Data-driven decision making: A step-by-step guide

If there’s a looming decision ahead of you at work, it’s often hard to know which direction to go. If you go with your gut feeling, you may feel more confident in your choices, but will those choices be right for your team members? When you use facts to make decisions, you can feel more at ease knowing your choices are based on data and meant to maximize business impact.

Whether outshining competitors or increasing profitability, data-driven decision making is a crucial part of business strategy in the modern world. Below, we dive into the benefits of data-driven decision making and provide tips for making these decisions at work.

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What is data-driven decision making (DDDM)?

Data-driven decision making is the process of collecting data based on your company’s key performance indicators (KPIs) and transforming that data into actionable insights.

You can use business intelligence (BI) reporting tools during this process, which make big data collection fast and fruitful. These tools simplify data visualization, making data analytics accessible to those without advanced technical know-how.  

What does being data-driven mean?

In short, the concept of being data-driven refers to using facts, or data, to find patterns, inferences, and insights to inform your decision making process. 

Essentially, being data-driven means that you try to make decisions without bias or emotion. As a result, you can ensure that your company’s goals and roadmap are based on evidence and the patterns you’ve extracted from it, rather than what you like or dislike. 

Why is data-driven decision making important?

Data-driven decision making is important because it helps you make decisions based on facts instead of biases. If you’re in a leadership position, making objective decisions is the best way to remain fair and balanced. 

The most informed decisions stem from data that measure your business goals and populates in real time. You can aggregate the data you need to see patterns and make predictions with reporting software.

Some decisions you can make with support from data include:

• How to drive profits and sales

• How to establish good management behavior

• How to optimize operations

• How to improve team performance

While not every decision will have data to back it up, many of the most important decisions will. 

5 steps for making data-driven decisions

Making data-driven decisions takes practice. If you want to improve your leadership skills, then you’ll need to know how to turn raw data into actionable steps that work toward your company initiatives. The following steps can help you make better decisions when analyzing data.

1. Know your vision

Before you can make informed decisions, you need to understand your company’s vision for the future. This helps you use both data and strategy to form your decisions. Graphs and figures have little meaning without context to support them. 

Tip: Use your company’s yearly objectives and key results (OKRs) or quarterly team KPIs to make data-backed decisions.

2. Find data sources

Once you’ve identified the goal you’re working towards, you can start collecting data. 

The tools and data sources you use will depend on the type of data you’re collecting. If your goal is to analyze data sets pertaining to internal company processes, use a universal reporting tool. Reporting tools offer a single point of reference for keeping track of how work across your organization is progressing. Some reporting tools like Microsoft’s Power BI let you gather data from various external sources. If you want to analyze marketing trends or competitor metrics, you can use one of those tools.

Some general success metrics you may want to measure include:

• Gross profit margin: Gross profit margin is measured by subtracting the cost of goods sold from the company's net sales.

• Return on investment (ROI): The ratio between the income and investment, ROI is commonly used to decide whether or not an initiative is worth investing time or money in. When used as a business metric, it often tracks how well an investment is performing. 

• Productivity: This is the measurement of how efficiently your company is producing goods or services. You can calculate this by dividing the total output by the total input. 

• Total number of customers: This is a simple but effective metric to track. The more paid customers, the more money earned for the business.

• Recurring revenue: Commonly used by SaaS companies, this is the amount of revenue generated by all of your current active subscribers during a specific period. It's commonly measured either monthly or annually.

You can measure a variety of other data sets based on your job role and the vision you’re working toward. Machine learning makes aggregating real time data simpler than ever before.

Tip: Try to create a connected story through these metrics. If revenue is down, look at productivity and see if you can draw a connection. Keep digging through these metrics until you find a “why” for whatever problem you’re trying to solve.  

3. Organize your data

Organizing your data to improve data visualization is crucial for making effective business decisions. If you can’t see all your relevant data in one place and understand how it connects, then it’s difficult to ensure you’re making the most informed decisions.

Tip: One way to organize your data is with an executive dashboard. An executive dashboard is a customizable interface that usually comes as a feature of your universal reporting tool. This dashboard will display the data that’s most critical to achieving your goals, whether those goals are strategic, tactical, analytical, or operational.

