In today’s business landscape, decisions are based on data. Leaders are thankful to have systems that gather data to gain insights that guide the best outcomes. But translating data into decisions is a skill that must be developed over time.

So, how can COOs learn to use data effectively to make the best decisions for their company? This article provides some valuable tips.

Using Data to Guide Decisions

Start with the Question

Before gathering data, you must determine what question you want to answer. For example, as COO, you may wonder whether to change your inventory ordering process or consider adding a second shift to improve productivity.

Having a question in mind will help you determine what type of data you need and how to source it.

Define Your Metrics

Data provides metrics, so you should determine which metrics apply to your situation. Common metrics measured in operations include:

• Efficiency, such as cost per unit or labor productivity output
• Effectiveness, including order fulfillment and delivery rates
• Quality that can be measured in defect and error rates and returns
• Resilience, such as capacity utilization and supplier performance

Once you identify your metrics, tie them to a decision. Consider how various outcomes will affect your metrics.

Look for Meaningful Changes

Numbers can fluctuate if nothing real has changed. Before you act, ask yourself whether the changes represent a real improvement in performance or just normal wiggles.

To ensure the right approach, look at trends over time. Don’t act on small changes that occur daily.

Consider Both Qualitative and Quantitative Inputs

• Qualitative inputs: What you can count or measure
• Quantitative inputs: Non-numeric information such as feedback, notes, or observations

Quantitative data tells you what’s happening, while qualitative data tells you why it’s happening. As such, both should be considered in decision-making processes.

Use Simple Frameworks

There’s no need to overcomplicate the decision-making process. A simple framework will do.

In most situations, you can list two options and pros and cons in terms of cost, risk, customer satisfaction, and other factors. Determine if the pros outweigh the cons when determining whether to move the system forward.

Design Experiments

Not sure which way to go? A/B testing and similar experiments can guide the way. Choose the method that’s producing the best metric to guide operations.

This method can help reduce indecision and possible arguments in the workplace.

Turn Analytics Into Actions

Don’t just review charts and data. Consider how it can guide and change how you operate. Analyze what led to positive or negative results, and adjust these processes in your business model accordingly.

Focus on Short-Term Outcomes and Long-Term Goals

It’s easy to get caught up in data, and it can impact your time horizons. For instance, when making a long-term strategic decision, you may discover small issues that may take you on another path. Similarly, you may start obsessing about how small changes can impact the bigger picture.

It’s vital to focus on your goals. Don’t let today’s minor issues impact long-term planning, and don’t drag long-term questions into daily huddles.

Build Decision Logs

Frameworks are useful, but leaders should also consider developing decision logs to be revisited every quarter, 6 months, or annually. The log should review the main question, the decisions considered, the key data used, what was not known, the assumptions, and the expected outcomes. The idea is to improve your organization’s decision-making processes over time.

Invest in Data Quality Where It Matters

Quality, highly accurate data is beneficial, but it costs time and money. It should be used to make high-impact decisions such as hiring plans, capital spending, pricing, major contracts, and customer service commitments.

Lower quality data could include social media interactions, website engagement, and customer satisfaction scores. These roughly accurate statistics can be used for context, curiosity, or low-stakes decisions, such as blog topics and other matters that are not worth a heavy data-governance effort.

Train Your Managers in Data Literacy

Your managers may know how to read data, but can they interpret it? Train them to ask how their decisions can guide metrics and what that means for the big picture. Encourage them to make comparisons and consider different scenarios.

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