Sign in
Choose languageLanguage

Data-Driven Decision-Making: A Practical Guide

data-driven decision making
8 minute read

What Is Data-Driven Decision-Making?

Data-driven decision-making, or DDDM, is a relatively broad term that can be explained as — the use of various metrics and relevant data to steer the process of making business decisions in a way that aligns with a company's goals and objectives.  

It is a full-scale process that encompasses collecting and analyzing data (e.g., via market or customer research) and using that information to make decisions for the benefit of a particular company. 

Data-driven decision-making promotes using actual and verifiable information (i.e., data) as a means of making strategic business decisions in place of relying on guesswork to decide what the correct strategic move could or would be.

Why Is Data-Driven Decision-Making Important?

The main reason why data-driven decision-making is so crucial for modern business is that it allows companies to make decisions based on objective facts rather than inherent biases, assumptions, or the personal feelings of decision-makers.  

It’s the best way for leaders to make objective decisions while striving to stay fair and neutral. But doing that is not always easy, which is where DDDM can be of help. 

What data-driven decision-making does is that it pushes leaders to use data to achieve business goals by allowing them to make objective decisions that will ultimately benefit their company more easily.  

The most common types of decisions that leaders can make with data include: 

  • Driving sales and profits
  • Establishing rules and guidelines for management
  • Optimizing various business processes
  • Improving the overall team productivity and efficacy

5 General Steps in Data-Driven Decision-Making

Learning how to make data-driven decisions takes time and requires patience to perfect.

1. Understand Your Company’s Goals

Before you can start making data-driven designs, you first need to evaluate and understand your company's goals, vision, and objectives. By doing that, you can more easily narrow down what sort of data you want/need to collect in order to make informed decisions.

2. Identify the Relevant Data

Once you’ve settled on the broader objectives for your company, it’s time to figure out all the relevant data (i.e., data that will help you make the best decisions). 

The exact solutions and data sources you end up using will be subject to the type of data you want to collect, your industry, and your business model. 

The most common type of collected data includes: 

  • Gross profit margins — Equal to net sales minus cost of sold goods.
  • Productivity — It shows how efficient your company is at producing goods or services.  
  • Return on investment — It’s the total difference between investment and income. This metric is most commonly used to calculate whether or not a particular investment is worth the effort (in terms of time and money) as well as to track how an investment is performing.
  • Number of customers — The higher the number, the more money a business can earn.
  • Annual or monthly recurring revenue — Often utilized by businesses operating in the SaaS space, this metric shows how much money the current subscribers bring to the company on a monthly or yearly basis.
  • Human Resources Management  — Information about a company's employees (such as name, date of birth, position, department, etc.) that can be used for HR analytics and data-driven decision-making.

3. Collect the Data

You’ll have to pick out specific tools depending on what type of data you’re keen on collecting. For example, if your goal is to evaluate the payroll processes of your company, you can use time and attendance software to gather relevant data that you can use to optimize core HR processes. 

With this type of digital solution, you can gather data, such as: 

  • Arrivals and departures from work,
  • Any type of work break (lunch breaks, cigarette breaks, etc.)
  • Any type of leave (vacation days, holidays, sick leaves)
  • All paid hours, absences, and overtime for a given period. 

Bonus tip: Whatever your company’s goals and objectives are, you’ll more than likely find a digital tool that can help you achieve them.

4. Organize the Gathered Data

No matter how much data you collect, it will mean nothing if it’s not properly organized. If you’re not able to view it and see how it connects, you won’t be able to analyze it and draw conclusions to help you make your decisions (i.e., data-driven decision-making). 

There are many ways to organize data, with the most popular of them being:

Hierarchical Organization

Hierarchical organization is a type of structure where every piece of data or information, except one, is subordinate to a major entity.  

For example, let’s say you’ve collected data about attendance. That would be the major entity. The pieces of data that are contained in “attendance” will be subordinate to it and can include information, such as:

  • When employees come to work and when they leave
  • Types of breaks and how long they take
  • Paid or unpaid business trips that required time off work
  • Holidays and Vacation days
  • Sick leave 

So, in hierarchical organization, “attendance” is the main directory and all other pieces of data that pertain to it are viewed as folders or subfolders.

Time-Series Organization

In time-series organization, the data is managed and structured in chronological order (or in the order in which it originally occurred). Every piece of data is associated with a specific time period or timestamp. 

This type of data organization is useful for companies that have remote workers who all work different hours or operate on flexible work schedules.  

For example, the time-series data organization can allow decision-makers to view when their employees logged in for work.

Data Visualization

Data visualization is a method in which data is represented through charts, plots or animations. These serve as a way to more easily communicate complex relationships, patterns, trends, or insights.

Graph-Based Organization

In graph-based data organization, data is organized as nodes and edges. Nodes represent entities (types of data) and edges represent relationships between those entities (how said data is connected). 

For example, in social networks, nodes will represent people and edges will represent the way they are connected (think of friends on Facebook, followers or connections on Linkedin, and so on).

5. Perform analysis

Once the data is organized, it’s time to perform the data analysis. This is arguably the most important step, as during it, you’ll learn the information that will allow you to make data-driven decisions. 

Some popular techniques for performing data analysis are:

Cohort Analysis Technique

The Cohort analysis is a method most commonly used to evaluate user behavior (for example, users of B2C SaaS products). 

In this technique, users are put into categories based on shared characteristics (e.g., the date they joined a particular platform or started using a specific service). 

These cohorts (or groups) are then tracked along with their behavior in order to find trends and patterns.

Cluster Analysis

Cluster analysis, or clustering, is a data analysis method that’s used to group analogous data points into groups (i.e., clusters). Data points within a single cluster should be similar to each other but also different from the data points of other clusters. 

The main use of Cluster analysis is to gain information about how specific types of data are distributed within a particular dataset.

Regression Analysis

Regression analysis is used to evaluate the connection between specific variables. The main goal is to discern if there’s any association between a dependent variable (the variable you’re looking to measure) and other independent variables (any variable or factor that can influence the dependent variable). 

Then regression analysis aims to figure out how independent variables influence the dependent one (are there specific trends or patterns that the variables follow?).

Benefits of Data-Driven Decisions-Making

Make Decisions With Confidence

When your decisions are data-driven, you’ll be more sure of them. This will allow you to make important business decisions with more confidence. 

And when you are confident, the trickle-down effect can lead to better-performing team members.

Safeguard Against Biases

Data-driven decision-making is one of the best ways to safeguard against inherent biases. Even when they’re not aware of it, business leaders can express favoritism or have certain values that can affect their decision-making process. 

When decisions are made purely through the use of data, they are sure to be impartial and objective.

Streamline Processes

With data, it’s easier to identify and evaluate trends and patterns in the work preferences of team members and the company’s processes. 

This will allow you to streamline those processes and improve team performances based on objective data rather than assumptions.

As new tools for analyzing and collecting information are being developed and as they become more powerful and precise, the impact of data-driven decision-making will continue to grow. That will create a more secure future for companies in which the outcomes of business decisions can be predicted with a higher degree of accuracy.

New ideas & Best tips
How to create a productive and organized working place, where people love to perform