Data-Driven Decisions: The Key to Unlocking Business Growth

Daniel Thyrring16 Jun 23 • 14 min read

Blog > Manage


Data is the very backbone of the online world. It informs everything we do, and in turn, everything we do creates more data. This is a continuous cycle. Worldwide, 2.5 quintillion bytes of data are created daily (including but not limited to online), and at the time of writing more than five billion gigabytes of that data was on the Internet.

But why is data important for modern businesses?

Data is the fuel source for the modern business. It provides all the information an organization could possibly need to make better decisions. This is where data-driven decision-making comes into play. Data-driven decision-making, or DDDM for short, is the process whereby, instead of making decisions based on observations, intuition, or assumptions, businesses use data and facts to inform the decision-making process.

Businesses often practice DDDM, and use the facts, metrics, and data that have been gathered to guide strategic business decisions that align with your organization’s overall goals, objectives, and initiatives. This data can be anything from product information and customer feedback to sales figures and stock numbers. When collected and analyzed together, this information is a goldmine.

The thing about DDDM is that it enables businesses of any size and across any industry to view and measure the effectiveness of any given strategy and make well-informed decisions based on what they find. This means that teams in every department, such as sales, operations, and logistics can put a strategy in place and accurately measure its effectiveness. If an organization wants to improve business processes, increase sales or reduce costs, data provides evidential proof that the organization needs to inform their next steps. For example, if a new strategy is put in place to increase sales, the business can easily see if that strategy is on track, if it is indeed helping to increase sales, or if it’s not on track, where the areas of improvements lie. This is all made possible with the power of data.

Aside from enabling enhanced strategies, data-driven decision-making has many benefits associated with facilitating business growth. It provides organizations with the capabilities required to generate real-time insights and predictions such as gathering seasonal customer feedback on a particular product or service. This information can then be leveraged to enhance the product for the next season. This not only optimizes business performance but also ensures you stand out from the competition.

Effective DDDM also results in greater visibility of data, as teams share information between departments, it becomes more valuable and can fuel more and more business decisions. Alongside, improved visibility results in greater overall alignment of overarching business objectives – the more eyes that see data, the more it’s understood and ultimately, the more people buy into it.

This article will look in depth at why data-driven decisions are the key to unlocking business growth. It will specifically focus on the role of data, how to gather and analyze that data, and the benefits of implementing DDDM.

The role of data in business decision-making

While we now know that data plays a critical role in a modern business’ decision-making process, there are still many people that rely on gut instinct over quantitative/ qualitative data. Now, it’s not always referred to as gut instinct. More often than not, it’s shrouded with terms like ‘vision’ or business’ intuition.’ Whatever it’s called, the thing to remember is that there’s just nothing quite like a solid fact. Yes, gut instinct has been successful for many businesses in the past but sticking solely with this approach in today’s day and age can have some significant side effects.

Lack of evidence is the most common pitfall with gut instinct. If you have nothing to back up your feeling or decision, and you do indeed ‘go with your gut’ it can negatively impact your business. Everything from hurting the bottom line and sales to reduced customer satisfaction and creating undesirable products and services can be a result of bad decision-making based on gut. And while the phrase ‘don’t trust your gut’ swims to the front of many people’s minds, it isn’t all bad news. The key is to strike a balance between gut instinct and data. Businesses need both to encourage successful decision-making, for example, data that supports an instinct.

With the golden balance in place, data holds many desirable outcomes. It can successfully inform strategic planning and business objectives. By analyzing data, businesses can easily identify areas of improvement and make informed decisions on how to better allocate time, money, and resources. For example, a retail store may use data to understand which products are selling the most at a certain time of year. They can then decide to stock more of those products in-store at that particular time. Likewise, data can also provide insights into customer service, enabling you to create enhanced products through increased collection of customer feedback. A software company, for example, may use data to understand which features customers use the most and which ones they don't, and then prioritize development on the most popular features.