4. Perform data analysis

Once you’ve organized your data, you can begin your data-driven analysis. This is when you’ll extract actionable insights from your data that will help you in the decision-making process. 

Depending on your goals, you may want to analyze the data from your executive dashboard in tandem with user research such as case studies, surveys, or testimonials so your conclusions include the customer experience. 

Does your team want to improve their SEO tools to make it more competitive with other options on the market? The data sets you can use to determine necessary improvements may include: 

• Competitors’ performance data 

• Current SEO software performance data

• Current customer satisfaction data

• User research on a variety of SEO/marketing tools

While some of this information will come from your organization, you may need to obtain some of it from external sources. Analyzing these data sets as a whole can be helpful because you’ll draw a different conclusion than you would if you were to analyze each data set individually.

Tip: Share your analytics tools with your whole team or organization. Just like any collaborative effort, data analysis is most effective when viewed from many perspectives. While you may notice one pattern in the data, it’s entirely possible a teammate may see something completely different. 

5. Draw conclusions

As you perform your data analysis, you’ll likely begin to draw conclusions about what you see. However, your conclusions deserve their own section because it’s important to flesh out what you see in the data so you can share your findings with others. 

The main questions to ask yourself when drawing conclusions include:

• What am I seeing that I already knew about this data?

• What new information did I learn from this data?

• How can I use the information I’ve gained to meet my business goals? 

Once you can answer these questions, you’ve successfully performed data analysis and should be ready to make data-driven decisions for your business.

Tip: A natural next step after data analysis is writing down some SMART goals. Now that you’ve dug into the facts, you can establish achievable goals based on what you’ve learned. 

Tools and technologies for data-driven decision making

Effective data-driven decision making (DDDM) in the modern business landscape requires leveraging the right tools and technologies. Organizations can use these tools to collect, analyze, and interpret large amounts of data. This allows them to transform raw information into actionable insights that drive their business strategy.

Business intelligence software

Business intelligence (BI) software plays a pivotal role in data-driven decision-making processes. These powerful platforms aggregate data from various data sources, providing decision-makers with comprehensive dashboards and reports. Popular BI tools like Tableau, Power BI, and Looker offer robust data visualization capabilities, allowing users to create interactive charts, graphs, and maps that make complex datasets more understandable.

By using BI software, organizations can:

• Monitor key performance indicators (KPIs) in real-time

• Identify trends and patterns in business data

• Generate automated reports for stakeholders

• Enhance collaboration among teams through shared insights

Data analytics tools

While BI software focuses on reporting and visualization, data analytics tools dive deeper into the data to uncover hidden patterns and correlations. These tools employ sophisticated statistical methods and algorithms to analyze both structured and unstructured data.

Popular data analytics tools include:

• R and Python for statistical analysis and modeling

• SAS for advanced analytics and machine learning

• Apache Spark for processing large-scale data

• Excel for basic data analysis and manipulation

These tools enable data analysts and data scientists to perform various types of analysis, such as:

• Descriptive analytics to understand what happened

• diagnostic analytics to determine why it happened

• predictive analytics to forecast future trends

• Prescriptive analytics to recommend actions

Machine learning and AI in DDDM

Data-driven decision making has taken a significant leap forward in analytical capabilities with the integration of machine learning and artificial intelligence (AI). These technologies process vast amounts of data at incredible speeds, identifying patterns and insights that might be impossible for humans to discern.

Key applications of machine learning and AI in DDDM include:

• Predictive modeling for forecasting future outcomes

• Sentiment analysis for understanding customer opinions

• Recommendation engines for personalized marketing

• Anomaly detection for identifying fraud or errors

• Natural language processing for analyzing text data

Companies like Amazon use ML algorithms to optimize their supply chain, predict customer behavior, and personalize product recommendations, demonstrating the power of these technologies in driving business decisions.

Measuring the impact of data-driven decision making

To truly understand the benefits of data-driven decision-making, organizations must establish robust methods for measuring its impact on business performance.

Key Performance Indicators (KPIs) for DDDM

KPIs are essential metrics that help organizations track the effectiveness of their data-driven approach. Choosing KPIs for DDDM requires careful consideration of indicators that align with business goals and offer valuable insights into the decision-making process.

Some important KPIs for measuring the impact of DDDM include:

• Revenue growth: This KPI measures the impact of data-driven decisions on the company's bottom line. It quantifies financial gains from DDDM initiatives, such as data-driven marketing campaigns and data-informed pricing strategies.

