Navigating Data-Driven Decisions in Your Organization

Data-driven decisions are those you, or your company, make using data to inform your conclusion—rather than intuition, personal preference, or other reasons you might choose one path over another. The data-driven decision-making process (DDDM) is a framework for setting the goals and metrics you’ll track by collecting and analyzing data. This informed process ultimately leads stakeholders to a decision built on facts and analysis. 

Data-driven decisions can help your company meet its business goals, such as increasing your customer base or engagement, improving strategic plans, or entering new markets and designing new products. Explore the process of making data-driven decisions and how they can benefit you and your organization. 

What are data-driven decisions, and why are they important?

Data-driven decisions are choices that a company or organization makes based on data and analysis of past performance, market indicators, and other significant metrics. This data can provide insight into how effectively your organization meets its business goals. It can also help you identify areas for improvement or reduce the risk of starting new projects. 

Making data-driven decisions begins with having a clear understanding of your company’s goals and subsequently collecting data to provide insight into how well your organization is accomplishing its goals. Once you have the data you need, you can apply advanced data analytics techniques to gain insight into what the data means. Then, armed with the best information, you can make an informed decision that reduces risks. 

What are the benefits of data-driven decision-making?

Making company decisions using data offers a host of benefits for your organization. Consider how data-driven decisions can help you: 

• Make decisions with confidence: Data provides a more concrete foundation to make decisions compared to personal preference or having a hunch. As such, you can be more confident that you’re making the best choice and that you’ve considered enough scenarios. 

• Save money: You can use data to gain insight into how your customers will respond to products or programs, so you can focus on the best investments, avoid costly mistakes, and explore different solutions to find the most cost-effective. 

• Improve customer retention and satisfaction: You can use metrics—like customer surveys—to understand how customers feel about your brand and their experiences with it. This helps you make decisions that speak to your customers' needs and wants.

• Improve employee satisfaction: Similarly, you can use employee surveys and other metrics to determine the factors that drive employee engagement and satisfaction in their role.

• Make proactive—as opposed to reactive—decisions: You can use data to make decisions proactively to navigate changes smoothly and skillfully instead of scrambling to react when the competitive landscape changes.

• Locate growth opportunities: Data can provide insights across several facets. These include new markets, products your customers may be interested in, features that you can provide or add to new or existing products, and other opportunities to grow and meet your business goals. 

• Protect against bias: Human decision-making is prone to bias, but data can help you focus on the facts and avoid unseen errors based on internally held predispositions. 

Types of analysis for making data-driven decisions

You can use advanced data analysis techniques to gain insight for data-driven decisions. The four main types of data analysis are: 

• Descriptive analytics: Built on historical data, descriptive analytics helps you understand what happened, such as company sales, social media engagement reports, or customer ratings. 

• Diagnostic analytics: Using descriptive analytics as a base of information, diagnostic analytics explores why things happen. These include such factors that influenced your sales or campaigns that impacted your website engagement.

• Predictive analytics: Predictive analytics helps you understand what might happen in the future, using your descriptive and diagnostic analytics to guide predictions. 

• Prescriptive analytics: Prescriptive analytics builds on predictive analytics to consider a wider range of factors, such as the overall state of your market or supply chain challenges. 

How to implement data-driven decision-making in your organization

Once you understand the importance of making data-driven decisions, you might want to harness the power of data in your own organization. Explore a step-by-step approach to implementing DDDM, how to measure key performance indicators, and best practices for making data-driven decisions. 

Data-driven decision making: Step by step

To implement a data-driven decision-making strategy, you’ll need to follow these four steps: 

• Step 1—Identify company goals: Your company goals should guide all of the decisions it makes. So, the first step is to understand what your team wants to accomplish. Once you’ve completed step one, you’ll have a clear idea of what success will look like in the end. 

• Step 2—Collect and organize data: In this step, you’ll need to determine what data will help you understand your company’s current situation compared to your goals. For example, if you want to increase your sales, you might collect financial data. This includes your current sales, marketing data like online engagement or email open rates, and other metrics like customer feedback or competitive analysis. 

• Step 3—Visualize and analyze: Once you have the data you need, you will organize and visualize the data so that it’s easier to engage with. You can analyze the data to draw insights about company processes, customer opinions, price points, market position, and more. You can use this information to determine where you can change course or take advantage of new opportunities to help you reach your goals faster. 

• Step 4—Report findings: Unless you are the sole decision-maker in your organization, you will need to share your findings with other stakeholders so your entire team can benefit from the insight of data-driven decisions. 

How to measure data-driven decision-making key performance indicators

You should consider a fifth step to this process: measuring DDDM key performance indicators to measure success and reevaluate your organization’s progress. One way that you can measure this success is to compare your KPIs before and after implementing your data-driven decisions. The difference in this before and after evaluation can help you determine whether your decision was the best. Was it the most informed decision? Or can you improve and adjust your company’s course of action to gain even better results? You should also reevaluate often to address the constant flow of change in any company, whether internally or due to external factors—like market pressures or customer preferences. 

Tips for better decision-making

Data-driven decision-making is important for any organization. While following the steps above, keep these tips in mind: 

• Explore data visualization: Data visualization, or using visual cues like charts and graphs to illustrate data, is a great way to gain a deeper understanding of the data. It helps you to find patterns and share information in a digestible format with other members of your team. 

• Seek patterns everywhere: One of the ways that data helps you drive decision-making is by uncovering patterns you didn’t notice before. Practice this skill by looking for the patterns behind everything in your world, using data to confirm your hypothesis. This skill will help you develop a more analytical approach to problems. 

• Work as a team: Even while following a DDDM strategy, unconscious bias and human error can still make their way into your decision-making process. Another strategy to combat this problem is to work in teams to provide more perspectives to every project for more diverse strategy sessions. 

Overcoming challenges in data-driven decision-making

Making the best decisions possible can sometimes be more difficult when challenges arise in the DDDM process. Common challenges (and how you can overcome them) include: 

• Data quality: If you have poor data or poor organizational processes for collecting, storing, managing, and interacting with data, you may run the risk of inaccurate information guiding your decisions. You can overcome this challenge by creating thoughtful policies for how your company will manage data and educating your employees on the proper way to interact with it. 

• Ethical concerns and data illiteracy: When your company collects customer data, you will need to be careful to navigate privacy and other ethical concerns. Well-developed data management policies can help you implement ethical concerns, but you will also need to ensure your employees have the data literacy to successfully meet the expectations of your policies. Make sure to provide the necessary training so your employees understand their role in data privacy and how to extract the best insight from data. 

Learn about data-driven decisions with Coursera

Making data-driven decisions is an important part of directing company strategy. If you want to explore how to make data-driven decisions, consider an online course like Ask Questions to Make Data-Driven Decisions offered by Google on Coursera as part of the Google Data Analytics Professional Certificate. Or explore the Data-driven Decision Making course offered by PWC, part of the Data Analysis and Presentation Skills: the PwC Approach Specialization. If you’re ready to jump into some hands-on skills, consider the Guided Project Introduction to Data Analysis using Microsoft Excel.