Data-Driven Decision-Making Approaches for Your Operating Team

May 8, 2024 | Leadership, Operations Best Practices

Data-driven decisions. It’s a term that’s becoming a bigger part of business conversations. And it makes perfect sense. It’s essential to consider data to base current decisions on past outcomes and behaviors.

But a COO must ask themselves, “Which data should I rely on?” And “How should I incorporate it into my decision-making process?” This article will tell you everything you need to know.

Know Your Goal

A leader must first understand how their decision will drive their company forward. On one level, they must consider the immediate outcome of their decision. On a bigger level, they must consider how their decision will affect their company on a larger scale.

Factors like KPIs and ROI will weigh heavily on their decision-making objectives.

Identify Data Sources

COOs must identify what data they need and the best way to source it. Data and sources will vary depending on the decision you are faced with. You may use reporting tools that allow you to review internal data based on past company performance, or you may review external data such as market trends or competitor metrics.

Here are some key metrics to consider.

  • Gross Profit Margin: Gross profit margin measures how much your company earns after expenses. Profitability is key in guiding decisions. This metric is typically accessible through your company software.
  • Return on Investment (ROI): ROI tells you how much money your company made or lost when using a specific product or service. It can also measure the success of your campaigns. These metrics will help you determine if you made a wise investment. You can decide whether you should continue to invest in that product.
  • Customer Activity: Customer activity can be used to measure the success of a marketing campaign, product offerings, customer service, or any other tool that is part of the B2C transaction. Common metrics to study include sales, engagement, and churn.
  • Productivity: You can arrive at this metric by dividing your total output by your total input. It can help you determine the efficiency of your staff members, the tools you are using in the workplace, and your leadership approach.

Organize Your Data

A leader must often rely on several pieces of data to arrive at a final decision. The data should be available at a glance to provide an overview of how all factors work together. For example, customer activity must be considered alongside financial data to gain an accurate overview.

Many reporting tools offer an executive dashboard that can be customized to your preferences. It will allow you to review your data and gain a big picture that helps guide your decisions.

Clean Your Data

Data cleaning is necessary in the analysis process. It involves the following steps:

  • Check for Errors: Check for errors like missing information, typos, and inconsistencies. Make corrections as needed.
  • Use a Standardized Format: A standardized format will make data easier to review. It may involve looking at dates, currencies, or other formats.
  • Remove Irrelevant and Duplicate Data: Remove irrelevant data to gain a clearer view of key metrics.
  • Consolidate Data: In some instances, you may need to consolidate data into a summarized version to gain a clearer view of your key metrics.

Analyze Your Data

There are various types of data analysis. The one you use will depend on the type of data you are reviewing and your goals. Here are some common options.

  • Data Mining: Data mining involves transforming big data into an actionable activity. It often involves reviewing customer behavior and emotions. It is commonly used for articles, feedback, surveys, and reviews.
  • Statistical Analysis: Statistical analysis allows you to examine data and draw conclusions. It helps companies study customer behavior and product success to predict future market demands.
  • Diagnostic Analysis: Diagnostic analysis studies data anomalies to determine why they happened. It can be used to determine the reasons behind sudden increases and declines in sales.
  • Predictive Analysis: Predictive analysis looks at historical data to predict future trends. It can be used to determine various business scenarios including customer behavior, risks, opportunities, and market trends.
  • Prescriptive Analysis: Prescriptive analysis uses advanced algorithms and data to determine the best course of action in any situation. It can be used to help leaders make inventory decisions, improve customer service, predict demand, and more.

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