• Operational efficiency: This KPI assesses process improvements resulting from data-driven insights. It may include metrics like reduced cycle times or increased output per employee, such as tracking production downtime reductions through predictive maintenance.

• Customer satisfaction: This KPI measures how data-driven strategies affect customer experience and loyalty. Metrics can include NPS, retention rates, or customer lifetime value. It tracks the impact of using customer data for product development and personalized experiences.

• Decision quality and speed: This KPI focuses on enhancing the decision-making process. It measures improvements in decision speed and quality by comparing the outcomes of choices made using data analytics versus intuition and assessing time-to-decision reductions enabled by real-time data.

By consistently tracking these KPIs, organizations can quantify the valuable insights gained from their data-driven decision-making process and demonstrate the tangible impact on their bottom line.

Data-driven decision making examples

While the data analysis itself happens behind the scenes, the way data-driven decisions affect the consumer is very apparent. Some examples of data-driven decision making in different industries include: 

E-commerce 

Have you ever been shopping online and wondered why you’re getting certain recommendations? Well, it’s probably because you bought something similar in the past or clicked on a certain product. 

Online marketplaces like Amazon track customer journeys and use metrics like click-through rate and bounce rate to identify what items you’re engaging with most. Using this data, retailers are able to show you what you might want without you having to search for it. 

Healthcare

In the medical field, data-driven decision making is revolutionizing patient care and treatment strategies. Hospitals and clinics utilize electronic health records (EHRs) to analyze patterns in patient data, helping doctors make more informed diagnosis and treatment plans. For instance, by examining historical data on symptoms, treatments, and outcomes, healthcare providers can predict which patients are at higher risk for certain conditions.

Additionally, pharmaceutical companies leverage big data to streamline drug discovery processes. By analyzing vast amounts of genetic and clinical trial data, researchers can identify promising drug candidates more quickly and efficiently.

Finance

Financial institutions use data in a multitude of different ways, ranging from assessing risk to customer segmentation. Risk is especially prevalent in the financial sector, so it’s important that companies are able to determine the risk factor before making any significant decisions. Historical data is the best way to understand potential risks, threats, and the likelihood they occur. 

Financial institutions also use customer data to determine their target market. By grouping consumers based on socioeconomic status, spending habits, and more, financial companies can infer what consumers have the greatest lifetime value and target them. 

Transportation

Data science additionally plays a huge role in determining safe transportation. The U.S. Department of Transportation’s Safety Data Initiative underscores the role that data plays in improving transportation safety. 

The report pulls data from all types of motor crashes and evaluates factors like weather and road conditions to discover the source of problems. Using the hard facts, the department can work toward implementing more safety measures.

Decision-making tools for agile businesses

In this ebook, learn how to equip employees to make better decisions—so your business can pivot, adapt, and tackle challenges more effectively than your competition.

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Benefits of data-driven decision making

Analytics-based decision making is more than just a helpful skill—it’s a crucial one if you want to lead by example and foster a data-driven culture. 

When you use data to make decisions, you can ensure your business remains fair, goal-oriented, and focused on improvement.

Make confident decisions

The businesses that outlast their competitors do so because they’re confident in their ability to succeed. If the decision-makers within a business waiver in their choices, it can lead to mistakes, high team member turnover, and poor risk management. 

When you use data to make the most important business decisions, you’ll feel confident in those decisions, which will push you and your team forward. Confidence can lead to higher team morale and better performance.

Guard against biases

Using data to make decisions will guard against any biases among business leaders. While you may not be aware of your biases, having internal favoritism or values can affect the way you make decisions. 

Making decisions directly based on the facts and numbers keeps your decisions objective and fair. It also means you have something to back up your decisions when team members or stakeholders ask why you chose to do what you did.

Find unresolved questions

Without using data, there are many questions that go unanswered. There may also be questions you didn’t know you had until your data sets revealed them. Any amount of data can benefit your team by providing better visualization into areas you can’t see without statistics, graphs, and charts. 

When you bring those questions to the surface, you can feel confident knowing your decisions were made by considering every bit of relevant information.

