How to Make Accurate Data-Driven Decisions Online

What Is Data-Driven Decision Making?

This is a strategic approach that uses data as a foundation for making informed business decisions. It is also known as DDDM (Data-Driven Decision Making). The approach involves collecting data based on measurable goals and/or KPIs. That data is then analyzed to identify patterns and extract actionable insights.

Companies then use this data to develop business strategies, making decisions grounded in data to achieve objectives rather than relying on intuition.

What Types of Data Analytics Are Used for Decision Making?

There are four types of data analytics that can be used for business decision making.

Descriptive. Involves using raw data to describe a specific/current situation. For example, monthly sales or conversion rates over a specific period, or customer demographic analysis. This type uses methods like data mining and visualization.
Diagnostic. Aims to find out “why” by identifying patterns and analyzing data to understand why something that was discovered in the previous phase occurs. In BI dashboards, this technique is used to understand the root cause of organizational issues.
Predictive. Analyzes past and present data to forecast future events. This allows companies to predict future sales, revenue, and market changes. For this type, data analysts use data modeling and machine learning.
Prescriptive. This technique uses the results of the previous three types of analysis to create value by determining a possible solution to a problem. For example, prescriptive analytics is what a mobile GPS app uses to suggest the optimal route to a destination.

What Are the Benefits of a Data-Driven Approach to Decision Making?

Data-driven decision making means you rely on accurate information rather than your gut feeling. In terms of benefits, perhaps the most notable is risk reduction. When you make decisions based on objective data, you determine the risk and reward of each decision yourself.

Let’s look at an example. Imagine you are launching a new product and planning a marketing campaign. Instead of basing your strategy entirely on current market research, you can gather data and see what worked in previous launches of similar products. This allows for smarter conclusions in a shorter time frame.

According to research, using big data analytics for decision making offers several advantages:

More effective strategic decisions (69%)
Greater control over operational processes (54%)
Improved customer understanding (52%)
Cost reduction (47%)

How to Start Making Data-Driven Business Decisions?

It is important to have a clear action plan that outlines what data you need, how to find it, and what outcome you expect. Simply put, what do you want to measure and why?

If you don’t know where to start, here is a simple 4-step process you can follow:

First: Define Your Goals and Priorities

What problem do you want to improve? Review your business goals and priorities. Suppose you want more customers to buy a particular product that has been selling poorly. First, determine which data is key to understanding the reasons for the low sales—for example, consumer search trends, marketplace buying habits, and social media sentiment. Once you have this data, you can move to analysis, draw conclusions, and take specific actions to boost sales (e.g., target a new audience or adjust the offer based on current consumer preferences).

Second: Find and Present the Data

After you have defined the problem you want to solve and identified relevant data points, you need to develop a plan to collect that data.

Most likely, you will need to use a combination of web scraping tools and analytics tools to extract the information you need. You will want to use data collection tools tailored to your goals—for example, a search crawler if you believe search trends offer the greatest opportunity. How you present the data also matters. Proper visualization techniques help you get insights at a glance. The wrong approach can create additional chaos and fail to reveal relevant connections.

Pro tip: Most likely, you will need to combine online data collection tools with analytics tools to extract the necessary information.

Third: Extract Insights from the Data

Once you have the data, you can use analytics tools to identify patterns, relationships, and trends. For example, if you find that certain keywords are trending with your target audience, you can incorporate them into your web content.

The results will help you formulate an action plan based on accurate and relevant data.

Fourth: Monitor, Measure, Repeat

After drawing conclusions, decide what actions to take based on the data (e.g., perhaps products with free shipping sell best, and you want to offer free shipping for certain items and see how it affects sales). But this is certainly not the end of the road. This process should be cyclical, allowing you to continuously improve your business processes. Monitor the improved strategies and repeat measurements to confirm success or failure. Then repeat the process with other business units.

This is the best way to gradually build a data-driven company.

3 Things to Keep in Mind for Data-Driven Decision Making

When working with data, it is easy to become overwhelmed or act based on predetermined biases. The problem is that such errors lead to inaccurate data and ultimately harm your strategy’s results. Here are three things to consider when working with data in your company:

Watch Out for Bias

Sometimes we see what we want to see. This is one of the main challenges of data analysis. Implementing a data management culture means data is accessible to the right people, enabling them to make more informed decisions. You can eliminate bias by cross-referencing data from different sources or collecting data using various analog and digital tools, as well as from different GEOs, if it aligns with your goals.

Collect Data from the Start

Most companies wait until they have the perfect business/product/marketing strategy before starting to collect data. This is typical for large corporations, but small businesses and startups are generally more agile. You should use this agility to take advantage of data collection from the very beginning. For example, a startup that begins its go-to-market strategy using data will be able to find the right product-market fit by conducting accurate marketing research with data (What is the audience interest? What are competitors offering? etc.).

Set Measurable and Achievable Goals

Many companies can get overly excited about the possibility of collecting large volumes of data that might lead to strategic insights and business success. However, many companies have been undone by overly ambitious projects, such as collecting all target audience profiles on social media. I recommend defining the goals you want to achieve and breaking them down into smaller parts. For example, which posts about women’s shoes have the highest engagement (likes/comments) in Paris? This makes it easier to understand where current interest lies in that GEO, and you can take immediate, concrete action (e.g., launch an ad campaign targeting your new audience).

Wrapping Up

Building a data-driven company culture has its challenges. These may come in the form of resistance from colleagues or difficulties in finding, analyzing, and maintaining a data-driven business machine. But despite these difficulties, creating a company that thrives on data is essential for knowing your audience, competitors, and the landscape in which you operate. If collecting high-quality data becomes a prerequisite for doing business in the coming year, this will give you a welcome competitive advantage in the market.