How to Speed Up Your Decision Making With Data

Regardless of your professional role, qualifications, and status, every employee is faced with making vital decisions throughout their lives and careers. However, high-ranking executives like CEOs, CFOs, COOs, etc., often have to make impactful, profound decisions that change more than their immediate surroundings. These choices will affect the company culture, how the public views the organization, and any lasting relationships with clients and business partners. All these factors, when put together, result in each decision having a long-term impact on financial stability and prosperity.

Therefore, it’s vital to make a decision relatively quickly. But is there an alternative? You can make some decisions with shorter notice, but with quality and cohesiveness as a priority.

This article will debate the effectiveness of timing and timeliness and whether a few quality decisions outweigh the sheer quantity of rapid-fire choices. The truth always lies somewhere in the middle. But there are ways to inch closer to one end of the scale without putting too much at stake and delivering an excellent result.

The Variables of Decision Making

When someone needs to make a decision, it’s not always a black and white choice. Various factors will produce uncertainty and unanticipated lasting effects, and each choice will need to blend accuracy and speed to achieve the best result.

However, there’s a third aspect of the decision-making process that the professional world has only begun to utilize: data-driven decision-making (DDDM).

Data-driven decision-making relies on using established and gathered data points from internal and external sources. This data is then processed to guide the decision-making process rather than rely on gut instincts and loose observations.

Most companies and executives are aware of data-driven marketing and web-design concepts. These concepts have become popular over the past years as AI technology and data processing made colossal strides towards effectiveness and human readability.

DDDM works similarly. It uses existing data points and previous decisions, proposals, and results to extrapolate possible outcomes. DDDM allows companies to make decisions with efficiency, vigor, and speed without sacrificing quality.

To put things into perspective, the three critical aspects of the decision-making process will always be:

• Accuracy: How good is the result of each decision or how close to the best possible outcome did you get. Accuracy isn’t reliably quantifiable since the term ‘best outcome’ is often loose and isn’t well-known until after the decision has been made and its effects observed.
• Speed: How fast was a choice or decision made. There’s usually a lower limit where a decision is made quickly within reason, but is not impulsive, which is the goal companies try to achieve.
• Data-driven decision-making (DDDM): How much data did you use to reach a conclusion and make a choice? Is data high-quality or high-volume? All of these things can influence the process and the result.

If you like to view the professional world of decision-making through a mathematical lens, these three variables can be put into a formula to describe the process:

[Accuracy] x [DDDM] / [Speed] = [Growth Rate]

Companies need to make decisions that are accurate and based on real-time data and previous observations. This will increase their growth and allow them to outcompete organizations relying on instinct and following trends. Your growth rate will heavily depend on how much data you can compile and use. Business owners can use this data-based decision-making process for both day-to-day decisions and wider choices that can shape operations and departments.

Quality vs. Quantity in Decision-Making

The above equation is easy to conceptualize but challenging to implement. Primarily, there are many factors to consider when making a decision. There will always be more data to gather and research to conduct for past decisions and outcomes, and you’ll seldom catch up with the present.

Usually, higher executives will be pressed for time and forced to decide before all of the possible data can be compiled, processed, and assessed. Taking more time to choose can be beneficial and give a better result compared to a gut decision. For some departments, decision-making has to be as quick as possible to keep up with the rapidly-changing business environment, trends, and competition.

Decision-making and the equation above are all about striking a balance between speed and accuracy or quality. But the point where one starts to outweigh the other can be flexible and will depend on your company’s endeavors, culture, and standing in the industry.

According to Amazon’s founder and CEO, Jeff Bezos, the middle point is around 70% of possible data. If you wait for more than 80%-90% of the data to be processed, your growth rate will suffer. With smaller data points, it’s beneficial to be more effective at course-correcting and recognizing bad choices than accuracy. Once you master alleviating the bad outcomes, bad decisions might not be as damaging as you think. But being slow to choose will always have negative consequences.

Using Data to Make Quality High-Velocity Decisions

Now that you know the decision-making process and how to speed up your company growth rate, getting optimal results will take time and patience. However, here are some helpful tips to get started and make the most headway.

1. Establish a Process, Then Follow It
   The same principles that define good leadership also apply to the decision-making process. With a thorough process to follow, every team member is on the same page and knows where to go next or course-correct when needed. Having a strategy will be doubly effective if your organization has recurring projects that require choices, optimizing straightforward decisions purely based on previous results.
2. Create a Decision Model
   Complex decisions will often have more than two options, and multiple options can lead to a positive or the “best” result. Some decisions will impact more than one department, and others rely on more than one variable. For these choices, you need to create a model that considers the cost-benefit interaction of those variables. Structuring the model might take some time and effort, but it will become a part of the decision-making process and improve subsequent models.
3. Don’t Forget the Deadline
   Most decisions will have a deadline that you can’t afford to miss. Alternatively, some other projects might depend on a particular choice or option to go forward. In these cases, it might be more beneficial to make a quicker decision with less planning and data, streamlining and finishing other tasks, and correcting if needed.
4. Start Slow and Expand
   Developing a thorough process and business management cases can be slow and ineffective when starting. It’s usually better to implement a simpler decision-making process, calculate the outcomes, accuracy, and speed, then correct when you have more data at your disposal. Once you’ve progressed far along, you can update the process with additional steps, checks, or tasks to allow for better calculations and accuracy. This works much like predicting the weather patterns. When a storm is far away, you can’t tell where it’s going to hit, but it becomes more manageable the closer it is.
5. Don’t Overcommit
   Making impactful decisions can be risky and time-consuming if you don’t have alternatives or fallbacks. If you develop a strategy based on only part of the data, you can make a series of smaller, partial commitments. They allow companies to take fewer losses for bad decisions, correct their course quicker, and spot any ramifications and outcomes sooner. Over time, you’ll learn what path is better and how to achieve it.
6. Get the Team on Board
   As soon as you reach a decision, you need to act quickly. With modern communication, getting entire teams and departments updated is a much simpler task than it used to be a decade ago. Plus, there’s no need to wait for the next board meeting. You also need to develop the implementation strategy. Ask yourself who needs to do what and when then see how it can be done.
7. Confirm Everything
   When you make a decision, mark the data for that project down for future reference. AI-driven predictive data thrives on having as many valuable points to pull from, and each decision will lead to more successful ones in the long run. Once the business has gone past the initial phases, decisions will blend and patterns will emerge. This can greatly expedite the decision-making process if you know where to look for past examples.

Speed Drives Innovation

Once you start making fast and accurate decisions, you can quickly go back, reverse course if needed, or double down on your efforts to get something done. More minor decisions don’t come with high enough risk to warrant taking things slow. Many of them can be annulled and compensated with another choice that delivers better results.

When making big decisions, not everyone on the team will agree on the best course of action. Having a detailed process in place and knowing the goal will help people commit to the choice regardless of their opinion. Once you make a decision, it’s vital to move forward and have few doubts.

If you have enough data to use, you can theorize about the best options at the time. Start with the theories and conduct research to conclude their effects before you even have to make a choice. If nothing invalidates an option, it’s usually the best one with most certainty. This evidence-based decision-making process relies heavily on data and provides a foundation for innovation.

Decisions One at a Time

Now that you know what makes for an effective decision-making process, it’s time to implement it in the company’s projections and conduct. Your accuracy and speed will heavily depend on how much data you can process, and that’s where an AI tool becomes indispensable. The folks at smartboost have got that part figured out, so check them out to learn more.

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