While data facilitates better decision-making, it also protects the business from making ill-calculated decisions. Validating a course of action by using supporting quality data allows businesses to be more confident in the decisions they make. For example, a manufacturing company using data to understand which production methods are most efficient can make positive decisions on how best to optimize production, rather than sinking resources into inefficient methods.

Many household names already use data to aid successful decision-making. Take Amazon’s anticipated delivery model, for example, which is based on previous purchase data. The model analyzes data on what products customers have purchased in the past so that Amazon can anticipate what products customers will purchase in the future and stock more of those products in anticipation. Another great example is Netflix’s watch model. Netflix analyzes data on what shows and movies viewers have watched in the past, so they can recommend new content that is likely to be of interest to those customers.

These are just a few examples of how data can better inform successful business decision-making and drive business growth. Now, let’s look at how businesses actually go about collecting that data and putting it to good use.

Gathering and analyzing data for business decision-making

Before data can be valuable, it must be gathered, analyzed, and ultimately, understood.

There are many data types that can be collected, from market data, customer data, financial data, and sales data, to internal and external data, and structured and unstructured data. However, before we get into all that, let’s look at the different types of data. At its core, data can be broken down into two categories:

  • Quantitative data

    Data that measures something and is expressed with numerical values, such as sales figures, product volumes, and the number of members in a team

  • Qualitative data

    Data that is language-based or descriptive i.e. can be interpreted, such as customer feedback, product descriptions, and meeting notes.

Both types of data are valuable, but they are used for different purposes. Qualitative data can be used to understand customer sentiment and make positive changes based on that data, while quantitative data can be used to track performance over time.

Now, within the world of data teams and business intelligence, there are actually four types of data:

  • Nominal: non-numerical data (qualitative) that can’t be compared or contrasted with other data within its own category. Your name is a good example of nominal data, it can’t be compared to someone else's name, nor does it hold any quantitative data

  • Ordinal: somewhere between qualitative and quantitative, this data is part of a natural sequence where numerical data is present within that order, such as within a scale, rank, or grade. A customer satisfaction scale (1-10) is a good example of ordinal data

  • Discrete: quantitative data that can only be presented in whole numbers and not divided or split into decimal points. For example, the number of products a salesperson sells cannot be split into a decimal, nor can the number of people present in a restaurant

  • Continuous: the opposite of discrete data, it measures numbers that can be broken down into fractions and decimal points. Measurements such as height, weight, speed, and temperature are all examples of continuous data

Aside from the data type itself, organizations also need to consider the variety of tools and techniques available to collect and analyze data. Some common data-gathering techniques include surveys and focus groups, which are great for collecting qualitative data such as customer feedback, which is needed to enhance products. A/B testing and business intelligence or data analytics software tools can then be implemented to analyze that data – i.e. what changes customers are responding well to. These types of tools help businesses to identify patterns, trends, and opportunities in their data to make improvements.

However, businesses shouldn’t expect results with a simple plug-and-play solution. First, they must ensure data is accurate and relevant for its intended decision-making purposes. This means verifying the data, cleaning it, and ensuring it is up-to-date. Organizations must consider storage too, data must be stored securely, as breaches can significantly damage a company’s reputation and lead to serious financial losses.

For many, that all sounds like a little too much work. That’s where we come in. A company like exMon provides businesses with everything they need to efficiently gather and analyze data to successfully aid the decision-making process. ExMon offers a variety of solutions to address a whole host of business requirements, from data governance and management to the quality of data.

Gathering and analyzing data can be a complex process, but with the right tools, techniques, and solutions in place, businesses can use them to make better decisions.

Implementing data-driven decisions in your business

Once you have your data in place, it’s then time to actually make data-driven decisions. To do this effectively, it’s essential to build a data-driven culture. Without one, the business just won’t have the buy-in from the whole team to put things in motion, and then continue that trajectory. Failing to rouse a data-driven culture will lead to unstructured, un-gathered, and ultimately useless data. The result of which will heavily impact your employee’s efficiency and productivity.

Businesses should consider a few key things to successfully drive a data culture.

