Making Decisions Driven by Data Analytics

Every business has questions they need to answer. They can be as small as “What tasks should I prioritize today?” and as large as “How are we going to achieve our five-year strategic goals?”

Where do you go to find answers to those questions? There are abundant business strategy resources, from books to seminars to LinkedIn gurus who can tell you how they grew their business. But how can you know if their strategies will work for your business? Sometimes, you can spend years or months trying to implement them before you realize they don’t work for you and that efforts and resources have been wasted. 

Or perhaps you feel you have good instincts and know how to trust your gut. That can be valuable, but what happens when you encounter someone whose gut instinct is opposite to yours? How do you choose which way to go? 

These questions are designed to get you thinking about how your business approaches strategy. Every business question has a relatively straightforward way to find answers: using data to guide and back up your decisions. 

There is a lot of data to support every decision for your business, and that’s only going to grow—both in volume and in data’s ability to support strategic decision-making at your organization. Let’s explore how and why your company can best use data to achieve your objectives.

What is data-driven decision-making?

Data-driven decision-making (DDDM) is the process of using data to guide and inform business decisions—a fairly straightforward definition. In practice, this means your company goes from relying on things like gut instinct or outside gurus to using data to shape how and why decisions are made. This approach helps organizations make more accurate, objective, and effective decisions by leveraging data analytics to gain insights and predict outcomes.

By collecting, analyzing, and interpreting data, organizations can make well-informed decisions backed by factual evidence. This method does not entirely rule out the human elements of decision-making, but it does provide guardrails, support, and frameworks to ensure you’re making sound decisions for your business and mitigating the risks of bad decisions. 

There are several benefits to using data to drive decisions at work: 

• Data provides a solid foundation for decisions, reducing the likelihood of errors and increasing the accuracy of your decisions.
• Decisions are based on factual evidence rather than subjective opinions and provide enhanced objectivity.
• Data-driven insights can streamline processes and optimize resource allocation to improve efficiency.
• Data analytics can forecast trends, allowing businesses to predict future outcomes with more accuracy, anticipate changes, and prepare accordingly.

Data doesn’t solve every business problem, and it doesn’t always make strategic business decisions easier. But when you correctly use data to support your decisions, you can have more confidence in your choices and more realistic expectations of what is to come. 

Some benefits of data-driven decision-making can be best illustrated with examples of how companies can apply data to their decisions in real-world scenarios. Here are some ideas of how companies are using data to guide business decisions across various business functions and industries:

Optimizing inventory management. A retail company can collect data on sales trends, seasonal demand, and customer preferences to manage its inventory more efficiently. By analyzing this data, the retailer can forecast which products will be in high demand during specific periods and adjust its stock levels accordingly, reducing costs with overstock and improving customer experience by avoiding stockouts.

Enhancing marketing strategies. A digital marketing agency can use data analytics to track the performance of its campaigns. By combining metrics like click-through rates, conversion rates, website visits, and customer engagement, it can identify the most effective campaigns and allocate its budget more wisely.

Improving customer service. A telecommunications company can analyze customer feedback and support call data to identify common issues and trends. Based on the data, the company can proactively address recurring problems to reduce support resources. It can also enhance its support services by increasing staff during peak call hours or having top-performing support reps train other employees. 

Personalizing customer experiences. An e-commerce platform can use data on browsing behavior, purchase history, and customer preferences to personalize the shopping experience for its customers. By combining and visualizing these data sources, it can see which products are most frequently purchased together. Then, it can provide recommendations or promotions to drive consumers to purchase more. 

Streamlining production processes. A manufacturing firm can use data from sensors on its production line to monitor equipment performance and detect potential issues before they lead to downtime. By analyzing this data, it can implement preventive maintenance, optimize production schedules, and improve overall efficiency.

