviors and actions are guided by a clearly defined decision process.
There are three reasons for doing this:
1 – You can’t improve something you don’t fully understand
Most decision processes are not well understood, documented, or objectively evaluated. You first need to raise awareness of how you are making decisions, closely examine your process, determine where there are deficiencies, and commit to continuous improvement. This can only happen if you define and stabilize your process, removing variability in your approach to isolate breakdowns and bottlenecks.
2 – Shrink the change and build momentum.
Continuous improvement means becoming comfortable with change. By isolating your improvement efforts on a specific decision process (think financial or sales forecasting), you are better able to manage the change necessary and build momentum with early successes (i.e., new insights and measurable process improvements). This will accelerate the change process.
3 – Align teams around a clear process and common purpose.
Research shows that people are motivated by autonomy, mastery, and purpose.i By aligning your team around a defined process, autonomy and mastery grow as they learn, internalize, and improve the decision process. By mastering the process and required skills, people have more time for autonomous tasks like creative thinking and problem-solving.
Defining a Decision Process
Before we go any further, let’s define a decision process and how it differs from a business process. A decision process is a set of discrete steps individuals or teams use to evaluate and analyze information to create insights that fuel decisions. At a high level, it involves locating and evaluating evidence, structuring it for analysis, and making decisions based on this analysis.
Every decision involves predicting the future and, ideally, keeping score as to the accuracy of your predictions. Decision processes usually are embedded in a broader business process, with the distinction that the output of a decision process is an agreement to take some action. The output of a business process is typically a tangible thing (e.g., a product or service) resulting from many cumulative decisions.
Examples of decision processes include a consumer goods manufacturer engaging in demand planning to inform production schedules. Or the finance function of a fast-growing tech firm projecting cash flow and profitability to ensure hiring new staff doesn’t outpace financial realities.
A complete discussion of all the decision types is beyond the scope of this post. Still, a critical takeaway is that the mistake most leaders make is underestimating the complexity of decisions, which translates to overlooking opportunities for improvement. Furthermore, improvement doesn’t mean a single, significant change (which can be enticing but unattainable) but incremental progress over time, and dozens of decisions.
DPI: Change and Continuous Improvement
To improve decision processes, teams must first understand how the process currently works, make assumptions about what might improve it, align stakeholders around the goals for improvement, and embed the new behaviors in the decision processes. This is straightforward — even intuitive– but is rarely done inside organizations. In their book, The Knowing-Doing Gap, two Stanford professors may have found the reason people too often know what to do but still do not follow through:
“…the answer to the knowing-doing problem is deceptively simple: Embed more of the process of acquiring new knowledge in the actual doing of the task.”
In other words, continuous improvement of your decision process is not an intellectual exercise; it’s a formal practice. We call this formal practice Decision Process Improvement or DPI. At its core, DPI is about improving operational performance by increasing the speed and quality of decisions. Let’s dive in to see how it works.
How DPI Works
Using DPI, teams start with a decision process (e.g., loan processing, or sales or financial forecasting) and work backwards, looking at all the inputs and outputs that informed the decision and identifying areas for improvement that will impact the speed and quality of decision-making.
The key elements of DPI include:
1 – Mapping the major stages of the current decision process.
2 – Listing the task and activities occurring at each stage.
3 – Identifying the people/teams involved and their roles and relationships, highlighting key stakeholders like the internal “suppliers” of information and “customers” of the output.
4 – Documenting the inputs (data) going into the process as well as outputs (analysis) produced at each stage.
5 – Logging issues and blockers at each stage.
6 – Capturing the time, effort, and cost by stage and cumulatively for the entire process (hours, days, FTEs).
DPI analysis isolates, examines, and deconstructs existing decision processes, identifies issues and bottlenecks, recommends improvements, and provides a prioritized roadmap for process improvement. Importantly, it identifies the scope and nature of the change required across people, processes, tech tools, and data.
Common Decision Process Pitfalls
Over the years, we’ve worked on projects across industries building advanced analytics solutions for a wide variety of challenges, including fraud detection, manufacturing demand forecasting, pricing optimization, product defect rates, sales forecasting, and loan portfolio risk. While each of these use cases has slightly different underlying decision processes, we’ve seen a consistent pattern of decision process pitfalls.
The following are some of the contributing factors, organized by high-level decision process phases, that we’ve found lead to slower, lower quality, and more resource-intensive decisions.
Major Decision Process Phases and Common Pitfalls
Gathering and organizing data
Using limited data. Think outside the box (and perhaps your team) when identifying the data you use to inform decisions. Focusing on the metrics that tell the most favorable story can hide trouble brewing in your operations. Ask yourself whether other metrics may give you a more accurate picture.
Missing data. You may want to expand your analysis with additional data but can’t access it. In most large organizations, getting access to data outside your business unit or function can be challenging. Even if you get access, it may take too long or not be in a usable format, thus requiring additional time and effort to get it in shape. How can you address these bottlenecks?
Analyzing and reporting
Task orientation. Often, the people doing the analysis can be more focused on completing the task of building a dashboard or report than engaging in thoughtful analysis of the underlying data. For the analyst, the mindset should be that of an investigator searching for patterns and trends that may not be obvious, not the mindset of an assembly line worker producing widgets. Are you and your staff more focused on task completion or thoughtful analysis?
Lacking context. Do you stop at the first answer or dig deeper when doing your analysis? You may be limited by your data (see Missing Data above) or lack a complete understanding of the context of a given situation. Regarding the latter, you might not understand all the factors that could impact an outcome, so you focus only on readily available information. Ask yourself, what am I missing? What am I not seeing? Who can help me understand?
Making decisions based on analysis
Deferential communications. Your process involves group decision-making and creating consensus, but you may not be getting multiple perspectives. Group dynamics can limit open discussion and dissent; often, the first, loudest, or most senior person in the room dictates the outcome. Are you allowing for respectful dissent and disagreement in your group decision-making process?
Lacking skepticism. Group dynamics can lead to conformity that stifles dissent and healthy skepticism. But, as author Phil Tetlock noted in his treatise on the art and science of forecasting,
“Consensus is not always good; disagreement not always bad. If you do happen to agree, don’t take that agreement—in itself—as proof that you are right. Never stop doubting.”
Whether in a group or individual setting, challenging your beliefs or assumptions is essential to better decisions. Do you see skepticism in you and your colleagues as a healthy personal trait?
Keeping Score: Evaluating your Decisions
Not looking back. Keeping score or auditing results helps expose blind spots in your thinking and breakdowns in your decision process. You can’t learn from mistakes if you don’t see and acknowledge them. Do you have a way of tracking your past decisions to help inform and calibrate your future decisions?
Conclusion
Annie Duke, author, consultant, and accomplished professional poker player, attributes her success over the years in making rapid, high-stakes decisions with real money on the line to developing a high-quality decision process.
Why is it so important to have a high-quality decision process? Because there are only two things that determine how your life turns out: luck and the quality of your decisions. You have control over only one of those two things. What you do have some control over, what you can improve, is the quality of your decisions.
Once you raise the visibility of your decision processes and commit to continuous decision process improvement, you will get closer to the data-driven culture you need to make faster, better decisions.
Other Blogs in the Series:
Data-Driven Change: Essential Mindsets