Set measurable goals

Using data is one of the simplest ways to set measurable goals for your team and successfully meet those goals. By looking at internal data on past performance, you can determine what you need to improve and get as granular as possible with your targets. For example, your team may use data to identify the following goals:

• Increase number of customers by 20% year over year

• Reduce overall budget spend by $20,000 each quarter

• Reduce project budget spend by $500

• Increase hiring by 10 team members each quarter

• Reduce cost per hire by $500 

Without data, it would be difficult for your company to see where they’re spending their money and where they’d like to cut costs. Setting measurable goals ultimately leads to data-driven decisions because once these goals are set, you’ll determine how to reduce the overall budget or increase the number of customers.

Improve company processes

There are ways to improve company processes without using data, but when you observe trends in team member performance using numbers or analyzing company spending patterns with graphs, the process improvements you make will be based on more than observation alone. 

Processes you can improve with data may include:

• Risk management based on financial data

• Cost estimation based on market pricing data

• Team member onboarding based on new hire performance data

• Customer service based on customer feedback data

Changing a company process can be difficult if you aren’t sure about the result, but you can be confident in your decisions when the facts are in front of you.

Challenges in implementing data-driven decision making

While the benefits of DDDM are clear, organizations often face several challenges when implementing this approach. Understanding and addressing these challenges is crucial for the successful adoption of a data-driven culture.

Data quality and accuracy

The foundation of effective data-driven decision making lies in the quality and accuracy of the data used. Poor data quality can lead to flawed analysis and, consequently, misguided decisions.

On the other hand, good data management ensures accurate and complete information for quantitative analysis. This involves standardized collection, regular audits, and addressing data gaps. With reliable data, organizations can make informed decisions and avoid costly mistakes.

Ensuring data security and privacy

Data security and privacy are paramount concerns as organizations collect and analyze increasing amounts of data. Compliance with regulations like GDPR, CCPA, and HIPAA is critical. Companies must implement security measures, including data encryption, strong access controls, and regular system updates. Safeguarding customer rights and maintaining transparency in data usage are also necessary.

Overcoming resistance to change

Resistance to change often emerges when implementing data-driven decision making. This cultural shift requires effective change management strategies. Clear communication of benefits, the involvement of key stakeholders, and addressing concerns openly can help overcome resistance. Additionally, equipping employees with necessary skills through training and mentorship programs is vital for fostering a data-driven culture.

Scaling data management

Managing large data sets presents both opportunities and challenges. Big data requires storage solutions such as cloud-based systems, data lakes, or hybrid models. Efficient processing of these large datasets is key to timely decision-making. Techniques like parallel processing, in-memory computing, and stream processing can help organizations handle vast amounts of data effectively.

By addressing these challenges head-on, organizations can create a foundation for data-driven decision making, enabling them to harness the full power of their data and drive business success.

Tips for becoming more data-driven

Data-driven organizations are able to parse through the numbers and charts and find the meaning behind them. Creating a more data-driven culture starts with simply using data more often. However, this is easier said than done. If you’re ready to get started, try these tips to become more data-driven.

Find the story

The key to analyzing data, numbers, and charts is to look for the story. Without the “why,” the data itself isn’t much help, and the decision process is far more difficult. If you’re trying to become more data-driven in your decision making, look for the story the data is telling. This will be integral in making the right decisions.  

Consult the data

Before making any organizational decision, ask yourself: Does the data support this? Data is everywhere and can be applied to any major decision. So why not consult it when making tough choices? Data is so helpful because it’s naturally void of bias, so make sure you’re consulting the facts before any decision. 

Learn data visualization

Finding the story behind the data becomes easier when you’re able to visualize it clearly. While learning how to visualize data is often the toughest aspect of establishing a data-driven culture, it’s the best way to recognize patterns and discrepancies in the data. 

Familiarize yourself with different tools and techniques for data visualization. Try to get creative with the different ways to present data. If you’re well-versed in data visualization, your data storytelling skills will skyrocket. 

Make data-driven decisions easy with reporting software

You’ll need the right data in front of you to make meaningful decisions for your team. Universal reporting software aggregates data from your company and presents it on your executive dashboard so you can view it in an organized and graphical way.

Decision-making tools for agile businesses

In this ebook, learn how to equip employees to make better decisions—so your business can pivot, adapt, and tackle challenges more effectively than your competition.

Get the insights