Benefits of data-driven decision-making

Data-driven decision-making can help lead to better outcomes for your business. While you likely have some idea of the impact your data can have, here are some additional reasons every company should incorporate data into the decision-making process: 

• Data provides insights that help organizations formulate effective strategies, and businesses that leverage data analytics can gain an edge over competitors who rely on traditional decision-making methods.
• Data analytics helps identify inefficiencies and areas for improvement.
• Understanding customer behavior through data allows companies to tailor their offerings, improving customer satisfaction and loyalty.
• Data analysis minimizes guesswork, leading to more confident decisions.
• Analytics can uncover hidden opportunities for growth and innovation, helping companies identify otherwise unknown opportunities.
• Data-driven decisions are transparent and can be traced back to specific data points.
• Companies can become more agile by quickly adapting to market changes through analyzing real-time data.
• Analytics can predict potential risks, allowing organizations to mitigate them proactively.

Unsurprisingly, the data backs this up. Businesses that adopt data-driven decision-making often experience increased success. A study by McKinsey & Company found that data-driven organizations are 23 times more likely to acquire customers, 19 times more likely to be profitable, and six times as likely to retain customers.

Types of data-driven decision-making

If you’re looking to deploy your data to improve decisions, it is helpful to understand what types of data analysis are available. We’ve put together a list of some of the ways data can be used in data-driven decision-making: 

• Descriptive analytics examine historical data to understand past performance.
• Diagnostic analytics investigate the causes of past outcomes by analyzing data patterns.
• Predictive analytics use statistical models to forecast future trends and outcomes.
• Prescriptive analytics suggest actions based on predictive analysis to achieve desired results.
• Qualitative analysis uses non-numerical data, such as customer feedback and opinions, to provide context and deeper understanding.
• Quantitative analysis focuses on numerical data and statistical methods to measure and analyze variables.
• Real-time analytics involve analyzing data as it’s collected so you can make immediate decisions and respond quickly to changes.

Steps to implementing data-driven decision-making

Your data-driven decision-making process must be unique to your organization. Your data sources, goals, and resources are unique to you and the challenges you want to solve with data. Here are a few steps you can take to implement data-driven decision-making in your organization:

1. As with any change management initiative, you need to first consider your end goal to define and shape the rest of your implementation process. Look at what you want to achieve from data-driven decision-making, what outcomes you would expect, and where you want the company to go.Once you’ve defined your objectives, you can work backward to have the people, tools, and processes in place to ensure your data objectives are met. Some examples of objectives could be: ​​
   • Improve the accuracy of business decisions by basing them on reliable, comprehensive data rather than intuition or incomplete information.
   • Streamline operations by identifying inefficiencies and optimizing resource allocation through data analysis.
   • Gain deeper insights into customer preferences and behaviors to tailor products, services, and marketing strategies, increasing customer satisfaction and loyalty.
   • Use data to identify new market trends and opportunities for innovation.
   • Use predictive analytics to forecast potential risks and implement proactive strategies to mitigate them.
2. Then begin collecting data. This step is two-fold. First, you need to make sure you’re collecting the right data to support your objectives. If your goal is to improve the performance of your marketing campaigns, then you’ll need to collect data relevant to those campaigns and performance. If you want to improve strategic decisions based on future growth, then gathering sales data combined with predictive models will help give a glimpse of potential future sales.Once you identify what data you need to bring in, you will need to determine how to use it and combine it to ensure your data set is accurate and complete. Many companies can run into challenges on this step. Some common challenges can include:
   • Disparate data sources can hinder comprehensive analysis.
   • Knowing how to ensure data accuracy and consistency.
   • Employees may be reluctant to adopt new data-driven practices.
3. Next, you need to build reports and automate data delivery. Choose the key performance indicators (KPIs) you need to track to make accurate decisions in daily, monthly, or annual operations and build reports that will automatically update as new data comes in. This way, team members can spend time thinking about how to use the data in their decisions quickly and take actions when insights are surfaced.Some tools that will help support this include:
   • Business intelligence tools
   • Data warehouses or data lakes for storing and managing large volumes of data
   • AI and machine learning (ML) tools for predictive and prescriptive analytics
4. Track the impact of those decisions. Use your data to justify using more data. Continuously track the outcomes of decisions and use them to refine your strategies and improve your processes.
5. Once you have processes that work for your company, improve data literacy across your organization and encourage other teams to start using data in their daily workflows as well.