  • 1

    Create a roadmap. Create awareness around why data is important and the benefits of data-driven decisions, and look at who would be best suited to support a data-driven culture. Take everyone through the plan and get them involved so they feel a part of the process

  • 2

    Define goals and rules. What data goals are you looking to support with a data-driven culture? What do you need to get there?

  • 3

    Provide training. Data literacy is a concern for many organizations, hiring outside help or training can help to ensure that everyone knows what they are doing. Alternatively, implementing a user-friendly tool or platform can also help

  • 4

    Instill accountability. Ownership of data should be assigned depending on which department/ person the data is collected from in the first place. This person will then manage that data moving forward and be accountable for its accuracy

The role of leadership in driving data-driven decision-making is also critical. Someone needs to implement and oversee all of the above processes to build trust in the data. This is often led by the data/ BI team or even a specific role related to data governance, i.e. a data governance manager, Master Data management lead, and/or a team of data stewards – someone who is going to lead the whole data-driven culture and continue to push it forward. The key is to not offload all data responsibilities onto this person, they should only be acting as an overseer.

Once leadership has been appointed, it’s time to look at some best practices for using data to inform business decisions. Regularly reviewing and updating data as needed to ensure it is always accurate and relevant should be at the top of the list and should involve all the relevant stakeholders. Another important aspect to consider is data governance, which is the process of overseeing and managing data assets. Encompassing data quality, security, and compliance, a data governance framework outlines the policies, procedures, and standards that govern the management of data. It’s often seen as an afterthought for many organizations, but without it, businesses can exhibit poor data quality, incorrect resource allocations, inefficient operations, lost revenue, and as seriously, leave the organization open to falling foul of compliance requirements like e.g., international and local laws and regulations.

However, it’s not always just about the overarching business. It's also important to keep in mind the role of the end-users when implementing data-driven business culture. Data should be easily accessible, understandable, and actionable for them. Providing regular training and support to end-users on how to use the data, and adopting a user-friendly interface for accessing data are just two ways to keep employees happy. For example, a manufacturing company may use data to understand which production methods are most efficient, and then measure the impact of that decision on the company's bottom line.

The benefits of data-driven decision-making for business growth

Once successfully implemented, organizations can expect to experience some of the many benefits that data-driven decision-making has to offer. While some of these advantages may appear within a matter of weeks, others are more steadfast and become apparent over longer periods of time.

  • Increased efficiency and productivity – this is one of the major benefits of DDDM, and it’s often one of the first to be noticed. Say goodbye to manual intervention and human errors, instead, employees can focus on providing business value. How? Data has helped to improve processes by reducing wasted time and costs, leading to better resource allocation.
  • Improved customer satisfaction and loyalty – through analyzing customer data, businesses can understand what customers want from their products and services, meaning repeat purchases and increased word-of-mouth referrals. According to a study by Deloitte, one-third of industry professionals highlight that the right technologies for data collection and analysis are essential for a better understanding of customers.
  • Enhanced competitiveness and market positioning – improving data management means that businesses can always stay one step ahead of the curve, and jump on new trends to provide customers with the solution to a problem they didn’t even know they had yet. In turn, this leads to increased market share and better market positioning, which encourages higher revenue.

Being a data-driven organization will have a hugely positive impact on a business’s ability to grow and scale, but don’t just take our word for it….



  • Data-driven organizations are 23 times more likely to acquire the right customers
  • Businesses using big data experienced a profit increase of 8–10% and a 10% reduction in overall costs
  • IBM research found that 62% of retailers that leverage data and analytics exhibit a competitive advantage over others in their field


In today’s digital world, the question of ‘why data is important to business?’ is a common one.

On a global scale, many organizations are slowly realizing the benefits of leveraging data to better inform business decisions. With the right approach, people on board, and tools, any business can make data work for them to encourage and support greater business growth.

If your organization is looking to make better use of its data, then reach out to Exmon to learn how you can build a strong data culture by enabling collaboration, increasing transparency of data flows, and catching bad data before it impacts business decisions.

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Daniel ThyrringChief Commercial Officer, Exmon Software

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