In the past, business leaders used intuition to make bold decisions about their companies with little research to support their ideas. With more data available today, teams are empowered to prioritize a data-driven approach. This surge in analytics-based decision-making has led to significant gains for organizations entering a new “big data” frontier.

Applying the data-driven decision-making (DDDM) model is crucial to your team’s success, but the framework is only as effective as your preparation, implementation, and maintenance. Learn how to use data to make decisions, the ins and outs of this model, and the proper steps to optimize your company’s data-based approach.

Why is data-driven decision-making important?

Data-driven decision-making is a business philosophy that uses data analytics above all else to inform decision-making. A business using DDDM opts to predicate its marketing tactics, business model, and even brand identity on improving specific key metrics. The goal is to remove intuition and guesswork from the equation, resulting in more predictable outcomes.

Here are a few benefits of DDDM:

• Increased transparency and accountability. Quantifiable metrics are easy to disseminate through your team, putting everyone squarely on the same page.
• Continuous improvement. Tracking key metrics and prioritizing their significance helps set solid goals and monitor progress.
• Increased consistency. When all members track results and progress using the same key metrics, communication is straightforward and the team’s overall output is cohesive.
• Cost savings. Guesswork involves significant trial and error, and each error involves wasteful spending. With a data-driven model, you’ll see more successful trials and avoid investing resources in the wrong initiatives.

Lufthansa, a German airline, is an incredible example of DDDM done right. When they moved to a unified analytics platform for their more than 500 subsidiary companies, their efficiency skyrocketed by over 30%. This could only occur when business leaders began to make consistent, data-driven decisions.

Data-driven vs. data-informed

A data-driven decision is based solely on metrics, while a data-informed decision involves other inputs like business objectives and brand identity. Both can result in a positive outcome, but a data-informed decision still relies partly on intuition. Teams using data-informed strategies may face analysis paralysis more than when considering data alone.

Usually more straightforward than data-informed decisions, a data-driven model is easier to automate. For example, Netflix uses customer viewing data to recommend shows they might enjoy. This is an automated, data-driven strategy that results in higher viewership. If Netflix used a data-informed model, it might gather the same data but rely on employees to make recommendations to specific customer groups.

Key components of data-driven decisions

DDDM relies on two primary components: accurate, relevant data and its correlations. Here’s how these aspects work in tandem for a successful model:

• Accurate, relevant data. If your analytics are inaccurate or irrelevant to tracking your goals, your decisions will suffer. Ensure you’ve validated all of your metrics before you use them to make decisions.
• Metric correlations. Correlations between metrics are a key component of data literacy because they guide teams in generating creative ideas for reaching goals. Encourage your team to ask why the metrics are the way they are. This way, they’ll understand data dependencies and be more confident in their approaches to manipulating the numbers.

How to make effective data-driven decisions: 6 steps

Proper DDDM requires setting highly specific goals and developing targeted solutions. It takes significant effort to craft a strong data-driven strategy and then track it closely. Luckily, the following six steps can simplify developing and maintaining your data-driven process.

1. Clarify business goals

Your first objective is to identify what gains you’d like to see. Begin with goals that are easy to measure, like increasing website traffic and reducing resource costs. You can use simple metrics to track these goals, and there are many ways to affect their outcomes.

After a few successful campaigns, apply your newfound expertise to more ambiguous targets, like improving employee retention or streamlining the customer experience. These goals are also measurable but require more creative analysis and complex metrics.

2. Poll your team for vital data sources

With clear goals in mind, you must identify the metrics you’ll use to track progress. Determining the correct measurements — and the tools you need to decipher and apply them — requires extensive data research. Consider bringing in qualified data researchers at this stage to help guide your efforts in the right direction.

Luckily, you can also rely on your team’s insights as a constant information source. Their expertise makes them suited to identify the best analysis tools and metrics for a given goal. And when it’s time to drive toward that goal, they’ll be prepared and motivated because they helped shape it.

3. Gather and organize essential data

Now, you’ll accumulate the relevant, accurate data that’s essential to DDDM’s effectiveness. Analysis tools track a wealth of metrics, so be selective and focus on those most closely related to your goals. Keep these data points orderly as you gather them — don’t let your metrics grow disorganized or fragmented into data silos.

The best data visualization and analytics tools have comprehensive dashboards, tables, and exportable spreadsheets. Optimize these resources and ensure everyone has access — the more informed your team is, the better their ideas will be.

4. Analyze and navigate data insights

At this point, you should have a mountain of metrics to sift through. Now, it’s time to unearth the most valuable gems from it. When you pinpoint the fundamental insights for informed decision-making, discuss them extensively with your team to reveal interesting aspects of your key findings.

Encourage your team to ask questions that help them better understand the data and how it all interconnects. Drawing accurate conclusions from a large data pool takes data literacy and expertise, so you may consult with expert researchers again to help guide the analysis process.

5. Use data to make decisions

With all your analytics in hand and a list of key insights, it’s time to act. Thankfully, a DDDM philosophy makes the right actions self-evident. The insights you analyzed should reveal the best course of action with little to no intuition.

For instance, if your goal is to cut costs, your metrics should show exactly where you can reduce spending. And if you aim to increase website traffic, you should easily see which strategies are working and which aren’t.

Sometimes, the insights you discover won’t easily translate into actions, which means you need more or different data. Make the best decision with the available information, but consider adjusting what you track going forward.

6. Monitor your decision’s impact by continuing to track metrics

After spending considerable time, money, and effort making a data-driven decision, it’s time to keep a sharp eye on how the choice plays out. Continue tracking your metrics to monitor whether your decision is achieving its goal. When you’ve gathered more insights, go back to the analysis step and seek opportunities for improvement.

Achieve your goals with Webflow

Transitioning to a DDDM model may seem like a complex shift in your business strategy, but the right tools and resources can streamline the process. Webflow Enterprise enables you to integrate many analytics and optimization tools into your website to help steer your data-driven decisions. With Webflow, you can create visually stunning, reliable websites that quickly expand to meet your company’s growing needs.