Building a data-driven culture

Deploying data-driven decision-making in your organization isn’t just about turning on a data tool and letting people start using it. It takes time to build a culture that uses data to effectively achieve business goals. 

Your team needs to create a culture that values data. You can achieve this by: 

• Having leaders champion data-driven practices and encourage their adoption across the organization.
• Providing open access to data and insights to promote a culture of trust and accountability around data and decisions.
• Training and upskilling employees to better understand how to use data and deploy solutions within it. 
• Training employees to become more literate with data and help them understand and interpret data effectively.
• Providing specialized training for data analysts and scientists to enhance their analytical skills.

One critical aspect of a data-driven decision-making process is that it can’t be a top-down approach. Each business user needs to see how data affects their day-to-day roles. Encourage teams to share with each other how they’re using data. Get business users involved in exploring data on their own, so they can find ways to use data in their business decisions.

Examples and use cases of data-driven decision-making

Want to see some of the ideas and principles we’ve discussed in this article in action? We’ve pulled together some real stories of companies using data to drive decisions in their organizations—both at a high-level strategy and down to individual business users. 

Financial services

NAB is a bank in Australia that uses a data platform to help build out its marketing decision-making process. While its marketing was growing and the business was growing, NAB did not have any insight into what was working, the impact on the business of its marketing spend, or what best practices it could implement across business units to ensure they were optimizing marketing. NAB gathered data from more than 30 sources and brought it together in several dashboards that tracked high-level spend, spending across channels, and impact across business units. 

Data is now central to its decision-making processes for marketing. The team can look at where to adjust marketing spending to ensure each dollar spent has the most impact and ROI. It also uses statistical models on its historical data to spend more time with more accurate forecasts and predictive modeling, making the team more strategic. 

Logistics

UPS has empowered business users across its global organization to use data in their daily decisions. Before it could empower users with data-driven decision-making, UPS needed to improve its data infrastructure. Data was living in disparate places and was often incomplete or unusable, making it difficult to access the information employees wanted or trust the data they were able to get because they didn’t know if it was complete or accurate. 

UPS embarked on a process to upgrade its data experiences across the organization. It brought its disparate data into one platform and deployed new data techniques and reports across the company to save thousands of hours on manual reporting. UPS also encouraged employees to explore the data and see how they can use it in their jobs to improve data-driven decision-making across the company. 

Communications

TELUS is a Canadian telecommunications company that wants to use data to drive decisions. But one problem it faced was that gathering the relevant data took a long time. And, the data was so complicated that it was difficult to use in responding to current customer needs. 

To improve its data access, TELUS invested in a data management platform that helped streamline its data integration and reporting processes. Once it did that, reports and data were automatically updated, and users could spend time acting on and responding to the insights in the data rather than gathering it. This ensured the company could be more agile in responding to customer needs.  

Manufacturing

The manufacturing industry has tons of data available to help grow businesses and streamline operations.Emerson is using its data not only to improve and streamline its business but also to support its customers in their decision-making processes. Emerson Cold Chain helps keep perishable products at the right temperature as they move through the supply chain. The company uses data from IoT sensors to improve its operations and keep perishable products fresh. It also shares this data with customers, who use it to track compliance and verify processes on their end, ensuring transparency. 

Emerson has used its data to help grow partnerships with its clients. Both clients and Emerson turn to data to solve problems and overcome challenges. 

Ready to get started using data to improve your decision-making processes? Talk with Domo today.